Draft: 25 April 2014

CAST Programming Chair Report: Nick Sahinidis CAST Executive Committee Meeting

25 April 2014 (Friday), telecon, 11 AM - noon

Fall 2013 AIChE Meeting (San Francisco)

This meeting took place November 3 - November 8, 2013 in San Franisco, CA. Meeting

Program Chairs are Ranil Wickramasinghe (University of Arkansas) and Cawas Cooper

(Codexis, Inc.). CAST sponsored/co-sponsored 64 oral sessions and 5 poster sessions there.

There were a total of 445 oral presentations and 101 poster presentations in these sessions. The

session list is attached to the Appendix A of this report.

I appreciate the efforts of all area programming chairs and participants. I thank Ajay

Lakshmanan, Michael Baldea, Yannis Androulaki, Makis Orkoulas, and Kris Villez for doing an

excellent job of developing sessions and handling the peer-review process for this conference. I

am especially thankful to Wayne Bequette who handled programming through the middle of this

past summer and guided me with all follow-up activities thereafter.

Spring 2014 AIChE Meeting (San Antonio)

This meeting is scheduled for 20 March 30 - April 3, 2014 at Hilton New Orleans - Riverside.

The only CAST activity there that I am aware of is one CAST so-sponsored session: 23A02:

Shale Oil & Gas Development - Innovative Technological Contributions.

Fall 2014 AIChE Meeting (Atlanta)

This meeting will take place November 16-21, 2014 at Atlanta Marriott Marquis and Hilton

Atlanta. The Meeting Program Chairs are Wayne Bequette and Sal Garcia, who have suggested

that session allocations will be approximately the same as in the San Francisco meeting.

Tentative CAST sessions are listed in Appendix B of this report. Paper submissions are due May

12, 2014. Special sessions for this meeting include sessions honoring Larry Evans on the

occasion of his 80th

birthday and Ignacio Grossmann on the occasion of his 65th

birthday.

The Area Chairs for 2014 are Selen Cremaschi (10a), Tyler Soderstrom (10b), John Siirola (10c),

Steven Dubljevic (10d), and Gürkan Sin (10e). I thank them for their efforts in developing

session titles and co-chairs, based on their programming committee meetings last year in

Pittsburgh. I look forward to working with them as we complete the tasks of organizing the 2014

Annual Meeting.

Spring 2015 AIChE Meeting (New Orleans)

April 26-30, 2015 in Hilton, Austin. Program chairs are Joe Powell (Shell) and Wendy Young

Reed (Chemstations). CAST has agreed to take the lead to chair a Big Data Analytics Topical. I

am thankful to Leo Chiang (Dow, 10E chair for 2016), who has agreed to chair this topical on

behalf of CAST. Leo is planning three sessions (one industrial, one academic and one from

vendors) on big data. In addition, he will coordinate several additional sessions organized by

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others at the conference. The call for papers for this meeting opens May 5th

and closes

November 14th

.

Fall 2015 AIChE Meeting (Salt Palace Convention Center, Salt Lake City UT)

This meeting is scheduled for November 8-12, 2015 in Salt Palace Convention Center, Salt Lake

City UT. The Area Chairs for 2015 are Ramkumar Karuppiah (10a), Nael El-Farra (10b),

Alexander Mitsos (10c), Jinfeng Liu (10d) and Bri-Mathias Hodge (10e). We are tentatively

going with previous years allocations.

Other CAST Sponsored Meetings

DYCOPS 2013

DYCOPS 2013 conference took place in December 2013 in Mumbai (India). The co-chairs were

Mike Henson (University of Massachusetts) and Gabriele Pannnocchia (University of Pisa).

This was the 10th

conference in this series. The conference had received 181 submissions, out of

which 136 papers were finally accepted, including 3 plenary presentations, 6 keynote

presentations, 92 oral presentations in 18 sessions, and 32 poster presentations in 2 sessions. The

topics covered included aspects of process optimization and control, control applications, process

monitoring and performance monitoring, batch process modeling and control, inferential sensing,

interactions between design and control, and process scheduling.

Information about DYCOPS 2013 is at: http://www.dycops2013.org/

FOCAPD 2014

The next FOCAPD conference is scheduled for the Summer of 2014. The co-chairs are Mario

Eden (Auburn University), John D. Siirola (Sandia National Laboratories) and Gavin P. Towler

(Honeywell/UOP). The FOCAPD proceedings were just sent to the publisher (Elsevier): it

includes 96 contributed papers along with papers from 23 of the 34 invited speakers. The strong

turnout has enabled the chairs to schedule three poster sessions (Monday through Wednesday

evenings) to provide opportunities for interaction among the presenters and conference attendees.

The conference has $40,000 from the NSF to support travel for students, post-docs and junior

faculty, and the chairs are in the process of soliciting applications (applications are due May 15).

We currently have 135 registered participants (including at least 48 graduate students). The

early registration deadline is May 1. We are on track for the conference to break even, thanks in

large part to generous industrial support totaling over $35,000, including contributions from

AspenTech, the Auburn University Samuel Ginn College of Engineering, Bryan Research &

Engineering, Eastman Chemical Company, Evonik Industries, Honeywell/UOP, and SimSci by

Schneider Electric.

Information about FOCAPD 2014 is at: http://www.focapd.org/

PSE 2018

In 2018, it is highly likely that PSE will return back to the Americas. CAST worked with

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CACHE to make a proposal to IOPSE for organizing PSE 2018 in the US. Chris Floudas

(Princeton) and Gavin P. Towler (Honeywell/UOP) agreed to serve as co-chairs and submitted a

proposal to IOPSE in January 2014. IOPSE will select the winning proposal by June 2014.

Acknowledgments

I want to thank all CAST Area chairs and Executive Committee members for your assistance.

CAST’s continued programming successful outcomes are entirely due to your efforts. I look

forward to working with all of you in the coming years. I want to especially thank Wayne

Bequette, who carried out all programming efforts through the middle of summer 2013 and has

been a tremendous source of guidance for me during the past year.

Nick Sahinidis, CAST Programming Chair

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Appendix A. Fall 2013 CAST Division Program

Sunday, November 3, 2013

1:15 PM-3:45 PM

(#2) - In Honor of John MacGregor's 70th Birthday I Sponsored by: Pharmaceutical Discovery, Development and Manufacturing Forum Co-Sponsored by: Systems and Process Design (10A)

4:00 PM-6:30 PM

(#14) - In Honor of John MacGregor's 70th Birthday II Sponsored by: Pharmaceutical Discovery, Development and Manufacturing Forum Co-Sponsored by: Systems and Process Design (10A)

Monday, November 4, 2013

8:30 AM-11:00 AM

(#46) - CAST Division Plenary Sponsored by: Computing Systems and Technology Division

12:30 PM-3:00 PM

(#105) - Area Plenary: Future Directions in Applied Mathematics and Numerical Analysis Sponsored by: Applied Mathematics and Numerical Analysis

(#137) - Networked, Decentralized and Distributed Control Sponsored by: Systems and Process Control Co-Sponsored by: Applied Mathematics and Numerical Analysis (10D)

(#139) - Optimization and Predictive Control Sponsored by: Systems and Process Control Co-Sponsored by: Computers in Operations and Information Processing (10C)

3:15 PM-5:45 PM

(#200) - Poster Session: Applied Mathematics and Numerical Analysis Sponsored by: Applied Mathematics and Numerical Analysis

(#201) - Poster Session in Information Management and Intelligent Systems Sponsored by: Information Technology

(#202) - Poster Session: Systems and Process Control

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Sponsored by: Systems and Process Control

(#203) - Poster Session: Systems and Process Design Sponsored by: Systems and Process Design

(#204) - Poster Session: Systems and Process Operations Sponsored by: Computers in Operations and Information Processing

Tuesday, November 5, 2013

8:30 AM-11:00 AM

(#231) - Advances in Process Control Sponsored by: Systems and Process Control

(#240) - Control of Large-Scale and Networked Systems Sponsored by: Systems and Process Control

(#251) - In Honor of Rex Reklaitis' 70th Birthday Sponsored by: Computers in Operations and Information Processing

(#253) - Ionic Liquids: Thermodynamics and Properties I Sponsored by: Innovations of Green Process Engineering for Sustainable Energy and Environment Co-Sponsored by: Thermodynamics and Transport Properties (01a), Systems and Process Design (10A)

(#254) - Mathematical Approaches for Systems Biology Sponsored by: Engineering Fundamentals in Life Science Co-Sponsored by: Biomedical Applications of Chemical Engineering (T7), Systems and Process Control (10B)

(#268) - Process Monitoring, Detection and Diagnosis Sponsored by: Computers in Operations and Information Processing

12:30 PM-3:00 PM

(#287) - Advances in Intelligent Systems (Invited Talks) Sponsored by: Information Technology

(#288) - Advances in Optimization I Sponsored by: Computers in Operations and Information Processing

(#295) - Complex and Networked Chemical and Biochemical Systems I Sponsored by: Applied Mathematics and Numerical Analysis

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(#311) - Ionic Liquids: Thermodynamics and Properties II Sponsored by: Innovations of Green Process Engineering for Sustainable Energy and Environment Co-Sponsored by: Thermodynamics and Transport Properties (01a), Systems and Process Design (10A)

(#325) - Process Monitoring and Fault Detection I Sponsored by: Systems and Process Control

3:15 PM-5:45 PM

(#340) - Advances in Optimization II Sponsored by: Computers in Operations and Information Processing

(#351) - Complex and Networked Chemical and Biochemical Systems II Sponsored by: Applied Mathematics and Numerical Analysis

(#367) - Ionic Liquids: Thermodynamics and Properties III Sponsored by: Innovations of Green Process Engineering for Sustainable Energy and Environment Co-Sponsored by: Thermodynamics and Transport Properties (01a), Systems and Process Design (10A)

(#370) - Modeling and Control of Polymer Processes: A Tribute to John P. Congalidis I Sponsored by: Systems and Process Control

(#383) - Process Monitoring and Fault Detection II Sponsored by: Systems and Process Control Co-Sponsored by: Computers in Operations and Information Processing (10C), Information Technology (10E)

Wednesday, November 6, 2013

8:30 AM-11:00 AM

(#406) - Bio and Pharmaceutical Process Design Sponsored by: Pharmaceutical Discovery, Development and Manufacturing Forum Co-Sponsored by: Systems and Process Design (10A)

(#419) - Designing Products and Molecules Sponsored by: Systems and Process Design Co-Sponsored by: Product Design (12g)

(#426) - Industrial Applications in Design and Operations Sponsored by: Computers in Operations and Information Processing

(#432) - Modeling and Control of Polymer Processes: A Tribute to John P. Congalidis II

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Sponsored by: Systems and Process Control

(#441) - Perspectives on Information Management and Intelligent Systems Sponsored by: Information Technology

(#443) - Process Control Applications Sponsored by: Systems and Process Control

(#444) - Process Design: Innovation for Sustainability Sponsored by: General Co-Sponsored by: 3rd Annual World Congress on Sustainable Engineering (TB), Innovations of Green Process Engineering for Sustainable Energy and Environment (TG), Alternative Energy and Fuel Cells (07F), Process Development (09D), Sustainability (09g), Systems and Process Design (10A), Process Development Division (12), Process Research and Innovation (12a), Sustainable Biorefineries (23B), Sustainable Energy (23C)

(#445) - Process Intensification by Process Integration Sponsored by: Process Intensification & Microprocess Engineering Co-Sponsored by: 3rd Annual World Congress on Sustainable Engineering (TB), Innovations of Green Process Engineering for Sustainable Energy and Environment (TG), Systems and Process Design (10A)

(#448) - Quantitative Approaches to Disease Mechanisms & Therapies Sponsored by: Engineering Fundamentals in Life Science Co-Sponsored by: Systems and Process Control (10B)

12:30 PM-3:00 PM

(#483) - Energy Systems Design I Sponsored by: Systems and Process Design Co-Sponsored by: Computers in Operations and Information Processing (10C)

(#492) - Manufacturing Sustainability Sponsored by: General Co-Sponsored by: 3rd Annual World Congress on Sustainable Engineering (TB), Computers in Operations and Information Processing (10C), Computing Systems and Technology Division (10), Systems and Process Control (10B)

(#493) - Modeling and Control of Energy Systems I Sponsored by: Systems and Process Control

(#494) - Modeling and Control of Polymer Processes: A Tribute to John P. Congalidis III Sponsored by: Systems and Process Control

(#497) - Multiscale Modeling: Methods and Applications Sponsored by: Applied Mathematics and Numerical Analysis

8

(#507) - Process Design for Process Intensification Sponsored by: Process Intensification & Microprocess Engineering Co-Sponsored by: Systems and Process Design (10A)

(#509) - Quantitative Approaches to Cancer Mechanisms & Therapies Sponsored by: Engineering Fundamentals in Life Science Co-Sponsored by: Biomedical Applications of Chemical Engineering (T7), Systems and Process Control (10B)

(#518) - Supply Chain Optimization I Sponsored by: Computers in Operations and Information Processing

3:15 PM-5:45 PM

(#523) - Advances in Data Analysis: Theory and Applications Sponsored by: Information Technology

(#538) - Energy Systems Design II Sponsored by: Systems and Process Design Co-Sponsored by: Computers in Operations and Information Processing (10C)

(#554) - Modeling and Computation in Energy and the Environment Sponsored by: Applied Mathematics and Numerical Analysis

(#555) - Modeling and Control of Energy Systems II Sponsored by: Systems and Process Control

(#564) - Process Design: Indicators for Sustainability Sponsored by: General Co-Sponsored by: International Congress on Energy (ICE) 2013 (T4H), 3rd Annual World Congress on Sustainable Engineering (TB), Innovations of Green Process Engineering for Sustainable Energy and Environment (TG), Sustainability (09g), Systems and Process Design (10A)

(#573) - Supply Chain Optimization II Sponsored by: Computers in Operations and Information Processing

Thursday, November 7, 2013

8:30 AM-11:00 AM

(#589) - Advances in Computational Methods and Numerical Analysis I Sponsored by: Applied Mathematics and Numerical Analysis

(#607) - Design and Operation under Uncertainty I

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Sponsored by: Systems and Process Design Co-Sponsored by: Computers in Operations and Information Processing (10C)

(#612) - Dynamic Simulation and Optimization Sponsored by: Computers in Operations and Information Processing

(#613) - Dynamics, Reduction and Control of Distributed Parameter Systems Sponsored by: Applied Mathematics and Numerical Analysis

(#628) - Modeling and Control of Sustainable Processes Sponsored by: Systems and Process Control

(#636) - Numerical Methods in Synthetic and Systems Biology Sponsored by: Applied Mathematics and Numerical Analysis Co-Sponsored by: Bioengineering (15c)

(#644) - Synthesis and Design for Water Systems Sponsored by: Systems and Process Design

12:30 PM-3:00 PM

(#651) - Advances in Computational Methods and Numerical Analysis II Sponsored by: Applied Mathematics and Numerical Analysis

(#666) - Computational Approaches in Biomedical Engineering Sponsored by: Applied Mathematics and Numerical Analysis Co-Sponsored by: Engineering Fundamentals in Life Science (15d)

(#669) - Design and Operation Under Uncertainty II Sponsored by: Systems and Process Design Co-Sponsored by: Computers in Operations and Information Processing (10C)

(#696) - Planning and Scheduling I Sponsored by: Computers in Operations and Information Processing

(#698) - Process Design I Sponsored by: Systems and Process Design

(#699) - Process Modeling and Identification I Sponsored by: Systems and Process Control

3:15 PM-5:45 PM

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(#710) - Advances in Information Management and Integration Sponsored by: Information Technology

(#714) - Big Data Applications in Chemical Engineering Sponsored by: Information Technology

(#747) - Molecular and Mesoscopic Systems: Methods and Applications Sponsored by: Applied Mathematics and Numerical Analysis Co-Sponsored by: Computational Molecular Science and Engineering Forum (21)

(#755) - Planning and Scheduling II Sponsored by: Computers in Operations and Information Processing

(#756) - Process Design II Sponsored by: Systems and Process Design

(#757) - Process Modeling and Identification II Sponsored by: Systems and Process Control

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Appendix B. Fall 2014 CAST Division Program

Session Listing Computing and Systems Technology Division

this page now OR Download this information to Word

26897: Advances in Computational Methods and Numerical Analysis I Sub Title: This session invites contributions in all areas of applications of computational science in Chemical Engineering, including theory, efficient and/or novel algorithms, supercomputing applications, numerical methods. Type: Oral keywords: Algorithms & Numerical Methods, Computational Methods and Supercomputing Chemical Engineering Applications Abstract id# 356846 Ffffffffffff Chair Roger P. Pawlowski Senior Member of Technical Staff: Sandia National Laboratories PO Box 5800-0316 Computational Sciences Albuquerque, NM 87114 Email: rppawlo@sandia.gov Co-Chair G. Orkoulas University of California at Los Angeles 5531 Boelter Hall 420 Westwood Plaza Chemical and Biomolecular Engineering Los Angeles, CA 90095 Fax Number: (310) 206-4107 Email: makis@seas.ucla.edu -- Will not be published

26898: Advances in Data Analysis: Theory and Applications Sub Title: The amount of data made available by the continuing advances in experimental measurement techniques has created both opportunities and major challenges towards

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efficient computational processing of the data sets. Extracting useful information (i.e., knowledge) from these large data sets requires an integral approach that couples the design of experiments or measurement techniques with the algorithms or applications required for the data processing and analysis. New tools for representing, extracting, and using the knowledge available in data sets are expected to emerge to perform tasks in data-rich, analysis-rich situations rather than being data-poor, analysis-rich, which has been the traditional scenario until recently. Contributions are sought that propose new modeling approaches and computational algorithms for addressing issues related to data analysis, management and data-based decision making. Suggested topics include, but are not limited to: * Model based experimental design and use of new data to refine models * Analysis of complex systems including material and biological * Use of data analysis for design, optimization, and control * Interplay of first principles and data driven models in knowledge extraction. The following topics are also of interest to this session: * Distributed decision making in organizations * Decentralized information processing * Financial modeling & investment planning * Information management across the WWW. Applications with industrial relevance are strongly encouraged. Type: Oral keywords: Computational Methods, Plant Operations and Process Design Chair Heinz A. Preisig Professor: Norwegian University of Science and Technology Chemical Engineering Trondheim, 7491 Norway Email: Heinz.Preisig@chemeng.ntnu.no -- Will not be published Co-Chair Mano R. Maurya Assistant Project Scientist: Department of Bioengineering, University of California San Diego 9500 Gilman Drive, MC 0412 La Jolla, CA 92093-0412 Email: mano@sdsc.edu -- Will not be published * Membership Number 0090104005 Biographical Sketch: I completed my B.Tech. in Chemical Engineering from IIT Bombay in 1998, M.E. in Chemical Engineering in 1999 from City College of New York and Ph.D. in Chemical Engineering in 2003 from Purdue University. Since then I was a postdoctoral researcher for three years in the Bioengineering Department at University of California, San Diego (UCSD). Currently I am an Assistant Scientist at UCSD. My Ph.D. research was on fault diagnosis in complex chemical plants and postdoctoral research is focused on applications of process systems engineering for studying complex biochemical pathways. I have published about 30 papers in peer-reviewed journals and conference proceedings. In 2005, I received the Best Paper Award for my paper published in the journal of Engineering Applications of Artificial Intelligence in 2004. My current research interests include applications of systems engineering approaches to Chemical Engineering and Biomedical Sciences.

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26899: Advances in Information Management and Integration Sub Title: This session solicits submissions describing methods, applications, and challenges in data management, information management, knowledge management, and integrating data from disparate systems for process design, process operations, and other applications. Examples of such efforts include ontologies and other formal representations, cheminformatics tools and applications, semantic web applications, sharing operations data across groups in industry, and others. Type: Oral keywords: Computational Methods, Information management and integration and Information technology Abstract id# 363444 The Ontology System for Easy and Reusable Model Knowledge Representation Marina Fedorova, Gürkan Sin and Rafiqul Gani, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark Abstract Text:

The development and application of process models are usually not trivial due to the complexity of the system being studied and/or scales being considered. Therefore, it is useful to store the modelling results in some knowledge base with the possibility to update, access and extract information about the model, in order not to repeat the same model development steps, and also to reuse or improve existing know-how. Hence, there is a need for a simple but efficient system for knowledge representation and management of models and their connections to other tools for different model-based applications.

This work presents design and implementation of an ontology for knowledge representation related to computer-aided modelling. This ontology is built on the basis of the modelling language, which includes classes, layers, and blocks (these classification names are related to the graphical representation of the model in the modelling-tool software). Class is a pattern with specific parameters or characteristics that is used as template for creation of modelling objects. These objects are then called the instances of the class. Classes interrelate between each other; upper layers represent classes and contain sub-layers; sub-layers contain blocks. The collection of instances of the classes represents the model in the system.

The structure of the knowledge ontology is based on a model decomposition technique [1]. Every model

representation includes four upper layers, which are System information, Balance equations, Constitutive

equations and Connection equations. Every process model could be decomposed to the equations and

relations, which can be linked to one of the upper layers. Each upper layer includes a number of sub-layers, representing specific phenomena occurring in the system. In the upper layer there are predefined sub-layers (giving the basic and essential description of the system) as well as user-defined sub- layers, which might be unique for the specific case. The predefined sub-layers are, for example, number of

balance-volumes, number of phases, presence of the reaction etc., given in the System information upper

layer; similarly, mass balance, energy balance, momentum balance equations belong to the Balance

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equations upper layer; kinetic, thermodynamic, diffusion models belong to the Constitutive

equations upper layer; relations between volumes inside the system and between system and outside

world, closure equations, initial conditions belong to the Connection equations upper layer. Each sub-

layer contains one or more blocks, representing various options or scenarios, which are possible in relation to the phenomena, linked to the corresponding sub-layer. Model equations are included in the corresponding blocks and this gives the model developer the possibility to quickly generate-create the required type of model by choosing one of the options available in the sub-layers. Also, the system allows updating of existing models, therefore, storing knowledge for further reuse.

On top of simple representation of the model structure this system is linked to a tool for model solution and analysis. Also, a model transfer via XML option for the created or existing model is available. Therefore, any tool, which accepts this type of XML file as an input, could be linked to the modelling tool-box.

This modelling ontology can be applied to a wide range of modelling applications in chemical engineering, as it is not associated to any specific modelling tools. The software implementation of the ontology makes it also easy to use even for the model developer with limited knowledge about modelling. The ontology also represents the knowledge in a way that allows model reuse and transfer to other software applications.

[1] I. Cameron, R. Gani, 2011, Product and Process Modelling. A Case Study Approach., Elsivier.

Chair Antonis C. Kokossis Professor, CEng, FIChemE, FRSA, FIET: National Technical University of Athens Zografou Campus, 9, Iroon Polytechniou Str. GR-15780 School of Chemical Engineering Athens, 15780 Greece Fax Number: 00-30-2-10-772- 3155 Email: akokossis@mail.ntua.gr Co-Chair Arun V. Giridhar Purdue University 480 Stadium Mall Drive School of Chemical Engineering West Lafayette, IN 47907 Email: agiridha@ecn.purdue.edu -- Will not be published

26900: Advances in Optimization Sub Title: Optimization methods algorithms form the core tools for process operations. The session invites papers in the general area of Process Systems Optimization. Topics on theory, algorithms and software for all classes of optimization problems are considered.

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Application papers offering insights into the interplay between optimization theory and practice are also encouraged. Type: Oral keywords: Computational Methods, Optimization and Plant Operations Abstract id# 360529 On the Implementation of a Structured-Exploiting Interior-Point Solver for Structured Nonlinear Programs Victor M. Zavala, Mathematics and Computer Science, Argonne National Laboratory, Argonne, IL Abstract Text:

We describe the implementation of a fillter line-search interior point solver capable of exploiting multiple embedded structures at the linear algebra level (PIPS-NLP). We place special emphasis on the issue of inertia detection because we argue that this prevents the use of modular linear algebra implementations and thus hinders scalability. We prove that a simple test for curvature along the computed direction is sucient to guarantee global convergence. In addition, using CUTEr and energy optimization problems, we demonstrate that the strategy is as eective as inertia detection obtained through symmetric indenite factorizations in converging to second order points. We provide scalability results for our linear algebra implementation using stochastic optimal control problems of natural gas networks in which we exploit stochastic and reduced space structure.

Abstract id# 360261 Process Simulation-Based Optimization of a Commercial Amine Gas Sweetening Unit Abdallah S. Berrouk and Satyadileep Dara, Chemical Engineering, Petroleum Institute, Abu Dhabi, United Arab Emirates Abstract Text:

Amine sweetening units are integral parts of any natural gas plant with the objective of recovering mainly

H2S and CO2 from gas streams before the latter are transported or used to produce LNG. Safety,

environmental, operational and economic considerations govern the essence of removing these acid

gases: H2S is toxic and corrosive while CO2 reduces the heating value besides being corrosive. In most

cases, sales gas revenues far outweigh the capital and operating costs of these gas plants. Nevertheless, Emphasis in recent years has been on increasing gas plants efficiency due to exploration of sour gas

fields with high H2S and CO2 concentrations which pose challenge to their profitability. Indeed, Abu Dhabi

has significant developed and undeveloped sour gas reserves with high H2S and CO2 content (5% to

30%). This indicates the necessity of investigating several optimization schemes in order to maintain the economics of Abu Dhabi different gas plants.

This paper discusses the prominence of optimum operating conditions in amine gas units to best improve Abu Dhabi gas plants’ efficiency without additional capital expenditure. Effect of lean amine temperature and amine strength on plant performance in several aspects is explained through sensitivity analysis. To

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this end, a kinetics based process simulation model has been developed for a commercial gas sweetening unit of a gas plant located in Abu Dhabi. Trends of various process variables such as sweet

gas H2S, CO2 & H2O, steam consumption rate and solvent circulation rate as function of the two

abovementioned parameters are explained in detail. It is demonstrated through this study that lower

amine temperature (47 OC) provides significant reduction in solvent circulation rate, steam consumption

rate, pumping duty, dehydration unit load while posing no risk of hydrocarbon condensation and hydrate formation. Also, it is found that amine strength of 50 % is best suited for amine-sweetening unit’s operating conditions as it resulted in potential savings in operating costs without increasing the risk of corrosion and fouling. To provide guidelines for optimum plant operations, DCS (Distributed Control System) lookup tables for the commercial amine-sweetening unit have also been generated and successfully tested.

Abstract id# 362673 Tightening Piecewise Mccormick Relaxations through Partition-Dependent Bounds for Non-Partitioned Variables Pedro M. Castro, Centro de Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal Abstract Text:

The simplest and most common type of nonlinear constraint in Chemical Engineering arises when mixing multicomponent streams of different properties. Blending constraints appear in crude oil operations in refineries (Lee et al. 1996, Jia et al. 2003), in the blending of liquid fuels (Jia and Ierapetritou 2003, Kolodziej et al. 2013b), in the design of distributed wastewater treatment systems (Galan and Grossmann 1998, Meyer and Floudas 2006, Teles et al. 2012) and integrated water networks (Karuppiah and Grossmann 2006, Faria and Bagajewicz 2012, Rubio-Castro et al. 2013). Bilinear terms also arise in the trim loss problem in paper plants (Harjunkoski et al. 1998, Zorn and Sahinidis 2013). Bilinear terms are nonconvex and therefore give rise to a variety of local solutions that may be far from the global optimum.

In order to find rigorous global optimal solutions to bilinear problems, alternative algorithms can be used that have in common the generation of linear or mixed-integer linear relaxations of the original problem. A well-known example of an MILP relaxation is the piecewise McCormick envelopes of Grossmann and co-workers (Bergamini et al. 2005, Karuppiah and Grossmann 2006). While piecewise McCormick relaxation can prove global optimality for a typically large number of partitions, the resulting MILP problem quickly becomes intractable due to the linear growth of binary variables with the number of partitions (Kolodziej et al. 2013a). A tighter formulation using the same number of binary variables is thus needed.

Piecewise McCormick relaxation works by diving the domain of one of the variables in each bilinear term into a given number of partitions. A tighter relaxation, compared to the standard linear McCormick (1976) envelopes, results from considering partition-dependent lower and upper bounds for the partitioned variables. We now propose to use partition-dependent bounds also for the non-partitioned variables so as to further improve the quality of the relaxation. These can be determined through optimality-based bound contraction (see for instance Castro and Grossmann 2014), which involves solving two linear problems per partition and per variable. While hundreds or even thousands of linear bound contracting problems may be involved, the benefit from a tighter formulation more than compensates the additional computational time. Once the solution of the tightened relaxation is found, it is used as a starting point for solving the original nonlinear problem with a fast local solver, so as to find a near optimal solution and to compute a rigorous optimality gap.

The performance of the new algorithm is illustrated with a set of small test problems and larger water-using network design problems from the literature. For the latter, the results show that the proposed

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algorithm outperforms state-of-the-art commercial solvers BARON (Tawarmalani and Sahinidis 2005) and GloMIQO (Misener and Floudas 2013). This is an indication that piecewise relaxation schemes and optimality-based bound contraction should be used to a greater extent.

Acknowledgments: Financial support from the Luso-American Foundation under the 2013 Portugal-U.S.

Research Networks program and from Fundação para a Ciência e Tecnologia through the Investigador FCT 2013 program.

References:

-Bergamini, M.L., Aguirre, P., Grossmann, I.E. (2005). Logic-based outer approximation for globally optimal synthesis of process networks. Computers and Chemical Engineering 29, 1914-1933.

-Castro, P.M., Grossmann, I.E. (2014). Optimality-based Bound Contraction with Multiparametric Disaggregation for the Global Optimization of Mixed-Integer Bilinear Problems. Journal of Global Optimization. Doi: 10.1007/s10898-014-0162-6.

-Faria, D.C., Bagajewicz, M.J. (2012). A New Approach for Global Optimization of a Class of MINLP Problems with Applications to Water Management and Pooling Problems. AIChE J. 58 (8) 2320-2335.

-Galan, B., Grossmann, I.E. (1998). Optimal Design of Distributed Wastewater Treatment Networks. Ind. Eng. Chem. Res. 37, 4036.

-Harjunkoski, I., Westerlund, T., Pörn, R., Skrifvars, H. (1998). Different Transformations for Solving Non-convex Trim Loss Problems by MINLP. European Journal of Operational Research, 105, 594-603.

-Jia, Z., Ierapetritou, M. (2003). Mixed-Integer Linear Programming Model for Gasoline Blending and Distribution Scheduling. Ind. Eng. Chem. Res. 42, 825-835.

-Jia, Z., Ierapetritou, M., Kelly, J.D. (2003). Refinery Short-term Scheduling Using Continuous Time Formulation: Crude-Oil Operations. Ind. Eng. Chem. Res. 42, 3085-3097.

-Karuppiah, R., Grossmann, I.E. (2006). Global optimization for the synthesis of integrated water systems in chemical processes. Computers and Chemical Engineering 30, 650-673.

-Kolodziej, S., Castro P.M., Grossmann, I.E. (2013a). Global Optimization of Bilinear Programs with a Multiparametric Disaggregation Technique. Journal of Global Optimization 57, 1039-1063.

-Kolodziej, S., Grossmann, I.E., Furman, K.C., Sawaya, N.W. (2013b). A discretization-based approach for the optimization of the multiperiod blend scheduling problem. Computers and Chemical Engineering 53, 122-142.

-Lee, H., Pinto, J.M., Grossmann, I.E., Park, S. (1996). Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Ind. Eng. Chem. Res. 35, 1630-1641.

-McCormick, G.P. (1976). Computability of global solutions to factorable nonconvex programs. Part I. Convex underestimating problems. Mathematical Programming 10, 146.

-Meyer, C.A., Floudas, C.A. (2006). Global Optimization of a Combinatorially Complex Generalized Pooling Problem. AIChE J. 52 (3), 1027.

-Misener, R., Floudas, C.A. (2013b). GloMIQO: Global mixed-integer quadratic optimizer. Journal of Global Optimization 57, 3-50.

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-Rubio-Castro, E., Ponce-Ortega, J.M., Serna-González, M., El-Halwagi, M.M., Pham, V. (2013). Global Optimization in Property-based Inter-plant Water Integration. AIChE J. 59 (3), 813-833.

-Tawarmalani, M., Sahinidis, N.V. (2005). A polyhedral branch-and-cut approach to global optimization. Mathematical Programming 103 (2), 225-249.

-Teles, J.P., Castro, P.M., Matos, H.A. (2012). Global Optimization of Water Networks Design using Multiparametric Disaggregation. Computers and Chemical Engineering 40, 132-147.

-Zorn, K., Sahinidis, N.V. (2013). Computational Experience with Applications of Bilinear Cutting Planes. Industrial & Engineering Chemistry Research, 52, 7514-7525.

Chair Victor M. Zavala Argonne Scholar: Argonne National Laboratory 9700 S Cass Ave Mathematics and Computer Science Argonne, IL 60439 Email: vzavala@mcs.anl.gov -- Will not be published Co-Chair Joseph Scott Clemson University Chemical and Biomolecular Engineering Email: jks9@clemson.edu -- Will not be published Co-Chair Benoit Chachuat Senior Lecturer: Imperial College London South Kensington Campus Centre for Process Systems Engineering, Department of Chemical Engineering London, SW7 2AZ United Kingdom Email: b.chachuat@imperial.ac.uk Co-Chair Fengqi You Assistant Professor: Northwestern University 2145 Sheridan Road Department of Chemical and Biological Engineering Evanston, IL 60208-3120 Email: you@northwestern.edu

26901: Advances in Process Control Sub Title:

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The session seeks to showcase novel theoretical developments and applications in any traditional or emerging area of process control. Type: Oral keywords: Control Theory, Emerging Technology and Process Control Abstract id# 359912 Compromise Optimization Tuning Strategy for Model Predictive Controllers Andre S. Yamashita and Darci Odloak, Chemical Engineering, University of São Paulo, São Paulo, Brazil Abstract Text:

Abstracts:

o ptp-abstract.docx (17.3KB) - Uploading Abstracts

Abstract id# 363557 Stochastic Model Predictive Control of High-Dimensional Systems: An End-to-End Continuous Pharmaceutical Manufacturing Case Study Joel Paulson1, Ali Mesbah2 and Richard D. Braatz1, (1)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (2)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA Abstract Text:

Probabilistic uncertainties are ubiquitous in complex dynamical systems and can impair closed-loop performance of model predictive control (MPC) approaches, which are widely used to realize high-performance operation of complex systems. Stochastic MPC provides an alternative approach for conventional robust MPC strategies [1], which design the optimal control law with respect to worst-case system uncertainties that can have vanishingly small probability of occurrence. Stochastic MPC approaches shape the predicted probability density functions of system states and outputs in an optimal manner over a finite prediction horizon [2,3,4,5]. Stochastic MPC also enables considering state constraints in a probabilistic sense (so-called chance constraints) to alleviate the inherent conservatism of robust MPC approaches that merely deal with deterministic worst-case perturbations. The key challenge in stochastic MPC is however the propagation of probabilistic uncertainties. The commonly used methods (e.g., Monte Carlo and Markov Chain Monte Carlo methods [6]) for probabilistic uncertainty analysis are prohibitively expensive for real-time control. Additional complexity in stochastic MPC arises from chance constraints, which entail the computation of multivariable integrals [7].

In this talk, a fast stochastic MPC algorithm with chance constraints is presented for high-dimensional stable linear systems with time-invariant probabilistic uncertainties (e.g., uncertainties in initial conditions and system parameters). Generalized polynomial chaos (PC) theory [8,9] is used to enable efficient uncertainty propagation through the high-dimensional system model. In the PC approach, the implicit mappings between the uncertain variables/parameters and system outputs are replaced with an

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expansion of orthogonal polynomials, whose statistical moments can be determined efficiently from the expansion coefficients. To determine the expansion coefficients, Galerkin projection [9] is adapted to construct PC expansions for a general class of linear differential algebraic equations (DAEs), so that the SMPC algorithm is applicable to both regular and singular/descriptor systems. The quadratic dynamic matrix control (QDMC) algorithm [10] is used to formulate an input-output framework for stochastic MPC with output constraints. The resulting probabilistic input-output framework is independent of the state dimension to circumvent the prohibitive computational costs of control of uncertain systems with a large state dimension.

The proposed probabilistic input-output MPC approach with chance constraints is used for control of an end-to-end continuous pharmaceutical manufacturing process (with approximately 8000 states) [11] in the presence of time-invariant, probabilistic parametric uncertainties. The process has nine inputs and three outputs---the active pharmaceutical ingredient (API) dosage of tablets, the impurity content of tablets, and the production rate of the process [12]. The critical quality attributes (CQAs) of the manufactured tablets consist of the API dosage and impurity content of the tablets, which should be regulated consistently in a stochastic setting. The closed-loop simulation results reveal that the stochastic MPC approach leads to a much tighter distribution (i.e., lower variance) of the API dosage around its desired setpoint. In addition, the chance constraint imposed on impurity content ensures that the tablets meet the stringent drug specifications in the presence of system uncertainties. These results indicate the promising potential of the proposed fast stochastic MPC approach for application to control of high-dimensional systems (e.g., pharmaceutical manufacturing), in which stringent requirements on robust closed-loop performance should be met.

[1] A. Bemporad and M. Morari. Robust model predictive control: A survey. In Robustness in Identification and Control (A. Garulli and A. Tesi, eds.), 207-226, Springer Berlin, 1999.

[2] A. Schwarm and M. Nikolaou. Chance-constrained model predictive control. AIChE Journal, 45:1743-1752, 1999.

[3] D. Bernardini and A. Bemporad. Scenario-based model predictive control of stochastic constrained

linear systems. In Proceedings of the 48th IEEE Conference on Decision and Control, 6333-6338,

Shanghai, 2009.

[4] G. C. Calafiore and L. Fagiano. Robust model predictive control via scenario optimization. IEEE Transactions on Automatic Control, 58:219-224, 2013.

[5] A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Stochastic nonlinear model predictive control with probabilistic constraints. In Proceedings of the American Control Conference, To Appear, Portland, 2014.

[6] M. Vidyasagar. Randomized algorithms for robust controller synthesis using statistical learning theory. Automatica, 37:1515-1528, 2000.

[7] F. Oldewurtel, D. Sturzenegger, P. M. Esfahani, G. Andersson, M. Morari, and J. Lygeros. Adaptively constrained stochastic model predictive control for closed-loop constraint satisfaction. In Proceedings of the American Control Conference, 4681-4688, Washington, 2013.

[8] N. Wiener. The homogeneous chaos. American Journal of Mathematics, 60:897-936, 1938.

[9] D. Xiu and G. E. Karniadakis. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM Journal of Scientific Computation, 24:619-644, 2002.

[10] C. E. Garcia and A. M. Morshedi. Quadratic programming solution of dynamic matrix control (QDMC). Chemical Engineering Communications, 46:73-87, 1986.

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[11] B. Benyahia, R. Lakerveld, and P. I. Barton. A plant-wide dynamic model of a continuous pharmaceutical process. Industrial Engineering & Chemistry Research, 51:15393-15421, 2012.

[12] J. A. Paulson, A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Fast stochastic model predictive control of high-dimensional systems. Submitted to IEEE Conference on Decision and Control, Los Angeles, 2014.

Chair Jinfeng Liu Assistant Professor: University of Alberta Department of Chemical and Materials Engineering Edmonton, AB T6G2V4 Canada Email: jinfeng@ualberta.ca Co-Chair Rui Huang United Technologies Research Center Email: huangr76@gmail.com -- Will not be published

26902: Area Plenary: Future Directions in Applied Mathematics and Numerical Analysis Sub Title: This plenary session consists of invited presentations that highlight key contributions and provide a perspective of future directions in applied mathematics and numerical analysis. Type: Oral keywords: Applied Mathematics, Computational Methods, Numerical analysis and Simulation Techniques Chair Stevan Dubljevic Assistant Professor: University of Alberta 9107-116 Street 7th Floor, ECERF Department of Chemical and Materials Engineering Edmonton, AB T6G 2V4 Canada Email: stevan.dubljevic@ualberta.ca -- Will not be published * AICHE Member * Division Member Co-Chair

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Jinfeng Liu Assistant Professor: University of Alberta Department of Chemical and Materials Engineering Edmonton, AB T6G2V4 Canada Email: jinfeng@ualberta.ca

26903: Infrastructure & Big Data Applications in Chemical Engineering & Science Sub Title: Chemical Engineering has entered the Big Data era in which large-scale data collection systems result in great opportunities for knowledge discovery. In this session, reports on real-life applications in this Big Data context are solicited for. Type: Oral keywords: Big data, Computational Methods and Infrastructure and processing Chair Bri-Mathias S. Hodge National Renewable Energy Laboratory 15013 Denver West Parkway MS ESIF 200 Golden, CO 80401 Email: bri-mathias.hodge@nrel.gov -- Will not be published Co-Chair Adam Fermier Janssen Pharmaceuticals, Spring House, PA Email: adam.fermier@gmail.com -- Will not be published

26904: CAST Division Plenary Sub Title: An invited sessions consisting of papers from the CAST Division, Area 10. Type: Oral Chair Nick Sahinidis John E. Swearingen Professor: Carnegie Mellon University 5000 Forbes Avenue Chemical Engineering

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Pittsburgh, PA 15213 Email: niksah@gmail.com

26905: Complex and Networked Chemical and Biochemical Systems Sub Title: In this session contributions on analysis, modelling and control of networked systems are sought. Contributions emphasizing realizations and applications of networked systems in the broad area of chemical, biochemical and biological processes and systems are encouraged to participate. Type: Oral keywords: Biological Engineering, Biological Systems and Networked Systems Chair Roman Voronov Assistant Professor: New Jersey Institute of Technology NJIT Chemical Biological and Pharmaceutical Engineering NJ Email: roman.s.voronov@njit.edu -- Will not be published Co-Chair Mark P. Styczynski Assistant Professor: Georgia Institute of Technology 311 Ferst Drive School of Chemical & Biomolecular Engineering Atlanta, GA 30332 Email: Mark.Styczynski@chbe.gatech.edu

26906: Computational Methods in Biological and Biomedical Systems Sub Title: Biological approaches in chemical engineering are increasingly characterized by systems-level treatment of biological systems and the use of synthetic systems under complex control. Issues such as network size, network cross-talk, and parameter sloppiness can cause significant problems in both numerical analyses and biological interpretation that must be addressed. This session invites contributions that apply existing numerical methods to systems and synthetic biology problems, as well as contributions that identify unique numerical problems that arise in such biological systems and address them with novel approaches. Type: Oral

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keywords: Biological Engineering, Biological Systems, Computational Methods and Synthetic Systems Abstract id# 359244 Identification and Estimation of the Influential Parameters in Bioreaction Systems Mordechai Shacham, Ben Gurion University of the Negev, Beer-Sheva, Israel and Neima Brauner, Tel-Aviv University, Tel Aviv, Israel Abstract Text:

Abstracts:

o ShachamBrauner_AIChE14.pdf (22.8KB) - Uploading Abstracts

Abstract id# 361733 Uncertainty in Clinical Data and Stochastic Model for in-Vitro Fertilization Kirti Yenkie, Bioengineering, University of Illinois, Chicago, Chicago, IL; Vishwamitra Research Institute, Chicago, IL and Urmila Diwekar, Vishwamitra Research Institute, Center for Uncertain Systems: Tools for Optimization and Management, Clarendon Hills, IL Abstract Text:

Abstracts:

o Uncertainty analysis in IVF_Yenkie, Diwekar, 2014.pdf (89.8KB) - Uploading Abstracts

Chair Roman Voronov Assistant Professor: New Jersey Institute of Technology NJIT Chemical Biological and Pharmaceutical Engineering NJ Email: roman.s.voronov@njit.edu -- Will not be published Co-Chair Ashlee N. Ford Versypt Massachusetts Institute of Technology 77 Massachusetts Avenue Department of Chemical Engineering Cambridge, MA 02139 Email: ashleefv@mit.edu -- Will not be published

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26907: Control of Large-Scale and Networked Systems Sub Title: We seek contributions involving the control of large scale systems/ system interconnections Type: Oral keywords: Large Scale Systems, Networked Systems and Process Control Chair B. Erik Ydstie Professor: Carnegie Mellon University 5000 Forbes Avenue Department of Chemical Engineering Pittsburgh, PA 15213 Email: ydstie@cmu.edu -- Will not be published Co-Chair Nael H. El-Farra Professor: University of California, Davis One Shields Avenue Department of Chemical Engineering & Materials Science Davis, CA 95616 Fax Number: 530-752-1031 Email: nhelfarra@ucdavis.edu

26908: Process Design Sub Title: This session emphasizes recent advancements in Process Design. Papers that discuss novel theories and applications, innovative design strategies, incorporation of control or environmental considerations into the design process, and non-traditional process design problems will be encouraged. In addition to the traditional mathematical algorithms used for process design, simulation-based approaches are also sought. Industrial-based applications are also of particular interest. The contribution of the paper to the state-of-the-art should be clearly stated in the abstract. Type: Oral keywords: Computational Methods, Pilot Plants and Process Design Abstract id# 359808

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Design and Optimization of LNG Liquefaction and Bog Re-Liquefaction Processes Wonsub Lim, Kwangpil Chang, Jinkwang Lee and Kihong Kim, Hyundai Heavy Industries, Yongin-si, South Korea Abstract Text:

The natural gas is the fastest growing energy resource. The LNG market is continuously expanding as transportation over long distance is increased. In the LNG value chain, liquefaction technology is very important because it can be applied to LNG liquefaction and BOG re-liquefaction and these processes use a large amount of energy.

The LNG liquefaction and BOG re-liquefaction processes are designed based on the same fundamental principle 'compression refrigeration' but the objectives and conditions are little different. The selection of appropriate refrigeration cycle is very important in the design of LNG liquefaction and BOG re-liquefaction. The same refrigeration cycle could be employed but an optimal cycle may be different.

This study focuses on the differences between LNG Liquefaction and BOG re-liquefaction processes in respect of design and optimization. First, the processes were analyzed to determine boundary conditions and constraints. Next, the processes were designed based on mixed refrigerant and nitrogen expander cycles. Then, simulation and optimization studies were carried out by using commercial software. The results provide useful guidance to determine optimal process design.

Chair Thomas A. Adams II Assistant Professor: McMaster University 1280 Main Street West JHE-371 Chemical Engineering Hamilton, ON L8S 4L7 Canada Fax Number: (905) 521-1350 Email: tadams@mcmaster.ca Co-Chair Fengqi You Assistant Professor: Northwestern University 2145 Sheridan Road Department of Chemical and Biological Engineering Evanston, IL 60208-3120 Email: you@northwestern.edu Co-Chair Parag Jain Graduate Student: Carnegie Mellon University 5000 Forbes Ave. Chemical Engineering

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Pittsburgh, PA 15213 Email: Parag.Jain@Honeywell.com -- Will not be published Co-Chair Ignasi Palou-Rivera Principal Environmental Engineer: Argonne National Laboratory 9700 S Cass Ave Argonne, IL 60439 Email: ignasi.palou-rivera@lanzatech.com -- Will not be published

26909: Product and Molecular Design Sub Title: Designing a new chemical product or molecule is a complex process requiring many tools to assist in the innovation, evaluation and management of a novel concept. This session welcomes papers discussing experiences in product and molecular design. Type: Oral keywords: Materials, Pharmaceutical applications and Product design Chair Zoltan K. Nagy Professor of Chemical Engineering: Purdue University 480 Stadium Mall Drive Forney Hall G027 Chemical Engineering West Lafayette, IN IN 47907-2100 Email: zknagy@purdue.edu -- Will not be published Co-Chair Dimitrios I. Gerogiorgis University of Edinburgh Scotland, United Kingdom Phone Number: ARRAY(0xf707bd0) Email: Dimitrios.Gerogiorgis@gmail.com -- Will not be published

26910: Dynamic Simulation and Optimization Sub Title: Most chemical and biochemical systems and process operations are dynamic in nature where improved understanding and the analysis of the system dynamics may lead to substantial improvements in the development and control of these systems. The

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session invites papers on the dynamic simulation, control and optimization of various such systems. Applications and tools for dynamic simulation are considered, including large-scale, distributed parameter, and hybrid systems, as well as new algorithmic development in solving dynamic optimization problems, including robust and global optimization. Application areas and examples from various physical systems involving embedded dynamic constraints are also encouraged. Type: Oral keywords: Computational Methods, Dynamic Simulation and Plant Operations Chair Ajay Lakshmanan Sr. Manager: Aspen Technology, Inc. 200 Wheeler Road Burlington, MA 01803 Email: ajay.lakshmanan@aspentech.com Co-Chair Victor M. Zavala Argonne Scholar: Argonne National Laboratory 9700 S Cass Ave Mathematics and Computer Science Argonne, IL 60439 Email: vzavala@mcs.anl.gov -- Will not be published

26911: Dynamics, Reduction and Control of Distributed Parameter Systems Sub Title: In this session novel contributions in the broad areas of control, estimation, dynamics and model reduction of distributed parameter systems are invited. Contributions emphasizing advances in theoretical, numerical and computational aspects associated with distributed parameter systems are encouraged to participate. Type: Oral keywords: Computational Methods, Control and Estimation of distributed parameter systems (DPS), Dynamics and Model reduction of distributed parameter systems (DPS) and Numerical & Computational aspects of distributed parameter systems (DPS) Chair Antonios Armaou The Pennsylvania State University 170 Fenske Lab Chemical Engineering University Park, PA 16802-4400 Email: armaou@psu.edu -- Will not be published

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* AICHE Member * Division Member Co-Chair Panagiotis D. Christofides Professor: University of California, Los Angeles Department of Chemical and Biomolecular Engineering Los Angeles, CA 90095 Fax Number: 310-206-4107 Email: pdc@seas.ucla.edu

26912: Energy Systems Design & Operations Sub Title: With the increasing energy consumption of the world, it has become necessary to design efficient energy systems and also explore alternative energy sources. This session will focus on recent research work in energy systems design and operations. Type: Oral keywords: Alternative Energy/Fuels, Biofuels, Fossil Fuel and Process Design Chair Edward P. Gatzke Associate Professor: University of South Carolina, Dept. of Chemical Engineering Swearingen Engineering Center 301 Main St. Chemical Engineering Columbia, SC 29208 Fax Number: (803) 777-8265 Email: gatzke@sc.edu -- Will not be published Co-Chair Alexander Mitsos Assistant Professor: Massachusetts Institute of Technology 77 Massachusetts Avenue MIT 3-158 Department of Mechanical Engineering Cambridge, MA 02139 Fax Number: 1-617-258-5802 Email: mitsos@mit.edu -- Will not be published Co-Chair Yoshiaki Kawajiri Assistant Professor: Georgia Institute of Technology

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311 Ferst Drive School of Chemical & Biomolecular Engineering Atlanta, GA 30332-0100 Email: ykawajiri@chbe.gatech.edu -- Will not be published Co-Chair Pahola Benavides VISHWAMITRA RESEARCH INSTITUTE Email: thathiana@vri-custom.org -- Will not be published

26913: Industrial Applications in Design and Operations Sub Title: This session focuses on the practical applications of Process Systems Engineering. Areas of interest include applications of fundamental research in modeling, optimization and control in Enterprise Wide Optimization including scheduling, planning and control of supply chains, product and process design including microsystems, metabolic networks, and separation systems. Process control and safety papers on proven applications of process systems engineering in operations, collaborations between academia and industry with a potential future impact, and case studies are welcome. Type: Oral keywords: Industrial Applications, Plant Operations and Process Design Chair Ramkumar Karuppiah Corporate Strategic Research, ExxonMobil Research and Engineering Annandale, NJ 08801 Email: ramkumar.karuppiah@exxonmobil.com -- Will not be published Co-Chair Stacy L. Janak Sr. Software Developer: Aspen Technology, Inc. 2500 CityWest Blvd - Suite 1500 Houston, TX 77042 Email: stacy.janak@aspentech.com -- Will not be published

26914: Modeling and Computation in Energy and Environment Sub Title: In this session contributions on computational and theoretical research and/or development in the area of Energy and Environment are invited. Type: Oral

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keywords: Computational Methods, Energy & Environment, Modelling & Computation in Energy and Modelling & Computation in Environment Abstract id# 361019 Heat Integration of Continuous Streams in Batch Plants Jui-Yuan Lee, Esmael Reshid Seid and Thokozani Majozi, School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa Abstract Text:

Batch processes are flexible allowing the production of different products within the same facility, and suitable for producing low volume, high value-added products such as pharmaceuticals and agrochemicals. The trend towards batch processing has necessitated the development of scheduling techniques. Common objectives of batch process scheduling include profit maximisation within a given time horizon and makespan minimisation for a given production target. In addition to process scheduling, heat integration has been increasingly considered for batch plants to reduce external utility (e.g. steam and cooling water) requirements for tasks involving heating or cooling, such as endothermic and exothermic reactions.

The past two decades have been characterised by a significant body of research addressing heat integration of batch plants. Most of this research considers direct and/or indirect heat integration between processing units. However, the opportunity for heat recovery through heat exchange between process streams requiring heating and cooling (to meet the operating temperatures) was overlooked. The aim of this work is, therefore, to exploit such heat recovery opportunities. No previous study, as yet, has addressed heat integration of process streams during transfer between units with the consideration of process scheduling. In this work, a mathematical technique for simultaneous process scheduling and heat integration of batch plants is presented. The formulation, based on a superstructure, aims to maximise the coincidence of availability of hot and cold process stream pairs with feasible temperature driving forces, whilst taking into account scheduling constraints. Heat integration during stream transfer can shorten the time required for heating and cooling in processing units, and is expected to enable higher production and lower utility consumption for batch plants. A case study is solved to demonstrate the application of the proposed mathematical model.

Chair Raymond A. Adomaitis Professor: University of Maryland 2113 Chemical & Nuclear Engineering Bldg #090 Department of Chemical & Biomolecular Engineering; Institute for Systems Research College Park, MD 20742-2115 Email: adomaiti@umd.edu Co-Chair Dimitrios V. Papavassiliou The University of Oklahoma 100 East Boyd St School of Chemical Biological and Materials Engineering Norman, OK 73019 Email: dvpapava@ou.edu

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26915: Modeling and Control of Energy Systems Sub Title: Papers in the broad area of modeling, control and estimation of energy systems are solicited for this session Type: Oral keywords: Energy Systems, Process Control and Process Modeling Chair Fernando V. Lima Assistant Professor: West Virginia University 437 Engineering Sciences Building Department of Chemical Engineering Morgantown, WV 26506 Fax Number: 304-293-4139 Email: Fernando.Lima@mail.wvu.edu Co-Chair Prashant Mhaskar Associate Professor: McMaster University Chemical Engineering Hamilton, ON L8S 4L7 Canada Email: mhaskar@mcmaster.ca -- Will not be published

26916: Modeling and Control of Sustainable Processes Sub Title: Contributions are sought in the general area of sustainable chemical processing Type: Oral keywords: Process Control, Process Modeling and Sustainable Processes Chair Michael Baldea Assistant Professor: University of Texas 200 E Dean Keeton St. Stop C0400 Chemical Engineering

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Austin, TX 78712-1589 Email: mbaldea@che.utexas.edu -- Will not be published

26917: Molecular and Mesoscopic Systems: Methods and Applications Sub Title: Advances in the modeling and simulation of molecular and mesoscopic level systems are invited. Interfaces, molecular interactions, coarse graining, mesoscopic simulations and modeling, as well as multiscale approaches are among the areas that are covered by this session. Type: Oral keywords: Computational Methods, Mesoscopic simulations and modelling and Molecular interactions Chair Constantinos Theodoropoulos University of Manchester Sackville St School of Chemical Engineering and Analytical Science Manchester, M60 1QD United Kingdom Fax Number: +44 (0)161 236-7439 Email: k.theodoropoulos@manchester.ac.uk

26918: Multiscale Modeling: Methods and Applications Sub Title: Majority of chemical engineering problems are governed by dynamics associated with time and length scales that differ by several orders of magnitude. Bridging these scales with integrated, multi-scale models is a challenging problem. Contributions are solicited on novel, integrated multi-scale modelling methods with emphasis on numerical/algorithmic/computational details and associated applications in chemical and biological engineering. Type: Oral keywords: Computational Methods, Multiscale Algorithms, Multiscale Modelling and Numerical & Computational Aspects Chair Constantinos Theodoropoulos University of Manchester Sackville St

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School of Chemical Engineering and Analytical Science Manchester, M60 1QD United Kingdom Fax Number: +44 (0)161 236-7439 Email: k.theodoropoulos@manchester.ac.uk Co-Chair Antonios Armaou The Pennsylvania State University 170 Fenske Lab Chemical Engineering University Park, PA 16802-4400 Email: armaou@psu.edu -- Will not be published * AICHE Member * Division Member

26919: Networked, Decentralized and Distributed Control Sub Title: We seek contributions in the area of distributed control, including cooperative control approaches. Type: Oral keywords: Cooperative Control, Decentralized Control, Networked Control and Process Control Chair Panagiotis D. Christofides Professor: University of California, Los Angeles Department of Chemical and Biomolecular Engineering Los Angeles, CA 90095 Fax Number: 310-206-4107 Email: pdc@seas.ucla.edu Co-Chair Vikas Shukla ExxonMobil Chemical Company Email: vikas.shukla@exxonmobil.com -- Will not be published

26920: Networked, Decentralized and Distributed Control Sub Title: In this session novel contributions on networked, distributed, decentralized and/or cooperative control realizations are invited. Contributions on theoretical and application

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aspects of complex networked realized control architecture are encouraged to apply to this session. Type: Oral keywords: Computational Methods, Decentralized Control, Distributed Control and Networked Control Chair Mark P. Styczynski Assistant Professor: Georgia Institute of Technology 311 Ferst Drive School of Chemical & Biomolecular Engineering Atlanta, GA 30332 Email: Mark.Styczynski@chbe.gatech.edu Co-Chair Nael H. El-Farra Professor: University of California, Davis One Shields Avenue Department of Chemical Engineering & Materials Science Davis, CA 95616 Fax Number: 530-752-1031 Email: nhelfarra@ucdavis.edu

26921: Optimization and Predictive Control Sub Title: We seek contributions on new formulations of predictive control, as well as novel solution algorithms. Type: Oral keywords: Optimization, Predicitve Control and Process Control Abstract id# 363515 Stochastic Output Feedback Control of Nonlinear Systems with Probabilistic Uncertainties: Application to Control of Polymorphic Transformations in Batch Crystallization Ali Mesbah1, Joel Paulson2 and Richard D. Braatz2, (1)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (2)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA Abstract Text:

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Measurement noise, parametric and structural uncertainties, and exogenous disturbances are ubiquitous in complex dynamical systems. Uncertainties can lead to undesired variability of the system outputs and, as a result, a notable degradation of closed-loop performance. In this talk, a general framework will be presented for stochastic model predictive control (MPC) and state estimation of nonlinear systems with probabilistic, time-invariant uncertainties. Stochastic MPC with chance constraints (e.g., [1,2,3]) uses probabilistic descriptions of system uncertainties to realize acceptable levels of risk during system operation. Stochastic MPC enables shaping the probability distribution of system states, while ensuring the satisfaction of constraints with a desired probability level in a stochastic setting. This flexibility would have significant economic and safety implications for high performance operation of complex chemical and biological systems, which is often achieved in the vicinity of constraints. On the other hand, nonlinear estimation of the uncertain state variables enables output feedback application of stochastic MPC to (partly) suppress the effect of model mismatches and time-varying disturbances on control performance.

The key challenges in state estimation and control of nonlinear systems are the propagation of probabilistic uncertainties and reformulation of chance constraints in terms of computationally tractable expressions. In this work, generalized polynomial chaos (PC) theory [4,5] is used to present a probabilistic framework for stochastic nonlinear state estimation and control. The PC approach enables replacing the implicit mappings between the uncertain variables/parameters and system states with an expansion of orthogonal polynomials, whose statistical moments can be determined efficiently from the expansion coefficients (e.g., see [6,7,8]). Hence, PC expansions are computationally efficient surrogates for Monte Carlo-based approaches to perform uncertainty analysis for real-time estimation and control. A nonlinear Bayesian state estimation algorithm is presented that utilizes PC expansions to describe the evolution of state uncertainty, which is then used to maximize the posterior probability density function of the random state variables. The stochastic MPC approach is formulated to control the predicted probability densities of system states over a finite prediction horizon, while ensuring the satisfaction of constraints with a desired probability level. To obtain a computationally tractable stochastic optimal control problem, the individual chance constraints are transformed into explicitly convex second-order cone constraints for a general class of probability distributions with known mean and covariance [9].

The proposed stochastic output feedback control approach is applied for optimal control of the polymorphic transformation of L-glutamic acid in cooling batch crystallization [10]. Polymorphic transformation is of paramount importance in the specialty chemical and pharmaceutical industries, as such transformations can lead to undesired variations in the chemical and physical properties of the product crystals. The control problem in polymorphic transformations is to ensure consistent manufacturing of the desired polymorph in a stochastic setting due to time-invariant uncertainties in initial conditions and crystallization kinetic parameters. The simulation results indicate that the stochastic output feedback control approach enables effective shaping of the probability density of the performance index (i.e., the nucleation crystal mass to the seed crystal mass of crystals of the desired form). Stochastic MPC results in a significant reduction (approximately 30%) in the variance of probability density of the performance index, as compared to a nonlinear MPC approach that disregards the probabilistic system uncertainties. In addition, the chance constraints in stochastic MPC ensure that the system operation remains in the designated operating region defined in terms of state constraints, whereas in the nonlinear MPC approach the constraints are violated due to the system uncertainties.

[1] A. Schwarm and M. Nikolaou. Chance-constrained model predictive control. AIChE Journal, 45:1743-1752, 1999.

[2] D. Bernardini and A. Bemporad. Scenario-based model predictive control of stochastic constrained linear systems. In Proceedings of the 48

thIEEE Conference on Decision and Control, 6333-6338,

Shanghai, 2009.

[3] G. C. Calafiore and L. Fagiano. Robust model predictive control via scenario optimization. IEEE Transactions on Automatic Control, 58:219-224, 2013.

[4] N. Wiener. The homogeneous chaos. American Journal of Mathematics, 60:897-936, 1938.

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[5] D. Xiu and G. E. Karniadakis. The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM Journal of Scientific Computation, 24:619-644, 2002.

[6] Z. K. Nagy and R. D. Braatz. Distributional uncertainty analysis using power series and polynomial chaos expansions. Journal of Process Control, 17:229-240, 2007.

[7] J. Fisher and R. Bhattacharya. Linear quadratic regulation of systems with stochastic parameter uncertainties. Automatica, 45:2831-2841, 2011.

[8] A. Mesbah, S. Streif, R. Findeisen, and R. D. Braatz. Stochastic nonlinear model predictive control with probabilistic constraints. In Proceedings of the American Control Conference, Portland, 2014, in press.

[9] G. C. Calafiore and L. El Ghaoui. On distributionally robust chance-constrained linear programs. Journal of Optimization Theory and Application, 130:1-22, 2006.

[10] M. W. Hermanto, N. C. Kee, R. B. H. Tan , M. Chiu, and R. D. Braatz. Robust Bayesian estimation of kinetics for the polymorphic transformation of L-Glutamic acid crsytals. AIChE Journal, 54:3248-3259, 2008.

Chair Rishi Amrit Advance Process Control Engineer: Shell Global Solutions (US) Inc. 3333 Hwy 6 S Westhollow Technology Center Houston, TX 77210 Email: rishiamrit@gmail.com Co-Chair Edward P. Gatzke Associate Professor: University of South Carolina, Dept. of Chemical Engineering Swearingen Engineering Center 301 Main St. Chemical Engineering Columbia, SC 29208 Fax Number: (803) 777-8265 Email: gatzke@sc.edu -- Will not be published

26922: Perspectives on Information Management and Intelligent Systems (Invited Talks) Sub Title: This session puts the field of information management and intelligent systems in perspective within the context of the chemical engineering, relating the current state of the art to historical and future trends. The invited abstracts & speakers will focus on the needs of chemical engineering and the challenges faced by information management in improving manufacturing, innovation, research, and education.

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Type: Oral keywords: Historical and future trends, Information management and Research and innovation Chair Venkat Venkatasubramanian Professor: Columbia University 500 W 120th Street 800 Mudd building Chemical Engineering New York, NY 10027 Email: venkat@columbia.edu Co-Chair Gürkan Sin Associate professor: Technical University of Denmark Soltofts Plads, Building 227 Department of Chemical and Biochemical Engineering Kgs. Lyngby, Denmark Email: gsi@kt.dtu.dk -- Will not be published

26923: Planning and Scheduling Sub Title: Papers are invited in the general area of planning and scheduling of manufacturing processes. Planning and scheduling are key decision-making activities that vary in scope from the operational (day) and tactical (week) level, to the strategic (month/year) level. Despite the significant progress in this area in the last decade, many industrial problems still remain open. Areas of interest in this session include (but are not limited to): (i) novel modeling and optimization strategies for planning and scheduling of batch and continuous plants, (ii) integration of planning and scheduling in a unified framework, (iii) technologies for the analysis and visualization of planning and scheduling solutions in the context of enterprise-level decision making, (iv) novel incorporation of modeling uncertainty into planning and scheduling from plant operations and project management to the extended supply chain and (v) successful large-scale applications of new planning and scheduling methodologies. Application papers offering insights into the interaction between theory and practice in an industrial setting are particularly encouraged. Type: Oral keywords: Computational Methods, Planning, Plant Operations and Scheduling Abstract id# 361471

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Distillation Blending and Cutpoint Temperature Optimization Using Monotonic Interpolation Brenno C. Menezes, Refining Planning, Petrobras, Rio de Janeiro, Brazil, Jeffrey D. Kelly, Industrial Algorithms, Toronto, ON, Canada and Ignacio E. Grossmann, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA Abstract id# 362759 Resource-Task Network Continuous-Time Formulation for Crude Oil Blending Operations Pedro M. Castro, Centro de Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal and Ignacio E. Grossmann, Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA Abstract Text:

Every refinery has an optimization opportunity associated with better crude oil blend scheduling that can reach multimillion dollars per year [1]. From the crude oil purchases perspective, the faster the ability to assess whether a particular spot oil purchase will result in a feasible operation, the better the refinery can capitalize on short-term market opportunities. From the operational perspective, variation in crude oil mixture quality and flowrate charged to the distillation columns affects downstream production units and is perhaps the single most influential disturbance to a refinery. Overall, crude oil blend scheduling optimization is a relatively inexpensive and timely way to improve performance.

Early works on crude oil scheduling with mathematical programming techniques employed discrete-time formulations [2,3]. In particular, Lee et al. [3] proposed a MILP for the system comprised of storage tanks receiving crude from marine vessels, charging tanks and crude distillation units. It can be viewed as a relaxation of the non-convex MINLP model that is required to ensure that there are no discrepancies in crude compositions inside tanks with respect to their outlet streams.

The turn of the millennium saw increased emphasis on continuous-time models. Jia et al. [4] proposed a State-Task Network, unit-specific formulation. A two-stage MILP-NLP solution procedure was proposed to tackle the MINLP, featuring in the first stage a relaxed MILP model without the complex, bilinear blending constraints. After fixing the binary variables, a near optimal solution to the original problem was then found in the second stage. A more rigorous model with respect to the synchronization of time events with material balances is given in [5]. The unit-specific MINLP model of Li et al. [10] is for marine-access refineries and is tackled through a spatial branch-and-bound optimization algorithm that on each node uses the MILP-NLP procedure. Rather than removing the blending constraints, a tighter MILP relaxation was created through piecewise McCormick envelopes [11]. Overall, the integer feasible solutions obtained for a set of test cases from the literature were guaranteed to be within 2% of global optima.

Moro and Pinto [6] opted for a single time grid MINLP continuous-time formulation that was tackled with the augmented penalty version of the outer-approximation method. Sharing the time representation concept and the inability to provide global optima, Reddy et al. [7] proposed a MILP relaxation along the lines of [3] combined with a rolling-horizon algorithm to eliminate the composition discrepancy.

Mouret et al. [8] considered yet another type of continuous-time formulation, called single operation sequencing model, which represents a schedule as a sequence of priority slots. Nonlinearities where handled by the two stage procedure of [4]. Later, the part of the model without the blending constraints (MILP) was compared to models relying on different time representation concepts, with continuous-time scaling better with problem size and hence outperforming its discrete-time counterpart [8].

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In this work, we approach the solution of the crude oil scheduling problem with the Resource-Task Network based single time grid, continuous-time formulation of Castro et al. [12]. Contrary to most scheduling problems that feature a fixed recipe with known proportions of input materials to a processing task, the main goal of crude oil scheduling is to identify the optimal mix of low-cost and premium crudes that maximizes profit while meeting operational constraints. The main novelty is thus related to extending the generality of the formulation to handle variable recipe tasks with multiple input materials, of which crude oil blending is a particular case. While doing so, we also show how to derive a RTN superstructure that implicitly meets the logistic constraint of no simultaneous inlet and outlet streams from a storage/charging tank. The third novelty is related to the use of multiparametric disaggregation [13,14] for the relaxation of the bilinear blending constraints that: (i) is known to be tighter than the one resulting from the standard McCormick relaxation; (ii) scales more favorably than piecewise McCormick relaxation [14], often leading to lower optimality gaps.

Overall, the new formulation has the advantage of avoiding computationally inefficient big-M constraints, unlike previously proposed unit-specific and priority-slot based models. Through the solution of a set of test problems from the literature, we show that the majority of the resulting MINLP problems can be solved to global optimality by the state-of-the art commercial solver GloMIQO [16] in a few seconds. We also show that adopting a two-step MINLP algorithm in which the MILP relaxation is derived from multiparametric disaggregation, can reduce the optimality gap of the most difficult problem from 0.73 to 0.26%.

Acknowledgments:

Financial support from the Luso-American Foundation under the 2013 Portugal-U.S. Research Networks Program, from Fundação para a Ciência e Tecnologia (FCT) through the Investigador FCT 2013 program, and from FEDER (Programa Operacional Factores de Competitividade-COMPETE) and FCT through project FCOMP-01-0124-FEDER-020764.

References:

[1] Kelly, J.D.; Mann, J.L. Crude oil blend scheduling optimization: an application with multimillion dollar

benefits-Part 1. Hydrocarbon Processing 2003, 82(6), 47.

[2] Shah, N. Mathematical Programming Techniques for Crude Oil Scheduling. Comput. Chem.

Eng. 1996, 20, S1227.

[3] Lee, H.; Pinto, J. M.; Grossmann, I. E.; Park, S. Mixed-integer linear programming model for refinery

short-term scheduling of crude oil unloading with inventory management. Ind. Eng. Chem. Res. 1996, 35,

1630.

[4] Jia, Z.; Ierapetritou, M.; Kelly, J. D. Refinery Short-term Scheduling Using Continuous Time

Formulation: Crude-Oil Operations. Ind. Eng. Chem. Res. 2003, 42, 3085.

[5] Furman, K.; Jia, Z.; Ierapetritou, M.G. A Robust Event-Based Continuous Time Formulation for Tank

Transfer Scheduling. Ind. Eng. Chem. Res. 2007, 46, 9126.

[6] Moro, J.F.L.; Pinto, J.M. Mixed-integer Programming Approach for Short-Term Crude Oil

Scheduling. Ind. Eng. Chem. Res. 2004, 43, 85.

[7] Reddy, P.C.P.; Karimi, I.A.; Srinivasan, R. A new continuous-time formulation for scheduling crude oil

operations. Chem. Eng. Sci. 2004, 59, 1325.

[8] Mouret, S.; Grossmann, I. E.; Pestiaux, P. A novel priority-slot based continuous-time formulation for

crude-oil scheduling problems. Ind. Eng. Chem. Res. 2009, 48, 8515.

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[9] Mouret, S.; Grossmann, I.E.; Pestiaux, P. Time representations and mathematical models for process

scheduling problems. Comput. Chem. Eng. 2011, 35, 1038.

[10] Li, J.; Misener, R.; Floudas, C.A. Continuous-Time Modeling and Global Optimization Approach for

Scheduling of Crude Oil Operations. AIChE J. 2012, 58, 205.

[11] Karuppiah, R.; Grossmann, I.E. Global optimization for the synthesis of integrated water systems in

chemical processes. Comput. Chem. Eng. 2006, 30, 650.

[12] Castro, P. M.; Barbosa-Póvoa, A. P.; Matos, H. A.; Novais, A. Q. Simple Continuous-Time

Formulation for Short-Term Scheduling of Batch and Continuous Processes. Ind. Eng. Chem.

Res. 2004, 43, 105.

[13] Teles, J. P.; Castro, P. M.; Matos, H. A. Multiparametric disaggregation technique for global

optimization of polynomial programming problems. Journal of Global Optimization 2013,55, 227.

[14] Kolodziej, S.; Castro, P. M.; Grossmann, I. E. Global optimization of bilinear programs with a

multiparametric disaggregation technique. Journal of Global Optimization 2013, 57, 1039.

[15] McCormick, G. P. Computability of global solutions to factorable nonconvex programs. Part I. Convex

underestimating problems. Mathematical Programming 1976, 10, 147.

[16] Misener, R.; Floudas, C.A. GloMIQO: Global mixed-integer quadratic optimizer. Journal of Global

Optimization 2013, 57, 3.

Abstract id# 360554 Title Matteo Abaecherli, Institute for Chemical and Bioengineering, ETH Zürich, Zürich, Switzerland Chair Zukui Li Assistant Professor: University of Alberta Department of Chemical and Materials Engineering Edmonton, AB T6G2V4 Canada Email: zukui@ualberta.ca Co-Chair Pedro M. Castro Researcher: LNEG Department of Energy Analysis and Networks Lisbon, Portugal Email: pedro.castro@lneg.pt Co-Chair

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Stacy L. Janak Sr. Software Developer: Aspen Technology, Inc. 2500 CityWest Blvd - Suite 1500 Houston, TX 77042 Email: stacy.janak@aspentech.com -- Will not be published Co-Chair Dimitrios Varvarezos Chemical Engineer: Aspen Technology, Inc. 2500 CityWest Blvd - Suite 1500 Houston, TX 77042 Email: dimitri@aspentech.com -- Will not be published

26924: Poster Session: Information Management and Intelligent Systems Sub Title: This session solicits posters in the broad area of Information Management and Intelligent Systems in the chemical engineering domain. Abstracts describing methods, applications, challenges, and tools, are all welcome. Type: Poster keywords: Computational Methods, Information management and Intelligent systems Chair Gürkan Sin Associate professor: Technical University of Denmark Soltofts Plads, Building 227 Department of Chemical and Biochemical Engineering Kgs. Lyngby, Denmark Email: gsi@kt.dtu.dk -- Will not be published Co-Chair Bri-Mathias S. Hodge National Renewable Energy Laboratory 15013 Denver West Parkway MS ESIF 200 Golden, CO 80401 Email: bri-mathias.hodge@nrel.gov -- Will not be published

26925: Poster Session: Applied Mathematics and Numerical Analysis

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Sub Title: Presentations in all areas related to numerical methods, computational aspects and/or simulation techniques, mathematical modelling and their applications in chemical engineering are invited. Type: Poster keywords: Chemical Engineering Applications, Computational Methods, Computational/Simulation Aspects & Techniques and Mathematical Modelling Chair Stevan Dubljevic Assistant Professor: University of Alberta 9107-116 Street 7th Floor, ECERF Department of Chemical and Materials Engineering Edmonton, AB T6G 2V4 Canada Email: stevan.dubljevic@ualberta.ca -- Will not be published * AICHE Member * Division Member Co-Chair Jinfeng Liu Assistant Professor: University of Alberta Department of Chemical and Materials Engineering Edmonton, AB T6G2V4 Canada Email: jinfeng@ualberta.ca

26926: Poster Session: Systems and Process Control Sub Title: We seek contributions in the general area of Process Control, to be presented in poster format. Type: Poster keywords: Poster Session, Process Control and Systems Chair Tyler A. Soderstrom Sr. Staff Engineer: ExxonMobil 4500 Bayway Dr. Research and Engineering

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Baytown, TX 77522 Email: tyler.a.soderstrom@exxonmobil.com -- Will not be published Co-Chair Nael H. El-Farra Professor: University of California, Davis One Shields Avenue Department of Chemical Engineering & Materials Science Davis, CA 95616 Fax Number: 530-752-1031 Email: nhelfarra@ucdavis.edu

26927: Poster Session: Systems and Process Design Sub Title: This is the general poster session for CAST 10A - Systems and Process Design. All contributions in the areas of process/product design and analysis will be considered. Type: Poster Chair Selen Cremaschi Assistant Professor: University of Tulsa Department of Chemical Engineering Tulsa, OK 74104 Fax Number: 918-640-3268 Email: selen-cremaschi@utulsa.edu -- Will not be published

26928: Poster Session: Systems and Process Operations Sub Title: Poster submissions are invited highlighting recent advances in all areas of systems and process operations. Novel results and ideas are highly encouraged to be submitted. Outstanding presentations will be considered for area 10C awards. Type: Poster keywords: Computational Methods, Plant Operations and Systems and Process Operations Chair John D. Siirola Sandia National Laboratories P.O. Box 5800 MS 1326 Analytics Department

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Albuquerque, NM 87185-1326 Email: jdsiiro@sandia.gov -- Will not be published Co-Chair Alexander Mitsos RWTH Aachen University Email: alexander.mitsos@avt.rwth-aachen.de -- Will not be published

26929: Process Control Applications Sub Title: The session will include presentations that report on novel applications of process control techniques to a wide range of chemical engineering systems. Submissions describing industrial implementations and the associated challenges that were encountered are strongly encouraged. Applications to unconventional systems which present unique challenges are also of great interest. Type: Oral keywords: Computational Methods, Plant Operations and Process Control Abstract id# 356832 Nonlinear Model Predictive Control of an Industrial Polymerization Process Rahul Bindlish, Engineering Solutions Technology Center, Dow Chemical, Freeport, TX Abstract Text:

Abstract for presentation at AIChE Annual meeting, 2014

Title: Nonlinear Model Predictive Control of an Industrial Polymerization Process

Nonlinear model predictive control (NMPC) is used to maintain and control polymer quality at specified production rates because the polymer quality measures have strong interacting nonlinearities with different temperatures and feed rates. Polymer quality measures that are available from the laboratory infrequently are controlled in closed-loop using a NMPC to set the temperature profile of the reactors. NMPC results in better control of polymer quality measures at different production rates as compared to using the nonlinear process model with reaction kinetics to implement offline targets for reactor temperatures.

Dow's first application of a commercial Nonlinear Model Predictive Control technology that uses the laboratory quality measures in the feedback control loop is presented.

The industrial Nonlinear Model Predictive Control problem has the following challenges

o Long laboratory sampling times for controlled polymer quality attributes (0.5-1 day)

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o Varying dead times (2-7 days) and gains (multiplier of 1-20) for controlled polymer quality attributes with respect to reactor temperatures

o Process models need to extend for extremely low feed rates (approximately 35 percent of normal rates)

o Process also occasionally operates with one of the seven reactors bypassed for maintenance o For the first four reactors, recycle streams can only be manually set for heating or cooling

A Linear Model Predictive Controller (LMPC) will not be able to achieve the process objectives because there are strong nonlinear dependencies for polymer quality attributes with reactor temperatures and feed.

1. Process Description: The industrial polymerization process consists of seven well mixed reactors in series, where the extent of reaction is set by level and temperature in each reactor. The copolymerization of monomer and comonomer is carried out using a catalyst to make a polymer characterized by polymer viscosity, unreacted monomer content and byproduct content. Comonomer composition in the feed is set at a stoichiometric excess value to minimize the unreacted monomer content in the polymer product. The catalyst dissolved in a solvent is also fed separately to the first reactor. The flow rate and composition of the feed streams are measured on-line along with the reactor temperatures and levels. Off-line laboratory measurements are made for the polymer viscosity, unreacted monomer content and byproduct content. Each reactor has a recycle stream, whose temperature is controlled by heating or cooling it. The reactor temperature is controlled by manipulating the recycle stream temperature.

2. Nonlinear Model Predictive Control (NMPC): A validated fundamental kinetic model exists for

the process. The model consists of the various reactions taking place in the industrial reactors along with other physical phenomena governing the polymerization process, and has been used historically to maintain polymer quality attributes by evaluating an off-line reactor temperature profile. Aspen Technology Inc.'s Aspen Non-Linear Controller [1] is used as the commercial NMPC controller, thereby requiring development of a bounded-derivative-neural network (BDN) model instead of directly using the fundamental kinetic model. Initial BDN model development was done by using results of numerous fundamental kinetic model cases (approximately 50,000 cases) to cover the operating region of interest. These models were then deployed on-line, and tuned by comparison with real plant data. Over-parameterization of the BDN models was avoided to ensure extrapolation, and suitability for feedback control. The BDN model first derivatives of the controlled variables with respect to reactor temperatures and levels were examined over the operating range to ensure that they were monotonically increasing or decreasing, to match with those from the fundamental kinetic model.

3. Conclusions: The industrial NMPC has been in continuous use since October 2012 to maintain

polymer quality at specified production rates. After meeting the polymer quality and feed requirements, the NMPC has been enhanced to minimize the byproduct content. A cascade NMPC scheme was implemented to achieve the process objectives for reactor temperature control and polymer quality specifications that had vastly different settling times, and also to address the computational needs of the associated dynamic optimization problem. Dynamic optimization associated with this nonlinear control problem is computationally very demanding and required the Aspen Technology, Inc.'s software developers to remove program limitations.

References

[1] Turner, P., and J. Guiver, ``Introducing the bounded derivative network—superceding the application

of neural networks in control,'' J. Proc. Cont., 15 (4), 407—415 (2005).

Chair David H. Gay Director of Technology, Institute for Collaborative Biotechnologies: University of California, Santa Barbara Chemical Engineering

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Santa Barbara, CA 93106-5100 Email: david.gay@icb.ucsb.edu Co-Chair John R. Richards Research Fellow: E. I. du Pont de Nemours and Company PO Box 80304 Experimental Station DuPont Engineering Research & Technology Wilmington, DE 19880-0304 Fax Number: (302) 695-4414 Email: John.R.Richards@dupont.com

26930: Design of Integrated Biorefinery Sub Title: This session emphasizes recent advancements in Integrated Biorefinery design. Type: Oral keywords: Alternative Energy/Fuels, Biofuels and Process Design Chair Antonis Kokossis Professor, CEng, FIChemE, FRSA, FIET: National Technical University of Athens Zografou Campus, 9, Iroon Polytechniou Str. GR-15780 School of Chemical Engineering Athens, Greece Fax Number: 00-30-2-10-772- 3155 Email: akokossis@chemeng.ntua.gr Co-Chair Mario Richard Eden Department Chair and McMillan Professor: Auburn University 210 Ross Hall Department of Chemical Engineering Auburn, AL 36849-5127 Fax Number: 334-844-2063 Email: edenmar@auburn.edu

26931:

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Process Modeling and Identification Sub Title: Process Modeling and Identification Type: Oral keywords: Process Control, Process Identification and Process Modeling Chair Jin Wang B. Redd Associate Professor: Auburn University Auburn, AL 36849 Fax Number: 334-844-2063 Email: wang@auburn.edu Co-Chair Stevan Dubljevic Assistant Professor: University of Alberta 9107-116 Street 7th Floor, ECERF Department of Chemical and Materials Engineering Edmonton, AB T6G 2V4 Canada Email: stevan.dubljevic@ualberta.ca -- Will not be published * AICHE Member * Division Member

26932: Process Monitoring and Fault Detection Sub Title: We are interested in novel contributions concerning monitoring, fault detection and isolation in chemical, biochemical or biological processes. In addition, soft sensor or virtual metrology techniques are considered. Both data-driven and model-based approaches are welcome. Applications to industrial processes (both continuous and batch) are especially welcome. Type: Oral keywords: Fault Detection, Process Control and Process Monitoring Abstract id# 363983 Visualization and Prediction of Operating Conditions of Blast Furnace

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Koji Hazama1, Koichi Fujiwara1, Manabu Kano1, Shinroku Matsuzaki2 and Akira Morita2, (1)Dept. of Systems Science, Kyoto University, Kyoto, Japan, (2)Nippon Steel & Sumitomo Metal Corporation, Kimitsu, Japan Abstract Text:

Abstracts:

o AIChE2014_Visualization_R1.docx (258.6KB) - Uploading Abstracts

Chair Joseph Scott Clemson University Chemical and Biomolecular Engineering Email: jks9@clemson.edu -- Will not be published Co-Chair Q. Peter He Associate Professor: Tuskegee University Chemical Engineering Tuskegee, AL 36088 Fax Number: 334-724-4188 Email: qhe@mytu.tuskegee.edu

26933: Design and Operations Under Uncertainty Sub Title: Although the problems associated with optimal design and operation of process systems under uncertainty have been receiving increasing attention since the early 1990s, there are still several key areas that needs to be addressed. This session invites contributions in developments of (i) novel approaches for quantification and propagation of uncertainty, (ii) new models, methods and algorithms to incorporate uncertainty, and (iii) novel approaches to solve the resulting problems in process synthesis, design and operations area. The papers that incorporate simulation and optimization are encouraged. Type: Oral keywords: Computational Methods, Plant Operations and Process Design Abstract id# 357459 A Framework for Stochastic Modelling and Optimisation of Chemical Engineering Processes

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Usman Abubakar, Srinivas Sriramula and Neill C. Renton, University of Aberdeen, Aberdeen, United Kingdom Abstract Text:

Abstracts:

o Abstract for AIChE-Nov-2014.pdf (204.5KB) - Uploading Abstracts

Abstract id# 357052 A Framework for Stochastic Process Performance Modelling and Optimization Usman Abubakar, Srinivas Sriramula and Neill C. Renton, University of Aberdeen, Aberdeen, United Kingdom Abstract id# 363577 Optimal Design of Chemical Processes with Chance Constraints Guennadi Ostrovsky1, Tatyana Lapteva2 and Nadir Ziyatdinov2, (1)Karpov Institute of Physical Chemistry, Moscow, Russia, (2)Institute of Automated Control Systems and Information Technologies, Kazan State Technological University, Kazan, Russia Abstract Text:

Abstracts:

o abstract_AIChEJ_2014.doc (268.0KB) - Uploading Abstracts

Chair Benoit Chachuat Senior Lecturer: Imperial College London South Kensington Campus Centre for Process Systems Engineering, Department of Chemical Engineering London, SW7 2AZ United Kingdom Email: b.chachuat@imperial.ac.uk Co-Chair Zukui Li Assistant Professor: University of Alberta Department of Chemical and Materials Engineering Edmonton, AB T6G2V4

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Canada Email: zukui@ualberta.ca Co-Chair Xiang Li Queen's University Chemical Engineering Kingston, ON K7L 3N6 Canada Email: xiang.li@chee.queensu.ca -- Will not be published Co-Chair Pahola Benavides VISHWAMITRA RESEARCH INSTITUTE Email: thathiana@vri-custom.org -- Will not be published

26934: Supply Chain Optimization Sub Title: Efficient management of supply chains has become a crucial component in the operations of a modern day enterprise. To remain competitive and profitable in a competitive environment, every organization must operate as a single intelligent enterprise, with every department collaborating toward common goals and objectives. Papers are invited in the general area of supply chain management that address the developments in the state-of-the-art of supply chain management solutions, elaborate on the challenges faced by the industry, and discuss future directions in this field. Areas of interest include (but are not limited to) enterprise resource planning, forecasting and demand management, supply chain network design and optimization, strategic and operational planning and scheduling. Application papers offering insights into the interplay between theory and practice in an enterprise setting are particularly encouraged. Type: Oral keywords: Computational Methods, Plant Operations and Supply Chains Chair Fengqi You Assistant Professor: Northwestern University 2145 Sheridan Road Department of Chemical and Biological Engineering Evanston, IL 60208-3120 Email: you@northwestern.edu Co-Chair Chrysanthos E. Gounaris Carnegie Mellon University Department of Chemical Engineering Email: gounaris@cmu.edu -- Will not be published

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Co-Chair Ruth Misener Royal Academy of Engineering Research Fellow: Imperial College South Kensington SW7 2AZ Department of Chemical Engineering London, United Kingdom Email: r.misener@imperial.ac.uk

26935: Synthesis and Design of Water Systems Sub Title: There is an increasing emphasis on the availability of adequate water supplies in the future. This session is focused on novel methods for synthesis and design of water systems that can address this growing problem through optimization, recycle, reuse, etc. Both theoretical and applied research contributions are welcome. Type: Oral keywords: Process Design, Resource Recovery and Sustainability Abstract id# 363026 A Simultaneous Optimization Approach for Synthesis and Design of Process and Water Networks Zainatul Bahiyah Handani, Department of Chemical and Biochemical Engineering, Computer Aided Process Engineering Center (CAPEC), Kongens Lyngby, Denmark, Alberto Quaglia, CAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark, Gürkan Sin, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark and Rafiqul Gani, CAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark Abstract Text:

Recently, significant efforts are being made to reduce freshwater consumption and wastewater generation in process industries. These efforts are regarded as critical concerns among industrial practitioners due to the rise of freshwater and effluent treatment costs and stringent regulations. One of the more efficient ways to reduce freshwater consumption in the process is by reusing wastewater that is generated by the process or utility after being treated in the wastewater treatment plant to acceptable limits. This can be done by considering various treatment technologies and exploring numbers of alternatives. Furthermore, different process technologies and design alternatives are also being considered in the processing network to transform raw materials into products while reducing freshwater consumption and wastewater generation. In order to accomplish this goal, many design alternatives can be considered. One of the techniques used to represent the design space and identify best technology network is superstructure-based optimization techniques [1, 2].

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Usually, in process synthesis and design, subsystems (e.g. water network and heat exchanger network) are dealt and solved sequentially or separately after process synthesis and design is performed and an optimal process flow sheet is identified. In this work, a simultaneous synthesis and design of process and wastewater networks or also known as a multi-network problem is presented. The integration of water consumption early at process synthesis and design stage is expected to reduce the consumption of freshwater and reusing more processed water. In this end, we address the challenge to manage the complexity of early-stage decision making in synthesizing and designing process and wastewater networks.

In order to address these challenges, a systematic framework recently developed by Quaglia et al. [1] is used as a basis and expanded use of the superstructure-based optimization approach for the optimal synthesis and design of processing plant network that is connected with a wastewater treatment network. A solution strategy to solve the multi-network problem accounts explicitly the interactions between the process and wastewater networks by selecting appropriate technologies and alternatives in order to convert raw materials into products and produce cleaned water to be reused in the process. The strategy

accounts explicitly for the interactions between the process and water networks (so called as a network

within a network) via selection of appropriate raw materials, technologies and alternatives for process and water treatment as well as products. The systematic approach is used to manage the difficulty and solving simultaneously process synthesis and water synthesis network problems with respect to economics, resources consumption and sustainability.

A new superstructure is formulated for the simultaneous synthesis of the process and water networks in order to achieve this task optimally and efficiently. All possible alternatives with respect to the topology of the process and wastewater network are represented in the superstructure at different process and treatment tasks. The resulting synthesis-design problem size is large and complex. The optimization problem is formulated as a Mixed Integer Non Linear Programming (MINLP) and solved in GAMS under different objective function scenarios (e.g. minimize total cost, minimize waste etc.) and constraints (e.g. effluent discharge limits, process pollutant limit etc.). The applicability of the systematic approach is demonstrated using a conceptual case study, especially developed to test all the features of the solution approach.

Reference:

[1] A. Quaglia, B. Sarup, G. Sin, R. Gani, 2012. Computers and Chemical Engineering, 38, 213.

[2] H. Yeomans, I.E. Grossmann, 1999. Computers and Chemical Engineering, 23, 709.

Chair Ajay Lakshmanan Sr. Manager: Aspen Technology, Inc. 200 Wheeler Road Burlington, MA 01803 Email: ajay.lakshmanan@aspentech.com Co-Chair Apurva Samudra Research Scientist: Rockwell Automation 9500 Arboretum Blvd STE 400 Advanced Technology Austin, TX 78759 Email: apsamudra@ra.rockwell.com

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27127: Tools for Chemical Product Design Sub Title: Designing new products is essential for the prosperity of the chemical industry. But how are these chemical products designed? How can they be designed better? We are interested in papers discussing computational, experimental or methodological tools that can help design these new products. Papers on tools and techniques used for teaching chemical product design are also most welcome. Type: Oral keywords: Computational Methods, Physical Properties, Polymers and Product Design Chair Kevin G. Joback President: Molecular Knowledge Systems, Inc. PO Box 10755 Bedford, NH 03110-0755 Fax Number: 603-472-5359 Email: kevin@molecularknowledge.com * Membership Number 0000091629 Co-Chair Arunprakash T. Karunanithi Assistant Professor: University of Colorado Denver Denver, CO 80217-3364 Email: arunprakash.karunanithi@ucdenver.edu -- Will not be published

27394: How Computing Has Changed Chemical Engineering - Session in Honor of Professor Larry Evans’ 80th Birthday Sub Title: Invited papers in honor of Larry Evans's 80th birthday. Type: Oral keywords: Computational Methods, Process Design and Process Simulation Chair Vladimir Mahalec Prof.: McMaster University

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1280 Main St West ITB 107 Chemical Engineering Hamilton, ON L8S 4K1 Canada Fax Number: 905-528-7901 Email: mahalec@mcmaster.ca Co-Chair Chau-Chyun Chen Professor: Texas Tech University Chemical Engineering Lubbock, TX 79409-3121 Email: chauchyun.chen@ttu.edu Biographical Sketch: Dr. Chau-Chyun Chen is a co-founder of Aspen Technology, Inc. Responsible for science and technology innovations in AspenTech’s process modeling business area, he is probably most well-known for his development of the electrolyte NRTL activity coefficient model widely used for modeling electrolyte solutions. He is the inventor of AspenTech’s electrolyte process modeling, polymer process modeling, and pharmaceutical solubility modeling capabilities. His recent focus areas include modeling of CO2 capture processes, modeling of renewable energy technologies, and pharmaceutical process modeling.

27511: Knowledge Management & Intelligent Systems for Safety (Invited Talks) Sub Title: This invited session is dedicated to the latest trends and developments in the area of knowledge management and intelligent systems for safety and risk assessment in chemical, oil&gas and processing industries at large. Type: Oral keywords: Computational Methods, Plant Operations, Process Safety and Regulations Chair Il Moon Yonsei University 134 shinchon-dong, Seodaemun-gu Department of Chemical Engineering Seoul, 120-749 South Korea Email: ilmoon@yonsei.ac.kr Co-Chair Rajagopalan Srinivasan Professor & Institute Chair: Indian Institute of Technology Gandhinagar

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Chandkheda, Visat-Gandhinagar Highway, Ahmedabad GJ-382424 Department of Chemical Engineering Gujarat, India Email: raj@iitgn.ac.in

27519: Ontology Engineering: Theory and Applications Sub Title: Ontology engineering in modern sense studies the methods and methodologies for building and applying ontologies for effective data, information and knowledge management across a broad range of applications in chemical, biochemical, pharmaceutical research, development and manufacturing. Papers are invited in the general area of ontology related applications in engineering, business and management. Topics include formal ontology design/modelling, ontology inference, matching, integration and alignment, ontology driven processes and design, as well as impact, relevance and new ontological concepts for respective areas. Type: Oral keywords: Information technology, Knowledge management and Ontology Chair Franjo Cecelja Professor: University of Surrey Process and Information Systems Engineering Research Centre Guildford, GU2 7XH United Kingdom Email: f.cecelja@surrey.ac.uk Co-Chair Elisabet Capon Swiss Federal Institute of Technology, Zurich (ETHZ) Vladimir-Prelog-Weg 1-5/10 Chemistry and Applied Biosciences, Institute for Chemical- and Bioengineering Zurich, 8093 Switzerland Email: elisabet.capon@chem.ethz.ch

27521: Data Consistency, Heterogeneity, Quality and Relevance Sub Title:

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Data collection constitutes a significant part of research and development efforts and responsible for significant amount of time and material resources. Data can be of many types including thermodynamic, kinetics, rate, equilibrium, etc. Data are often multidisciplinary and multidimensional and further complicated with various sources of uncertainties and measurement errors. In this session, we seek methodological research, tools development and contributions in the broader area of data consistency, coherence, quality assurance and validation to flag data quality before being used for its intended applications from process design, parameter estimation to process optimisation and feasibility evaluation. Type: Oral keywords: Chemical Reactions, Computational Methods and Management Chair Leo H. Chiang The Dow Chemical Company 2301 Brazosport Blvd., B1225 Analytical Tech Center Freeport, TX 77584 Email: HChiang@dow.com -- Will not be published Co-Chair Edrisi Muñoz Centro de Investigacion en Matematicas A.C. Mineral y Valenciana S/N, Guanajuato Information Technologies Guanaguato, Mexico Email: emunoz@cimat.mx Alternate Email: edrisi.munoz@upc.edu -- Will not be published

27554: Advances in Intelligent Systems Sub Title: Advances in intelligent systems seeks contributions that integrate ideas and methodologies from artificial intelligence, logical reasoning and applied statistics with those of chemical engineering sciences including operations research, estimation and control theory, monitoring. This session solicits abstracts with methodological research and applications on broad range of chemical engineering problems such as product design, process design, process operations monitoring, planning, scheduling, or control. Type: Oral keywords: Artificial intelligence, Computational Methods, Information theory and Intelligent systems Chair

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Gürkan Sin Associate professor: Technical University of Denmark Soltofts Plads, Building 227 Department of Chemical and Biochemical Engineering Kgs. Lyngby, Denmark Email: gsi@kt.dtu.dk -- Will not be published Co-Chair Jeevan Maddala Co-Founder & Lead Engineer: SYSENG, LLC Microfluidic Systems Lubbock, TX Email: jeevan@sysengresearch.com -- Will not be published

27556: Parallel Computing Sub Title: Parallel computing applications in chemical engineering and science Type: Oral keywords: Chemical engineering , Computational Methods and Energy Conservation and Efficiency Abstract id# 361489 Parallel Distillation Calculations Using Openmp Ronald W Bondy1, David Bluck1, David Van Peursem1, Srividya Gummadi1 and Rajkumar Vedam2, (1)Simulation Sciences, Schneider Electric, Lake Forest, CA, (2)Simulation Sciences, Schneider Electric, Houston, TX Abstract Text:

With current CPU speeds reaching a practical maximum value, CPU manufacturers are now creating CPU's with multiple cores to allow for parallel processing. However, most process design simulators do not take advantage of parallelism. This paper presents speedup results for solving rigorous distillation columns by Newton's Method using OPENMP. A new thermodynamic engine was developed and used here which allows for parallel computation of thermodynamic properties such as the fugacity coefficients, enthalpies and also the associated temperature and composition derivatives. Strategies to avoid cache misses and keep relevant data in the L1 and L2 caches are discussed. Results are presented for the speedups obtained by calculating the thermodynamic properties, equation residuals and jacobian elements in parallel. The systems studied are rigorous superfractionator and demethanizer distillation columns.

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Chair Bri-Mathias S. Hodge National Renewable Energy Laboratory 15013 Denver West Parkway MS ESIF 200 Golden, CO 80401 Email: bri-mathias.hodge@nrel.gov -- Will not be published Co-Chair Pradeep Suresh Assoc. Research Scientist: Dow Chemical Company 2301 N. Brazosport Blvd Engineering & Process Sciences, Core R&D Freeport, TX Email: pradeep.suresh@dow.com

27583: In Honor of Ignacio Grossmann's 65th Birthday Sub Title: This session of invited papers is in honor of Prof. Ignacio Grossmann's contributions to process systems engineering research, education and service on the occasion of his 65th birthday. Type: Oral keywords: Computational Methods, Optimization and Process Design Chair Christodoulos A. Floudas Princeton University Chemical and Biological Engineering Princeton, NJ 08544 Email: floudas@titan.princeton.edu Co-Chair Efstratios N. Pistikopoulos Prof.: Imperial College London Biological Systems Engineering Laboratory, Department of Chemical Engineering London, United Kingdom Email: e.pistikopoulos@imperial.ac.uk -- Will not be published

27584: Design of Renewable Systems

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Sub Title: This session emphasizes recent advancements in Design of Renewable Systems. Type: Oral keywords: Alternative Energy/Fuels, Process Design and Sustainability Chair K. V. Camarda University of Kansas Department of Chemical and Petroleum Engineering Lawrence, KS 66047 Email: camarda@ku.edu -- Will not be published Co-Chair Ramkumar Karuppiah Corporate Strategic Research, ExxonMobil Research and Engineering Annandale, NJ 08801 Email: ramkumar.karuppiah@exxonmobil.com -- Will not be published

27585: Design of CO2 Capture Systems Sub Title: This session emphasizes recent advancements in Design of CO2 Capture Systems. Type: Oral keywords: Global Warming, Process Design and Sustainability Chair Nishanth G. Chemmangattuvalappil Associate Professor: University of Nottingham - Malaysia Jalan Broga Department of Chemical and Environmental Engineering Semenyih, Malaysia Fax Number: 60389248017 Email: nishanth.c@nottingham.edu.my Co-Chair Athanasios I. Papadopoulos Associate Researcher: Centre for Research and Technology-Hellas Chemical Process and Energy Resources Institute Thessaloniki, 57001 Greece Fax Number: +302310498130 Email: spapadopoulos@cperi.certh.gr

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27644: Modeling and Control of Crystallization Sub Title: This session focuses on the control of crystallization process and the development of crystallization processes using modeling. Type: Oral keywords: Process Control, Separations and crystallization Chair Zoltan K. Nagy Professor of Chemical Engineering: Purdue University 480 Stadium Mall Drive Forney Hall G027 Chemical Engineering West Lafayette, IN IN 47907-2100 Email: zknagy@purdue.edu -- Will not be published

27649: Control for Smart Manufacturing Sub Title: Smart manufacturing is broad grouping of activities around integrating and using data from different levels in a manufacturing environment from the level of individual sensors up through the supply chain. This session invites papers in the general area of smart manufacturing and the utilization of smart manufacturing infrastructure for improved control strategies. Type: Oral keywords: Manufacturing Intelligence, Process Control and Smart Manufacturing Chair Panagiotis D. Christofides Professor: University of California, Los Angeles Department of Chemical and Biomolecular Engineering Los Angeles, CA 90095 Fax Number: 310-206-4107 Email: pdc@seas.ucla.edu Co-Chair Q. Peter He Associate Professor:

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Tuskegee University Chemical Engineering Tuskegee, AL 36088 Fax Number: 334-724-4188 Email: qhe@mytu.tuskegee.edu

27650: Modeling and Control for Small and Multiscale Processes Sub Title: This session focuses on the modeling of phenomena at microscopic length scale (including multiscale modeling) and their control to desired behavior using feedback control systems. Type: Oral keywords: Dynamic Modeling, Feedback Control, Multiscale Modeling and Process Control Co-Chair Michael Baldea Assistant Professor: University of Texas 200 E Dean Keeton St. Stop C0400 Chemical Engineering Austin, TX 78712-1589 Email: mbaldea@che.utexas.edu -- Will not be published

27651: Economics and Process Control Sub Title: This session focuses on the representation and incorporation of economic objectives into feedback control strategies. Topics on representing economic objectives, economic based controller formulations, performance of strategies incorporating economics, and the interplay between competing control objectives are considered. Papers offering insights into other aspects of economics based controls are also encouraged. Type: Oral keywords: Economic Model Predictive Control, Economics Based Control, Economics Based Dynamic Optimization and Process Control Chair John D. Hedengren Assistant Professor: Brigham Young University 350 Clyde Building

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Brigham Young University Chemical Engineering Provo, UT 84602 Email: john.hedengren@byu.edu Co-Chair Fernando V. Lima Assistant Professor: West Virginia University 437 Engineering Sciences Building Department of Chemical Engineering Morgantown, WV 26506 Fax Number: 304-293-4139 Email: Fernando.Lima@mail.wvu.edu

27711: Mathematical Approaches for Systems Biology Sub Title: This session is devoted to theoretical and computational studies of biological systems. Such systems include protein/DNA interactions, protein/protein interactions, genetic networks, metabolic pathways, and signal transduction pathways. Presentations will focus on theoretical and computational developments for the study of the behavior of these systems in response to stimulation or changes in their kinetic and thermodynamic properties. Contributions of novel computational strategies for systems biology are especially welcome. Type: Oral keywords: Biological Engineering, Computational Methods, Mathematical methods and life science Chair Rajanikanth Vadigepalli Associate Professor: Thomas Jefferson University 1020 Locust St Room 314 Daniel Baugh Institute for Functional Genomics and Computational Biology, Department of Pathology, Anatomy, and Cell Biology Philadelphia, PA 19107 Fax Number: 215-503-2636 Email: Rajanikanth.Vadigepalli@jefferson.edu -- Will not be published Biographical Sketch: Rajanikanth Vadigepalli received his Bachelor's and Doctorate degrees in Chemical Engineering from Indian Institute of Technology-Madras and University of Delaware, respectively. He is currently an Assistant Professor in the Department of Pathology, Anatomy and Cell Biology, at Thomas Jefferson University, Philadelphia, PA, USA. Work in Dr. Vadigepalli's lab is directed at understanding the regulatory network dynamics driving the intra- and inter-cellular adaptive processes in

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mammalian pathophysiology. Though cross-disciplinary collaborative projects, his lab studies signaling and gene regulatory networks in: central cardiorespiratory control circuits adversely adapted in hypertension, alcohol toxicity on liver repair and regeneration, and abnormal stem cell differentiation in the context of fetal alcohol spectrum disorders. Dr. Vadigepalli's lab also focuses on developing advanced computational modeling, systems engineering and bioinformatics approaches to complement high-throughout biological experimentation. Co-Chair Yonghyun (John) Kim Assistant Professor: The University of Alabama Box 870203 Department of Chemical and Biological Engineering Tuscaloosa, AL 35487 Fax Number: 2053487558 Email: ykim@eng.ua.edu

27715: Quantitative Approaches to Disease Mechanisms & Therapies Sub Title: This session highlights applying conventional – e.g., reaction and transport phenomena – and emerging – e.g., dynamical systems and statistical mechanics – concepts from (bio)chemical engineering to aid in understanding disease mechanisms and designing therapies. Submissions that integrate experimental work with simulation and that demonstrate a clear connection between engineering fundamentals and disease pathophysiology are particularly encouraged. Type: Oral keywords: Biological Engineering, Disease models and Drug development and discovery Co-Chair Matthew J. Lazzara University of Pennsylvania 311A Towne Building 220 South 33rd Street Chemical and Biomolecular Engineering Philadelphia, PA 19104-6393 Email: mlazzara@seas.upenn.edu -- Will not be published Co-Chair Stacey Finley Assistant Professor: University of Southern California Biomedical Engineering Email: sfinley@usc.edu -- Will not be published

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28234: How Computing Has Changed Chemical Engineering - Session in Honor of Professor Larry Evans’ 80th Birthday II Sub Title: Invited papers in honor of Larry Evans's 80th birthday. Type: Oral keywords: Computational Methods, Process Design and Process Simulation Chair Vladimir Mahalec Prof.: McMaster University 1280 Main St West ITB 107 Chemical Engineering Hamilton, ON L8S 4K1 Canada Fax Number: 905-528-7901 Email: mahalec@mcmaster.ca Co-Chair Chau-Chyun Chen Vice President of Technology: Aspen Technology, Inc. 200 Wheeler Road R&D Burlington, MA 01803 Email: chauchyun.chen@gmail.com -- Will not be published Biographical Sketch: Dr. Chau-Chyun Chen is a co-founder of Aspen Technology, Inc. Responsible for science and technology innovations in AspenTech’s process modeling business area, he is probably most well-known for his development of the electrolyte NRTL activity coefficient model widely used for modeling electrolyte solutions. He is the inventor of AspenTech’s electrolyte process modeling, polymer process modeling, and pharmaceutical solubility modeling capabilities. His recent focus areas include modeling of CO2 capture processes, modeling of renewable energy technologies, and pharmaceutical process modeling.

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