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Computers and Chemical Engineering 50 (2013) 8– 27

Contents lists available at SciVerse ScienceDirect

Computers and Chemical Engineering

jou rn al h om epa ge: w ww.elsev ier .com/ locate /compchemeng

ustainPro—A tool for systematic process analysis, generation and evaluation ofustainable design alternatives

na Carvalhoa,∗, Henrique A. Matosb, Rafiqul Ganic

CEG-IST, Instituto Superior Técnico, UTL, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCPQ/DEQ, Instituto Superior Técnico, UTL, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCAPEC, Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800 Lyngby, Denmark

r t i c l e i n f o

rticle history:eceived 2 July 2012eceived in revised form2 November 2012ccepted 19 November 2012vailable online 28 November 2012

a b s t r a c t

Chemical processes are continuously facing challenges from the demands of the global market relatedto economics, environment and social issues. This paper presents the development of a software tool(SustainPro) and its application to chemical processes operating in batch or continuous modes. Thesoftware tool is based on the implementation of an extended systematic methodology for sustainableprocess design (Carvalho, Matos, & Gani, 2008, 2009). Using process information/data such as the pro-

eywords:rocess retrofittingife cycle assessmentconomic analysis

cess flowsheet, the associated mass/energy balance data and the cost data, SustainPro guides the userthrough the necessary steps according to work-flow of the implemented methodology. At the end thedesign alternatives, are evaluated using environmental impact assessment tools and safety indices. Theextended features of the methodology incorporate life cycle assessment analysis and economic analysis.The application and the main features of SustainPro are illustrated through a case study of �-galactosidase

oftware production.

. Introduction

Climate change is currently a severe problem affecting the qual-ty of life of the modern society. It demands innovative solutionshat will help to control the current (increased) negative impacts, sos to avoid uncontrollable future consequences. Therefore, reduc-ion of the environmental impacts caused by the production ofoods, without compromising the actual living standards needso be considered. This need to connect climate change issues andustainable development has been proposed, for example, in thehird Assessment Report (TAR) of the Intergovernmental Panel onlimate Change (IPCC), which has suggested that sustainable devel-pment may be the most effective way to frame the mitigationuestion (Swart, Robinson, & Cohen, 2003). Retrofit design is listeds a key issue for sustainable development.

Retrofit design represents design problems constrained by theeed to use a sub-set of existing equipment and/or operationsut find process alternatives that are better than the existingne. In this way retrofit analysis/design can be complex because

f the additional constraints. Various methods have been devel-ped in order to evaluate and reduce the environmental impactf chemical processes. Cabezas, Bare, and Mallick (1999) devel-ped a waste reduction algorithm (WAR) where the potentialnvironmental impact of a chemical process is judged in terms of

∗ Corresponding author.E-mail address: anacarvalho@ist.utl.pt (A. Carvalho).

098-1354/$ – see front matter © 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.compchemeng.2012.11.007

© 2012 Elsevier Ltd. All rights reserved.

a set of property-based indicators. Although very useful to eval-uate process alternatives, this approach does not generate newalternatives. Sun, Pan, and Wang (2008) proposed the formulationof a multi-objective optimization problem to determine sustainablechemical process designs taking into account economic, environ-mental and societal aspects. Ponce-Ortega, Mosqueda-Jiménez,Serna-González, Jiménez-Gutiérrez, and El-Halwagi (2011) presenta multi-objective optimization model for the recycle and reusenetworks based on properties while accounting for the envi-ronmental implications of the discharged wastes using life-cycleassessment. These approaches need mathematical models to rep-resent all process alternatives, thereby making their applicationdifficult and time-consuming. Halim and Srinivasan (2002a, 2002b,2002c) developed a systematic methodology to guide users wish-ing to achieve waste minimization. The methodology determinesthe origins of waste in any process and through a set of rulesbased on process insights suggests process modifications for wastereduction. El-Halwagi (2012) presents the main concepts and appli-cations of sustainable design through process integration. Carvalho,Matos, and Gani (2008) developed a systematic methodology togenerate and evaluate sustainable design alternatives. The method-ology determines a set of mass and energy indicators from steadystate process data, establishes the operational and design tar-

gets, and through a sensitivity analysis, identifies the processalternatives that match the (design) targets.

These methodologies, while very useful, are not generic enoughand for their application, a number of additional methods and

A. Carvalho et al. / Computers and Che

Nomenclature

AF accumulation factorAP accumulation-pathsBI batch indicatorsC compoundCP compound propertiesDC demand costEAF energy accumulation factorECP energy closed-pathsEOP energy open-pathsEWC energy and waste costISA indicator sensitivity analysisISI total inherent safety indexLCA life cycle assessmentMCP mass closed-pathsMOP mass open-pathsMVA Material Value AddedND new designP processPD process dataRQ reaction qualityS streamsSA sensitivity analysisSI safety indexSM sustainability metricsSP stream propertiesTDC total demand costTVA total value addedU units

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UP units propertiesWAR waste reduction algorithm

ools and their related data, are needed. Consequently, usinghe advances in computer science and computational algo-ithms for process analysis, it becomes advantageous to employomputer-aided modeling systems and tools for integrated pro-ess retrofitting analysis. These computational tools make theetrofitting process relatively easier while providing a more accu-ate and systematic process analysis. The software tools can beivided into two groups: those that evaluate process performance

n terms of sustainability, life cycle assessment and environmentalmpact, and those that determine new design alternatives in ordero reduce the environmental impact.

Examples of software belonging to the first group are:

GaBi (GaBi Software (2012)): a software that includes tools anddatabases for product and process sustainability analysis, lifecycle assessment (LCA), carbon footprint calculation and Green-house analysis.SimaPro is a software to perform LCA. This software was devel-oped by Product Ecology Consultants – PRé (2012). SimaPro comesfully integrated with the well-known Eco-Invent database andis used for a variety of applications, like carbon footprint cal-culation, product design and eco-design, environmental productdeclarations (EPD), environmental impact of products or servicesand environmental reporting (GRI).BRIDGESworks Metrics (BRIDGESwork Metrics Software (2004))is a metrics management software tool that identifies key sus-tainability indicators and offers a variety of metrics for measuringsustainability (performance).

FLASCTM (Fast Life cycle Assessment of Synthetic Chemistry) wasdeveloped by Curzons, Jiménez-González, Duncan, Constable,and Cunningham (2007). This tool was developed from a detailedassessment of the cradle-to-gate life cycle environmental impacts

mical Engineering 50 (2013) 8– 27 9

associated with the manufacture of materials used in a typicalpharmaceutical process.

• TRACI is a tool for the reduction and assessment of chemical andother environmental impacts that has been developed by U.S. EPA(Bare, Norris, Pennington, & McKone, 2003). This tool was devel-oped to assist in impact assessment for sustainability metrics, lifecycle assessment, industrial ecology, process design, and pollu-tion prevention.

Examples of developed software belonging to the second groupare:

• AquoMin (Relvas, Matos, Fernandes, Castro, & Nunes, 2008) is asoftware tool dealing with water and wastewater minimization.This software was developed to study the problem of wastewa-ter minimization in a set of mass-exchange operations and thesubsequent distributed effluent system.

• ENVOPExpert (Halim and Srinivasan, 2002a, 2002b, 2002c) is anexpert system that, given the information concerning the processin the form of a flowsheet, process chemistry, and material infor-mation, can automatically detect the waste components in theprocess, diagnose the sources of their origin, and suggest intel-ligent design alternatives (heuristic) to eliminate or minimizethem.

• Kazantzi, Qin, El-Halwagi, Eljack, and Eden (2007) presented anew graphical approach for simultaneous process and moleculardesign, in which elements from both areas can be considered atthe same time. The proposed methodology provides a consistentset of property-based visualization tools that are applicable forthe process- and molecular-design tasks.

• DESASS (Ferrer et al., 2008) has been developed to design, sim-ulate and optimize wastewater treatment plants. The softwareallows the simulation of the most important physical, chemicaland biological processes.

From the above discussion it can be noted that, there are nowavailable a number of software with varying degrees of capabil-ities required for sustainable (retrofit) process design. The abovesoftware is related to specific issues, such as environment impactassessment, water reduction and waste reduction. They focusmainly on the characterization of the process. Thus, there is a needfor a software that combines process design with these specificissues so that existing processes or new process designs can be eval-uated for sustainable alternatives taking into account economic,environmental and safety issues in a generic manner. The new inte-grated software therefore should be generic enough to handle awide range of chemical processes operating in continuous and/orbatch modes and be able to characterize the alternatives in terms ofthe important issues (such as economic, environmental and safety),so that new (retrofit) alternatives that improve the overall sustaina-bility of the process can be identified. The objective of this paper isto present the new generic software, SustainPro, which allows theanalysis and the generation of process alternatives that are moresustainable compared to a reference (base case) design. It can beapplied to improve the performance of new processes and/or exist-ing processes. SustainPro is based on the work-flow, data-flow andcalculations corresponding to the methodology developed first forcontinuous processes by Carvalho et al. (2008) and then extendedto (Carvalho, Matos, & Gani, 2009). SustainPro is able to gener-ate, screen and then identify sustainable alternatives in chemicalprocesses (new or old) by locating economic, operational, environ-mental, and safety related bottlenecks inherent in the process. In

order to evaluate the generated alternatives it employs a set ofperformance criteria. These performance criteria include the sus-tainability metrics from Azapagic (2002) and the safety indicesproposed by Heikkilä (1999).

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The objective of this paper is also to present the SustainPro soft-are together with the new extensions of the methodology, whereow life cycle assessment analysis, using a software called LCSoft,nd economic analysis, using a software called ECON, are includedor use as parameters in the assessment of design alternatives. Thepplication of SustainPro is highlighted as a case study involvinghe production of �-galactosidase. For each step of the applicationxample, the data-flow and work-flow implemented in SustainProre also highlighted.

. Framework – implementation of SustainPro

Retrofit requires analysis of the process for the identification ofhe bottleneck points with subsequent generation of new designlternatives. Carvalho et al. (2008, 2009) proposed a stepwiseethodology to generate and evaluate new design alternatives

or a more sustainable process. This methodology is applicable forontinuous, semi-batch and batch processes. The starting pointor the design methodology is the process specification in termsf prices, conditions of operation and the corresponding processowsheet (for continuous processes) and the sequence of opera-ions (for batch processes). A knowledge base and some externalools have been used for the application of the design method-logy. For converting this methodology into a software, first aramework for inclusion of the steps of the methodology has beeneveloped (see Fig. 1). As shown in Fig. 1, the general supportingools, which include the knowledge base and external tools, arentegrated within a general user interface. The system has built-n flexibility to either use the in-house knowledge base tool andelated supporting tools, or, user-specific supporting tools.

As shown in Fig. 1, the starting point for new problems is torovide the process specifications, followed by the creation ofroblem specific data, which follows the methodology proposedy Carvalho et al. (2008, 2009). The software is divided into threearts, Part I – indicator analysis, Part II – evaluation and Part III –

eneration and comparison of new alternatives, that can be usedogether with Parts I and II, or used separately. For already existingase studies involving specific processes (saved earlier), Part IIIan be used directly to generate and analyze new alternatives.

Fig. 1. Overview of the framework for implementa

mical Engineering 50 (2013) 8– 27

Each part generates a corresponding output file containing theresults. These output files can be used on their own, after a case hasbeen analyzed or for a new solution of the problem.

2.1. General supporting tools

The supporting tools shown in Fig. 1 are briefly described in thissection.

2.1.1. SimulatorsProcess simulators provide mass and the energy balance related

data for SustainPro. In principal, any process simulator such as,Pro II, AspenTech, HYSYS, ICAS-Simulator (Gani, Hytoft, Jaksland, &Jens, 1997), gPROMS and SuperPro Designer can be used for thispurpose. The inputs for the simulators are the current processdesign and their corresponding process specifications. These toolsgive as output the mass and the energy balances in the form ofmass and energy flows and the corresponding stream composi-tions, temperatures, and pressures. The simulators may also be usedto evaluate/validate the generated design alternatives suggestedby SustainPro. SustainPro is able to read the mass and the energybalances data from an Excel file generated by the simulators.

2.1.2. CAPEC databaseTo calculate the mass and energy indicators (as defined by

Carvalho et al. (2008)), as well as the batch indicators, severalcompound properties, such as heat capacity, density, heat ofvaporization, are needed. The CAPEC database (Nielsen, Abildskov,Harper, Papaeconomou, & Gani, 2001), contains pure compounddata for nearly 13,000 chemicals and mixture properties data formainly binary (organic) mixtures and some ternary mixtures. Con-sequently, this tool is used to supply the required data.

2.1.3. ProPredProPred (Marrero & Gani, 2001) is a toolbox for estimation of

pure component properties of organic compounds. When purecomponent property data is not available in the CAPEC database,ProPred might be used to predict the missing data. The molecularstructural information is given to ProPred as input data.

tion of the sustainable design methodology.

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.1.4. LCSoftLCSoft performs the life cycle assessment, using US-EPA and IPCC

mission factors to calculate the environmental impact for a givenrocess. This tool is available in an Excel sheet and is used to assesshe environmental impact between the base case design and theew design alternative proposed by SustainPro.

.1.5. ProCAMDProCAMD is based on the hybrid methodology for Computer

ided Molecular Design developed by (Harper & Gani, 2000). When target improvement is related to the reduction of the flowrate of solvent, the use of another solvent may be considered as a designlternative. ProCAMD is used to find a suitable replacement solventhat improves sustainability.

.1.6. CAPSSThe CAPSS tool is based on the methodology developed by

aksland, Gani, and Lien (1995), and D’Anterroches and Gani (2005)

hich employs physicochemical properties and their relationships

o separation techniques for design and synthesis of separationrocesses. This tool is available in ICAS and is used to generateew design alternatives after SustainPro retrofit analysis.

able 1ummary of the interaction of SustainPro with the supporting tools.

Tools Purpose

Simulators Generate mass and energy balances

CAPEC database Compound properties

ProPred Property prediction

LCSoft Environmental parameters

ProCAMD Solvent selection

CAPSS Separation technique Selection

ECON Economic analysis

mical Engineering 50 (2013) 8– 27 11

2.1.7. ECONECON performs the cost calculations based on the cost model

given in “Plant Design and Economics for Chemical Engineers”(Peters, Timmerhaus, & West, 2004). This tool is available in anExcel sheet and is used for economic analysis of the new designalternatives after SustainPro retrofit analysis.

The interactions between SustainPro and the supporting toolsare summarized in Table 1, which also gives the required inputfor each of the tools and the corresponding retrieved output toSustainPro.

2.2. SustainPro – knowledge base

2.2.1. Knowledge baseThe objective of the knowledge base (KB) facility in SustainPro

(called SKB) is to store data of processes/compounds that have beenstudied previously. The advantage of creating a knowledge base isthat it provides the user an opportunity to modify an analysis thathas already been done without having to start as a new problem.Therefore, less time is consumed searching for properties that were

already determined before for other analyses.

The current knowledge base contains saved data correspondingto several previously solved problems, such as, VCM production,MTBE production, HDA production, ammonia production, biodiesel

Interaction with SustainPro

12 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

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roduction, among others, that were already studied through theethodology. The structure of the SKB is such that users are able to

reate and update their own versions of the knowledge base. TheKB is divided into two levels of information. The first level containsnformation about the process while the second level contains databout the compounds.

.2.1.1. SKB – first level. The data in SKB is organized accordingo a knowledge representation scheme and ontology. The processame, the process units, the unit properties, the process streams,he streams properties and the process data are the main cate-ories of data in the first level (see Fig. 2) of SKB. The processesre listed as the first category. The independent processes are thebjects of this category (e.g. VCM production, MTBE production,DA production, etc.). For each process (e.g. the object of the firstategory) the next (second) category of data, is divided into threeub-categories: units (category 2.1), streams (category 2.2) androcess data (category 2.3). In category 2.1, all the units involved

n the process are listed. For each process there is a connectionith the units present in that process. A third category, 2.1.1, is

ntroduced in order to store the properties related to each pro-ess unit. Here, the type of unit is specified, the heat exchangedn each unit, the reactions taking place in the unit, the type oftility used by that unit (when required) and the utility price aretored. In category 2.2, the streams within the process are listed.or each process, there is also a list of streams related to thativen process. In the third category, 2.2.1, the properties related

o each stream are stored. Pressure, temperature, compounds andhe respective flowrates are specified in this third category. In cat-gory 2.3, the general information related to each process is listed,uch as working hours per year, layout description, construction

n scheme in SKB (level 1).

material of the equipment. In this category information about theoperation time and the inherently process safety parameters arespecified.

2.2.1.2. SKB’s second level. The compounds are the first category ofthe SKB’s second level and the properties of the compounds are thesecond category of this knowledge base level (see Fig. 3). For eachcompound listed in the first category, a set of properties, such as,molecular weight, heat capacity, density, enthalpy of vaporization,price, flash point, boiling point, upper explosive limit, lower explo-sive limit and toxic limit are stored. The SKB allows the storage ofcompounds and their properties even if a process in study doesnot use them or vice versa. Therefore, when the user applies themethodology to a process, which was not analyzed before (meansnot available in the SKB), the user is still able to import informationrelated to the compounds that make part of the new process and areavailable in the SKB. This avoids extra work, on finding propertiesof compounds that are already saved in the SKB.

The structure of the SKB is generic and can easily be extendedto increase the range of applications. Horizontal extension (addingmore columns of data) means simply addition of more categoriesin the SKB while the vertical extension (adding more rows of data)means addition of more objects to the existing categories. Additionof new compound properties is an example of horizontal extensionwhile the addition of new processes in the SKB is an example of thevertical extension of the SKB.

2.3. ECON – economic analysis tool

The ECON software was developed in visual basic for appli-cations related to economic analysis (Saengwirun, 2011). ECON

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analysis, Part II – evaluation and Part III – generation and compari-

Fig. 3. Knowledge representation scheme of SKB (level 2).

ontains 7 sections, equipment cost calculation, capital cost cal-ulation, operating cost calculation, economic analysis, PIE chartnalysis, sensitivity analysis and alternative comparison. The costalculations in ECON are based on the cost model given in “Plantesign and Economics for Chemical Engineers” (Peters et al., 2004).n overview of ECON software architecture is presented in Fig. 4 and

he activity diagram that highlights the work flow and data flow isresented in Fig. 5.

The ECON software will allow the comparison between thease case and the new design alternatives suggested by Sustain-

ro. With this software it is possible to evaluate the economic costsn terms of investment return, operational costs and capital costs,

hich is very useful when taking long term strategic decisions for

Fig. 4. ECON software architect

mical Engineering 50 (2013) 8– 27 13

implementing retrofit suggestions. The new alternative investmentcan be assessed by the results given by ECON, such as the Net PresentValue, Pay Back Time and Return Rate. ECON is a good complementto any industrial decision-maker, who has to decide to either takethe risk of a new investment or not, appearing as a useful decisionsupport tool.

2.4. LCSoft – life cycle assessment

The LCSoft software was developed in visual basic for applica-tions related to life cycle assessment (LCA) analysis. LCSoft contains4 main sections, data collection, life cycle inventory, carbon foot-print calculations and impact assessment (Piyarak, 2012). Dataabout the mass balance, energy consumption, utilities as well as thefuel consumptions needs to be provided by the user. Based on thesevalues the software calculates the carbon footprint, the inventoriesand the impact assessment factors. An overview of LCSoft activitydiagram that highlights the work flow and data flow is presentedin Fig. 6.

3. SustainPro – overview

An overview of the SustainPro software is shown in Fig. 7a andb. The start menu interface allows the user to import data from analready solved example, previously saved on the knowledge base(see Fig. 7a, left down) or guides the user to the general interfaceused for the creation of a new problem (see Fig. 7a, left up). Thesolved problems are stored in the SKB section of the software forfuture access/applications. Through this option (see Fig. 7a, left up),stored/solved case studies can be accessed and/or modified and canbe used directly if a case study satisfies current user requirements.As shown in Fig. 7a (right up), for creating the problem interfacethe user has to specify the operation mode for the process in study(continuous or batch). After the start menu has been completed theMain Menu is displayed (see Fig. 7b). The Main Menu presents thework-flow of the methodology described in Carvalho et al. (2008,2009). The Main Menu is divided into three parts: Part I – indicator

son of new alternative. To solve a sustainable design problem (whichmeans apply the methodology described by Carvalho et al. (2008,2009)), the user needs to perform sequentially, Part I, Part II and

ure (Saengwirun, 2011).

14 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

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Fig. 5. Activity diagram

art III. A built-in color coded system guides the user through theifferent steps of the activity-flow. The user must follow the but-on highlighted in “orange”, which is the next step to be performed.he light blue color button indicates the already performed stepsnd the dark blue buttons indicate the steps that have not yet been

erformed.

If the user only wants to generate a new design alternative, Part and Part III need to be executed. Part I and Part III combine toorm the retrofit analysis. Part II alone calculates the sustainability

N (Saengwirun, 2011).

metrics, the safety indices, the LCA analysis and the economic anal-ysis for a given process. Part II is called the performance analysistool. Part III alone is used to generate new design alternatives for aspecific problem.

The SustainPro interface guides the user through instruc-

tions/provides help with each step and allows extension ofsupporting tools, SKB. The VBA (visual basic for applica-tions) programming language with Excel interface is used inSustainPro.

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 15

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.1. SustainPro – architecture

The architecture of the SustainPro software is illustrated in Fig. 8.he figure shows the work-flow (shown with solid lines) and data-ow (shown with dashed lines) through an “activity” diagram. Theupporting tools needed in each design step are also shown inig. 8 (black boxes). In the activity-diagram the boxes designated asnput-Data are the overall input for the software. The boxes defineds Output-Data are results obtained by the software application.ote, however, that these output data can also serve as input toownstream calculation.

The detailed description of the procedure to build a new archi-ecture for development of any software is available in the literatureBayer, Eggersmann, Gani, & Schneider, 2002). As shown in Fig. 8,he objective of step 1 of the SustainPro design procedure is to col-ect all data required – mass and energy balances and prices of

aterials and costs of associated operations. The data can be givens simulation results from a process simulator or as collected plantata.

For batch processes an additional step where the equipmentowsheet is transformed to an operational flow-diagram (Carvalhot al., 2009) is necessary. Both steps are necessary to provide inputso the system. The SKB might contain the required information fortep 1. In that case the information can be imported directly. Theata collected in step 1 is the input for step 2, which performs theowsheet decomposition and consequently generates the list ofpen-, closed-paths (Carvalho et al., 2008) and accumulation-pathCarvalho et al., 2009).

The input data as well as the results of step 2 will subsequentlyct as the input for step 3, with the help of three supportingools (CAPEC database, ProPred and PA-WAR). The output of step

consists of a set of indicators (three energy indicators, five massndicators (Carvalho et al., 2008), and batch indicators (Carvalhot al., 2009)).

Step 4 use as input the indicators determined on the step 3.n the fourth step a sensitivity analysis is performed, followingn indicator sensitivity analysis (ISA) algorithm (Carvalho et al.,008). This algorithm determines the set of target indicators for

mprovements. This set of selected indicators is the input for step. In step 5, a sensitivity analysis of the operational parameters that

nfluence the target indicators is performed (Carvalho et al., 2008).

he operational parameter that allows the highest improvementn the target indicator is selected. The target indicators are thenhe input for step 6. Using the help of two supporting tools (Pro-amd and CAPSS) a new design alternative is generated. The new

ram of LCSoft.

design alternative is evaluated through the performance criteria,which might use the sustainability metrics (Azapagic, 2002) andthe safety indices (Heikkilä, 1999), which are calculated directlyby SustainPro. The economic analysis and the LCA analysis areperformed by the external tools, ECON and LCSoft. If the new alter-native improves or maintains constant the performance criteriaparameters than it is accepted as a sustainable design alternative,otherwise, the design alternative is rejected and a new alternativemust be searched (Carvalho et al., 2008). The design alternativesgenerated by SustainPro usually respect that criterion, since whenan indicator is improved the correspondent sustainability issuesare also improved.

4. Case study – �-galactosidase (b-Gal) production

Application of the general features of SustainPro is illustratedthrough a case study related to the production of �-galactosidase(b-Gal). This process operates in the batch mode and has been cho-sen because in addition to some of the steps needed only for batchoperations, it also involves steps that are needed for continuousprocesses. Detailed solutions for a number of other solved casestudies can be obtained from the corresponding author.

This case study involves the production process of �-galactosidase (b-Gal), an intracellular enzyme produced byEscherichia coli. This enzyme (b-Gal) is normally produced by theE. coli by an amount up to 1–2% of total cell, although, using geneticengineering the level can go up to 20–25%. b-Gal is mainly used inthe production of cheese whey. Lactose tolerance is a problem withrespect to milk-based products, that is, some people are not able todigest milk or milk products. Production of lactose-free milk prod-ucts (using b-Gal reactors) therefore allows everybody to digestthese products. The flowsheet for the �-galactosidase productionprocess is shown in Fig. 9.

4.1. Flowsheet sections

The �-galactosidase production flowsheet can be divided intothree sections: (1) fermentation, (2) primary recovery, and (3)purification (see Fig. 9).

(1) Fermentation section

Here the E. coli. cells are used to produce the �-galactosidase(b-Gal), through a fermentation process. The fermentation process

16 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

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ig. 7. (a) Start menu interface – SustainPro. (b) SustainPro overview. (For interpretarticle.)

onsists of four operations: the charge, the reaction, the dischargend the clean.

2) Primary recovery section

The first step of the primary recovery section is cell harvest-ng to reduce the volume of the broth and to remove extracellular

f the references to color in the text, the reader is referred to the web version of the

impurities. Since �-galactosidase is an intracellular product, thenext step is cell disruption, performed in a high-pressure homoge-

nizer. After homogenization, a centrifuge is used to remove most ofthe cell debris. A dead-end polishing filter removes the remainingcell debris. The resulting protein solution is concentrated by anultra-filter.

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 17

icator-

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to an operational flowdiagram (see Fig. 11).

Fig. 8. Activity-diagram of the ind

3) Purification section

Next, the product stream is purified by an ion exchange chro-atography column, after which, it is concentrated by a second

ltra-filtration unit and polished by a gel filtration unit.

.2. Application of SustainPro

Step 1: Collect the steady state data

To apply SustainPro, a simulation of the process (b-Gal produc-ion) was obtained from SuperPro Designer. The mass and energyalances were taken from the simulation results obtained fromuperPro Designer (2009) library. All the prices necessary to the

based methodology in SustainPro.

indicators calculation were also obtained from SuperPro Designer(2009). The simulation results and the prices are the input data forstep 1 of SustainPro analysis (see Fig. 10).

Step 1A: Transform equipment flowsheet in an operational flow-sheet

The equipment flowsheet consists of 20 units, as shown inFig. 12. To apply the algorithm, the flowsheet needs to be connected

Fig. 12 presents the operational flowdiagram for this process,which contains 44 operations, 70 streams and 17 compounds.

Step 2: Flowsheet decomposition

18 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

Fig. 9. Flowsheet of �-galactosidase production process.

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SustainPro, uses the mass and energy balances information, col-ected in step 1, to determine the entire set of paths (open-, closed-nd accumulation paths).

Fig. 13 shows the data flow regarding step 2.For the b-Gal production flowsheet, a total of 251 mass open-

aths, 17 mass closed-paths, 36 energy open-paths, 1 energylosed-paths and 663 accumulation-paths were found for all theompounds. For illustrative purposes the mass closed- and open-aths are presented in Figs. 14 and 15, respectively. In Fig. 14 on the

eft side the user has information about the units included in theifferent partitions. On the right side the closed-paths are listed forll compounds. The streams included in each closed-path and theespective flowrate are displayed.

Fig. 11. Interconnection between the oper

step 1 (Mominuddin, 2003).

In Fig. 15 the open-paths are listed. For each path the name ofthe compound is specified, the streams across the path are listedand the flowrate is displayed.

The remaining interfaces for the other paths are identical andconsequently they are not presented here.

Step 3: Calculate the indicators

Fig. 16 shows that this step uses as input the paths determined

in step 2 and also some additional information about the compoundproperties, given by the supporting tools. The prices are also usedfor the indicators calculation. SustainPro calculates, automatically,the respective indicators for each path determined in step 2.

ational flowdiagram and SustainPro.

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 19

of �-g

s

O

Fig. 12. Operational flowdiagram

The indicators showing higher potential for improvements wereelected and they are listed in Table 2.

From Table 2 it can be seen that OP31, OP34, OP37, OP114,P118 and OP121 have very negative values of MVA (Material Value

Fig. 13. Summary of da

alactosidase production process.

Added). This means that money is being lost as these compoundsenter and leave the process. Improvement is achieved when theMVA value increases. It can be also seen that OP125 shows a highvalue of EWC (energy and waste cost), which indicates a high energy

ta-flow for step 2.

20 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

Fig. 14. Mass closed-path interface in SustainPro.

TM

Fig. 15. Mass open-path in

able 2ost sensitive indicators for the b-Gal production.

OP Path Component Flowrate (kg/h)

OP 31 S1–S21 H2O 33,158

OP 34 S1–S34 H2O 15,295

OP 37 S1–S47 WFI 14,472

OP 114 S41–S42 WFI 95,438

OP 118 S44–S45 WFI 155,349

OP 121 S63–S62 WFI 72,043

OP 125 S10–S14 N2 33,684

terface in SustainPro.

MVA (103 $/y) EWC (103 $/y) TVA (103 $/y)

−55,177 69 −55,246−25,451 37 −25,488−22,939 35 −22,973−75,367 0 −75,367

−122,679 0 −122,679−62,582 0 −122,679

0 67 −67

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 21

w for

ci

ci

wDoccattv

Att

tt(t

TM

presents the results in terms of scores for each of the selected indi-cators. Table 4 lists the scores for each of the selected indicators.

Fig. 16. Summary of data-flo

onsumption for this open-path. The EWC value should be reducedn order to improve the process.

For the batch processes the operational and the compound indi-ators are also calculated. The most sensitive batch operationalndicators were also selected and they are listed in Table 3.

From Table 3 it can be seen that the operational bottlenecksith respect to time are operations V-104 D2, V-107 C and V-1072 (high value of OTF – operational time factor). For this set ofperations, the operational times are not influenced by any of theompounds since these operations are related to the equipmentharges and discharges. Consequently, the compound indicatorsre not necessary for these operations and only operational indica-ors are calculated for them. It is also possible to see from Table 2hat operation DS-101 indicates high energy consumption (highalue of OEF – operational energy factor).

To highlight the interfaces of the indicators, the Material Valuedded interface is presented in Fig. 17. The values shown in the top

hree tables (see Fig. 17) are the required information while the lastable contains the calculated values of the indicators.

Part II – evaluation, was performed in this step to determinehe sustainability metrics and the safety indices. Some data relatedo the compounds safety and hazards were obtained from MSDS

http://www.msds.com/), which are required for the calculation ofhe safety indices. In the performance analysis, SustainPro reads the

able 3ost sensitive operational indicators for the b-Gal production.

Operation OTF OEF

DS-101 0.043 0.84V-104 D2 0.088 0V-107 C 0.088 0V-107 D2 0.088 0

step 3 (Mominuddin, 2003).

input data (mass and energy balances and prices) and give as anoutput the performance criteria parameters (see Fig. 18).

A table with the performance criteria values is presented in step6 for the base case and the new design alternative. Figs. 19 and 20show the interface for the sustainability metrics and the safetyindices, respectively.

Step 4: Indicator sensitivity analysis (ISA) algorithm

SustainPro orders the indicators taking into account their val-ues. The paths, which correspond to the most negative values ofMVA, RQ and TVA and the highest values of AF and EWC indicatehigher potential for improvements and are at the top of the table(see Fig. 21). Analyzing the table shown in Fig. 21, from the top tothe bottom the user can screen all the indicators from an orderedlist of indicators to select the ones with the highest potential forimprovement.

SustainPro performs a complete indicator sensitive analysis, and

Table 4ISA algorithm results for b-Gal production.

Path Indicator Scores

OP 31 MVA 12OP 34 MVA 20OP 37 MVA 20OP 114 MVA 4OP 118 MVA 6OP 121 MVA 4OP 125 EWC 6

22 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

Fig. 17. Material Value Added-interface in SustainPro.

Fig. 18. Summary of data-flow for performance analysis (Mominuddin, 2003).

Fig. 19. Sustainability metrics-interface in SustainPro.

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 23

Fig. 20. Safety indices-interface in SustainPro.

Fig. 21. Top indicators ordered by their potential for improvement-interface in SustainPro.

stainP

ti

Vwt

I

tg

SustainPro performs a design sensitivity analysis in order todetermine the target variables for the selected target indicators that

Fig. 22. Interface in Su

Table 4 shows that the target indicators for the b-Gal produc-ion process are the MVA for OP34 and OP37, because they are thendicators having the highest scores.

Regarding the batch indicators, OTF for V-104 D2, V-107 C and-107 D2 were found to have similar potential for improvementith respect to reduction of time. OTF for V-104 D2 is selected as

he batch target indicator.After performing the indicator sensitivity analysis step (with the

SA algorithm), SustainPro displays the interface shown in Fig. 22.To summarize, SustainPro in step 4 reads the information about

he indicators, performs automatically the sensitivity analysis andives, as result, the list of target indicators (see Fig. 23).

ro after ISA algorithm.

Step 5: Design sensitivity analysis

Fig. 23. Summary of data-flow for step 4.

24 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

wF

ettt

wao

raericcew

fldai�udPLtflwm

t

Table 5Improvements in target indicators for b-Gal production.

Target indicator Initial Final

MVA – OP34 −25,451 $/y 0 $/y

Table 6Improvements in batch target indicator for b-Gal production.

Target indicator Initial Final

OTF – V-104 D2 0.088% 0.03%

Fig. 26. Summary of data-flow for step 6.

Fig. 24. Summary of data-flow for step 5.

ould produce the best improvements in the target indicators (seeig. 24).

From a sensitivity analysis of the operational parameters influ-ncing the target indicator (MVA – OP34 and OP37) it was foundhat the most significant operational parameters are respectivelyhe flowrates of OP34 and OP37. The interface for the design sensi-ivity analysis is shown in Fig. 25.

For the batch indicators the flowrate of the accumulation-pathas found to be the most sensitive variable, and consequently, for

decrease of the operation time there should be an increase on theperational flowrate.

Step 6: Generation of new design alternatives

It was found that the most sensitive operational parameter iselated to the reduction of an open-path flowrate. This pointed to

reduction of the OP34 and OP37 flowrates for instance by consid-ring the recycle of water. The water coming from OP34 can beecycled directly to the initial operation (V-101 C). However, look-ng at OP37 it is possible to see that the water exiting in this pathontains proteins. These proteins require difficult separation pro-esses in order to purify the water. Consequently, it would not beconomically viable to purify this water and recycle it. Therefore,ater of OP37 will be sent for treatment.

To improve the batch target indicator (OTF), the dischargeowrate of V-104 D2 operation should be increased. This flowrateepends on the chromatographic column specifications. The cat-logue for chromatographic columns, Tosoh Bioscience (2008),ncludes data for an ion-exchange chromatographic column for-galactosidase purification. The biggest chromatographic col-mn presented in the catalogue has approximately the sameimensions as the chromatographic column simulated in Superro Designer (DCatalogue = 60 cm, LCatalogue = 40 cm; DCatalogue = 53 cm,Catalogue = 33 cm) and therefore, the operational data have beenaken from here. This chromatographic column has a maximumowrate of 1600 ml/min. The flowrate of the discharge operation

as increased by up to 75% of the maximum flowrate of the chro-atographic column, reducing in this way the operational time.Summarizing, the new design alternative consists of recycling

he water coming from OP34 and reducing the time in operation

Fig. 25. Design sensitivity analy

Fig. 27. Environmental impact assessment for the base case (BC) and for the newdesign (ND) – LCSoft results.

V-104 D2. With the water recycling the target indicator improved100% since it is possible to do the completely water recycle.Increasing the flowrate in the discharge operation, the batch targetindicator improved 74%. Tables 5 and 6 show the initial and thefinal value of the target indicators.

See Fig. 26 for the data flow in step 6.SustainPro determines the performance criteria, the sustaina-

bility metrics and the safety indices, and the results are listed inTable 7 for the base case and for the new design alternative.

Table 7 shows that the new design alternative is more sustain-able. For the new sustainable design alternative, which consists ofthe recycling water, the following improvements were achieved,the profit increased by 0.1% and the water metrics improved by 65%.

sis-interface in SustainPro.

A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27 25

Table 7Summary of performance criteria parameters.

Metrics Base case New design Improvement

Total net primary energy usage rate (GJ/y) 28,703 28,703 0%% Total net primary energy sourced from renewable 0.75 0.75 0%Total net primary energy usage per kg product (kJ/kg) 29,801.54 29,801.54 0%Total net primary energy usage per unit value added (kJ/$) 0.00 0.00 0%Total raw materials used per kg product (kg/kg) 5364.82 5364.82 0%Total raw materials used per unit value added (kg/$) 0.00 0.00 0%Fraction of raw materials recycled within company 0.00 0.00 0%Fraction of raw materials recycled from consumers 0.00 0.00 0%Hazardous raw material per kg product (kg/kg) 16.65 16.65 0%Net water consumed per unit mass of product (kg/kg) 885.04 599.23 32%Net water consumed per unit value added (kg/$) 1.69 × 10−5 1.14 × 10−5 32%Safety index 28 28 0%LCA – ozone layer depletion 0.038 0.036 5.4%LCA – photochemical oxidation 3.16 2.99 5.4%LCA – acidification 74,985 206 99.7%

28,123

6.38

Tm5tisrti

iu

LCA – eutrophication

Carbon footprint – raw materials

Profit ($/y)

he environmental impact also improved. On the life cycle assess-ent analysis the impact on the ozone layer depletion improved by

.4%, the impact on the photochemical oxidation improved by 5.4%,he acidification impact improved by 99.7% and the eutrophicationmproved by 49.5% (see Fig. 27). The carbon footprint analysis alsohows that the new design alternative is less harmful to the envi-onment, since there is a reduction on the CO2 emission regardinghe raw material acquisition (see Fig. 28). The carbon footprint was

mproved by 4%.

The new design alternative does not include any additionalnvestment, so ECON analysis will have the same economic val-es for the base case design and for the new alternative design.

Fig. 28. Carbon footprin

Fig. 29. Economic analys

20 14,200 49.5%22 4%

6 × 109 6.392 × 109 0.1%

However ECON analysis was performed in order to evaluatewhether it is economically viable to build a b-Gal plant. ECON anal-ysis will also give information to determine whether the processis economically sustainable in order to allow new additional pro-cess improvements, which require investments. After running theeconomic analysis using ECON, it was possible to conclude that thisprocess has a high Net Return value (see Fig. 29), which means thatthe investment is fully recoverable and high profits will be achieved

in the future with the b-Gal production. The rate of return is highand the payback time is very short, so in less than 1 y the full invest-ment on a b-Gal plant is recovered. This implies that new retrofitanalysis can be considered even if the new design alternatives

t – LCSoft results.

is – ECON interface.

26 A. Carvalho et al. / Computers and Chemical Engineering 50 (2013) 8– 27

Fig. 30. Cumulative curve of cash flows – ECON interface.

Fig. 31. Equipment purchase cost distribution – ECON Interface.

stribu

iywt

Fpt

aho

Fig. 32. Utility cost di

nvolve investments. The above results from the economic anal-sis confirms that new investments, proposed by retrofit analysis,ill be recovered in a short period, since the profits are expected

o be very high.ECON generates a cumulative curve of cash flows (see Fig. 30).

rom this curve it is possible to verify that an investment on a b-Gallant is recovered in less than 1 y, reducing the risk associated tohis project.

ECON also plots the distribution of the equipment purchase costsnd the utility costs (see Figs. 31 and 32). These plots might alsoelp in the equipment selection decisions and on the visualizationn the potential for improvements in terms of utility costs.

tion – ECON interface.

5. Discussion and conclusions

An Excel-based software, called SustainPro has been developedbased on the systematic indicator based methodology previouslyintroduced (Carvalho et al., 2008, 2009) and using VBA macros.Through this software it is possible to perform a systematicsustainable design analysis. This design analysis involves the char-acterization of the process based on the established initial design

(retrofit) and then subsequently finds design alternatives by chang-ing the identified design target variables to match the establishedtarget indicators values. The new design with some different equip-ment or variable values is again evaluated and the metrics are used

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o validate it as more sustainable. SustainPro allows a simple, accu-ate and fast analysis of any chemical process, simple or complex,ig or small, batch or continuous and it is integrated with otherneeded) external tools (such as process simulators, process syn-hesis tools). A supporting associated tool called knowledge baseSKB) has been incorporated in order to widen the application rangef SustainPro by revisiting processes already studied or to ana-yse similar ones. Many application examples have been developedo illustrate the potential of SustainPro: MTBE production, ammo-ia production, HDA-process, natural gas purification plant, VCMroduction, bio ethanol production and biorefinery plant. Two sup-orting tools called ECON and LCSoft have been included for thessessment step. These two tools provide a deeper analysis of theelected sustainable design alternatives, allowing the user to take

more conscious decision.SustainPro can be further extended, creating a code to transform

he original flowsheet in an operational flow-diagram automat-cally. Also, there should be an improvement in the connectionetween the commercial simulators and SustainPro in order tollow an easy extraction from them. The knowledge base shoulde further updated with the new analysis that will be performed

n SustainPro and also extended in the number of chemicals avail-ble. Finally, as the analysis requires data from various sources, anncertainty analysis on the data would be very useful.

cknowledgement

The authors gratefully acknowledge financial support fromundac ão para a Ciência e a Tecnologia (under Grant No. SFRH/BPD/3668/2009).

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