process model-based decision support for multi-stakeholder water-food-energy-ecosystem network

Process model based decision supportfor multi-stakeholder

water-food-energy-ecosystem networksHarmonization oflocalinterests

with global sustainability

Monika VargaResearchGroupon Process NetworkEngineering

Water-Food-Energy networks

Water Food

Energy

Source:Garcia andYou,2016.Computers andChemical Engineering,91:49–67.Further edited according to Fig.1ofthe above paper.

30%62%

8%

70%

15%

15%

99%

1%

Challenges ofharmonization• Growing global population→needsmorewater,food andenergyfrom efficient use of renewablelocalresources;

• Globalproblems with climate change andecosystems →need sustainable andresilient designandcontrol oflocalwater/food/energy networks;

• Model based decisions on localpossibilities helpsto find compromise between localinterests andglobal sustainability.

• Longterm questions →Youth have adefiniteinterestandresponsibility for finding solutions!

Conclusion: we have to manage bottom-up themosaics ofthese apparently quite differentprocess networks.

How?

Quantitative decision supportmethods ofProcess SystemsEngineering help to find goodlocalpossibilities:

• to minimize trade-offs and• to maximize synergies.

What isProcess SystemsEngineering?

• It originates from chemical engineering;• It offersmodelingandsimulation tools for– design,operation,control,andoptimization ofprocess systems.

• Recently it encompassesabroad rangeofmultidisciplinary applications:– e.g.biotechnology,environmental engineering,agriculture and food industry,advancedmaterials,etc.

How do process modeling andsimulationsupport better decisions?

• Amodel isasimplifiedrepresentationoftheessential features ofaprocess topromoteunderstandingofits real,dynamic behavior;

• Model based simulation enables topercievethose causal interactionsthatwouldnototherwisebeobvious;

• Thesimulation based designandcontroldecisions can consider the balances.

Our method for modeling andsimulation:Direct ComputerMapping

• Automatic generation ofexecutable processmodels from:– Structure:network ofthe investigated problem;– Functionalities:programprototypes to calculatethe elementary processes

• Graphical interface for model expert• Case specific interfaces for users (webbased,MSExcel,etc.)

• Successful andongoing demand-ledapplications:modelers +field experts

Our recent partners (modeling &field experts)

• NationalPolytechnicInstituteofToulouse,France• CenterforProcessandEnvironmentalEngineering,UPC,Barcelona,Spain

• UniversityofPannonia,Veszprem,Hungary

• Ecological ResearchCenter,HungarianAcademyofSciences

• ChinaAgriculturalUniversity,Beijing,China• Fino-FoodLtd.,Hungary• GS1HungaryNon-profitLtd.• ResearchInstituteforFisheriesandAquaculture,NationalAgriculturalResearchandInnovationCentre

Recently studied demand-led examples(with the unified DCMmethodology)

• Complex environmentalprocess system:managementofasensitivewatershed

• Trans-sectorial agrifoodprocess system:quantitativetracing andtracking

• Process design:Recirculating AquacultureSystems

• Process operation:scheduling ofamulti-product dairy plant

Recently studied demand-led examples

• Complex environmentalprocess system:managementofasensitivewatershed [1,2]

• Trans-sectorial agrifoodprocess system:quantitativetracing andtracking

• Process design:Recirculating AquacultureSystems

• Process operation:scheduling ofamulti-product dairy plant

Studied environmental systemEurope

LakeBalatonarea

Hungary

GISbased interface

Detailed hydrologicalmodel withmeteorological data

CORINErepresentation ofland use

Water network andland use

User interface

What isit for?

• To study the effects of(extremly or slowlychanging)meteorological situations;

• To follow contaminants in the watershed;• To study the effects ofpossible land use;• To combine with the investigation ofwater-food-energy-ecosystem related studies.

Example:fictitious weather scenarios

Example:spreading ofcontaminant

Recently studied demand-led examples

• Complex environmentalprocess system:managementofasensitive watershed

• Trans-sectorial agrifoodprocess system:quantitativetracingandtracking[2,3,4,10]

• Process design:RecirculatingAquaculture Systems

• Process operation:schedulingofamulti-product dairy plant

Tracingandtrackingoffood products

Challenges

• Heterogeneous actors (plant cultivation,fodder production,animal breeding,foodprocessing,commerce,etc.)

• Dataservicevaries from the logbook offamily farms to the sophisticated ERPsystems;

• Need for trackingandtracingof(sometimessuddenly appearing)harmful andusefulcomponents.

Testsystems

• Family farm(plant cultivation,vegetables)• Meat chain (from plant cultivation to slaughtering)

Technical partner

• GS1HungaryNonprofit Ltd.

Example:webbased data supply

What isit for?

• Qualitative tracking• Qualitative tracing• Quantitative tracking• Quantitative tracing• Balance control• Value chain analysis (ongoing)

Example:tracking report

Recently studied demand-led examples

• Complex environmentalprocess system:managementofasensitivewatershed

• Trans-sectorial agrifoodprocess system:quantitativetracing andtracking

• Process design:RecirculatingAquacultureSystems [5,6,8]

• Process operation:scheduling ofamulti-product dairy plant

Recirculation Aquaculture System

Basicscheme ofRAS

Increasing volume ofmultiple tanks in RAS

Challenges

• Technical:– Fish growth,feeding strategy (quality,amount,scheduling)transporting fishes between stagesandwastewater treatment are highly interacting;

– Designandcontrol ofthis whole system hasanenormous complexity.

• Organizational:– Push:long-term contracts for buying fingerlings;– Pull:long-term contracts for selling products.

Example for the modeling interface

What isit for?

• To design anew system for:– optimal tankstructure ofthe subsequent stages(adapted to the fish speciesandto commercialstrategy);

– optimal waste water treatment unit.

• To control anexisting system for:– optimal operation (e.g.minimize fresh water use);– optimal transporting strategy between the stages.

Example:optimization in a„fictitious tank”

Example:considering diversification in weight

Recently studied demand-led examples

• Complex environmentalprocess system:managementofasensitivewatershed

• Trans-sectorial agrifoodprocess system:quantitativetracing andtracking

• Process design:Recirculating AquacultureSystems

• Process operation:scheduling ofamulti-product dairy plant [7,9]

Demand driven scheduling ofadairy plant

PLANNING–Monthly

••Target:Equilibrated material balance••Push feature:Yearly based contracts for raw milk••Pull feature:EXPECTEDselling with EMPIRICALESTIMATION

SCHEDULING–Weekly

••Target:100%serving rate••Pull feature:last week selling andproduction numbers +realorders (fixed+ad-hoc)

••Push feature:MUSTprocess the milk in lessthan 48hours

RESCHEDULING– Daily/Shift

••Target:100%serving rate with minimumnumber ofconversions••Pull features:••Orders ofthe coming 4days••Available stock

Planning,scheduling andre-scheduling

Example flowsheet ofdairy plant processes

Rawmilktakeover

Milk processing(pasteurisation,homogenization,

skimming)

Batches••Cheese••Yogurt••Processedcheese

Packaging

Ripening/Storing

Selling

What isit for?

Dynamic simulation based reasoning with theknowledge ofdemands andstocks supports:

• fast scheduling/rescheduling according to theactual consumers’demands;

• consideration ofmany operational constraints(e.g.cleaning periods,cooling times,changingmachine capacity,etc.).

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Changing stocks ofthree products(in linewith packaging andselling)

JoghurtAJoghurtB

Example for decision supporting results

ConclusionsWe can use unified modellingandsimulationmethods- for analysis,designandoperation- ofthe various sub-systems of water,food,energyandecosystem related processes.

This ought to becombined with utilization ofavailable big data to generate better models forthe actual andpossible solutions.

Outlook: In this way the interacting complexsystems can beoverviewed andevaluated for aquantitatively founded decision support.

Outlook

Outlook

Outlook

Outlook

R&Dfor necessary newtechnological alternatives forfood-water-energy processes

Process models,built from thevarious technological

alternatives

Multi-objective evaluations forvarious time horizons

Outlook

efita2017.org/

References1. Varga M, Balogh S, Csukás B. An extensible, generic environmental process modelling framework

with an example for a watershed of a shallow lake ENVIRONMENTAL MODELLING & SOFTWARE 75:pp. 243-262. (2016) IF: 4.42

2. Varga M, Csukás B. Simulation of Agro-environmental Processes by Direct Computer MappingLecture Notes in Engineering and Computer Science II: pp. 847-852. (2015) World Congress onEngineering and Computer Science 2015. San Francisco, USA. ISBN 978-988-14047-2-5

3. Varga M, Csukás B, Balogh S. Transparent Agrifood Interoperability, Based on a Simplified DynamicSimulation Model In: Mildorf T, Charvat K (szerk.) ICT for Agriculture, Rural Development andEnvironment: Where we are? Where we will go?. Prague: Czech Centre for Science and Society,2012. pp. 155-174. (ISBN: 978-80-905151-0-9)

4. András Tankovics, Sándor Balogh, Mónika Varga. Testing of a process model based Web interface forintegration of small family farms in sector spanning traceability. Agriculture Informatics 2013 - Thepast, present and future of agricultural informatics, 8-9. November 2013, Debrecen, Hungary.

5. Varga M, Balogh S, Wei Y, Li D, Csukas B Dynamic simulation based method for the reduction ofcomplexity in design and control of Recirculating Aquaculture Systems INFORMATION PROCESSINGIN AGRICULTURE 3:(3) pp. 146-156. (2016)

6. Varga Mónika, Balogh Sándor, Kucska Balázs, Yaoguang Wei, Daoliang Li, Csukás Béla Testing ofDirect Computer Mapping for dynamic simulation of a simplified Recirculating Aquaculture System.JOURNAL OF AGRICULTURAL INFORMATICS 6: (3) pp. 1-12. (2015)

7. Linda Egyed, Mónika Varga, Béla Csukás. First steps toward Direct Computer Mapping basedscheduling of dairy production. Agricultural Informatics 2014 - Future Internet and ICT Innovation,13-15 November 2014, Debrecen, Hungary.

8. Gergo Gyalog. Bio-economic optimization of fish producing technologies. Ongoing PhD thesis.9. Linda Egyed. Analysis and development of multiscale (sub)optimal scheduling in a dairy plant.

Ongoing PhD thesis.10. András Tankovics. Dynamic process modeling of a dairy farm. Ongoing PhD thesis.