The document discusses the integration of water, food, energy, and ecosystem networks through process model-based decision support to harmonize local interests with global sustainability. It emphasizes the use of quantitative decision support methods in process systems engineering to address challenges posed by climate change and infrastructure complexity, highlighting collaborative efforts with various institutions. The approach aims to utilize modeling, simulation, and big data to enhance decision-making and promote sustainable practices across different sectors.

1. Process model based decision support
for multi-stakeholder
water-food-energy-ecosystem networks
Harmonization of local interests
with global sustainability
Monika Varga
Research Group on Process Network Engineering

2. Water-Food-Energy networks
Water Food
Energy
Source: Garcia and You, 2016. Computers and Chemical Engineering, 91: 49–67.
Further edited according to Fig.1 of the above paper.
30%
62%
8%
70%
15%
15%
99%
1%

3. Challenges of harmonization
• Growing global population → needs more water,
food and energy from efficient use of renewable
local resources;
• Global problems with climate change and
ecosystems → need sustainable and resilient design
and control of local water/food/energy networks;
• Model based decisions on local possibilities helps
to find compromise between local interests and
global sustainability.
• Long term questions → Youth have a definite
interest and responsibility for finding solutions!

4. Conclusion: we have to manage bottom-up the
mosaics of these apparently quite different
process networks.
How?
Quantitative decision support methods of
Process Systems Engineering help to find good
local possibilities:
• to minimize trade-offs and
• to maximize synergies.

5. What is Process Systems Engineering?
• It originates from chemical engineering;
• It offers modeling and simulation tools for
– design, operation, control, and optimization of
process systems.
• Recently it encompasses a broad range of
multidisciplinary applications:
– e.g. biotechnology, environmental engineering,
agriculture and food industry, advanced materials,
etc.

6. How do process modeling and simulation
support better decisions?
• A model is a simplified representation of the
essential features of a process to promote
understanding of its real, dynamic behavior;
• Model based simulation enables to percieve
those causal interactions that would not
otherwise be obvious;
• The simulation based design and control
decisions can consider the balances.

7. Our method for modeling and simulation:
Direct Computer Mapping
• Automatic generation of executable process
models from:
– Structure: network of the investigated problem;
– Functionalities: program prototypes to calculate
the elementary processes
• Graphical interface for model expert
• Case specific interfaces for users (web based,
MS Excel, etc.)
• Successful and ongoing demand-led
applications: modelers + field experts

8. Our recent partners (modeling & field experts)
• National Polytechnic Institute of Toulouse, France
• Center for Process and Environmental Engineering,
UPC, Barcelona, Spain
• University of Pannonia, Veszprem, Hungary
• Ecological Research Center, Hungarian Academy of
Sciences
• China Agricultural University, Beijing, China
• Fino-Food Ltd., Hungary
• GS1 Hungary Non-profit Ltd.
• Research Institute for Fisheries and Aquaculture,
National Agricultural Research and Innovation Centre

9. Recently studied demand-led examples
(with the unified DCM methodology)
• Complex environmental
process system:
management of a sensitive
watershed
• Trans-sectorial agrifood
process system: quantitative
tracing and tracking
• Process design:
Recirculating Aquaculture
Systems
• Process operation:
scheduling of a multi-
product dairy plant

10. Recently studied demand-led examples
• Complex environmental
process system:
management of a sensitive
watershed [1, 2]
• Trans-sectorial agrifood
process system: quantitative
tracing and tracking
• Process design:
Recirculating Aquaculture
Systems
• Process operation:
scheduling of a multi-
product dairy plant

11. Studied environmental system
Europe
Lake Balaton area
Hungary

12. GIS based interface
Detailed hydrological
model with
meteorological data
CORINE representation of
land use
Water network and land use

13. User interface

14. What is it for?
• To study the effects of (extremly or slowly
changing) meteorological situations;
• To follow contaminants in the watershed;
• To study the effects of possible land use;
• To combine with the investigation of water-
food-energy-ecosystem related studies.

15. Example: fictitious weather scenarios

16. Example: spreading of contaminant

17. Recently studied demand-led examples
• Complex environmental
process system: management
of a sensitive watershed
• Trans-sectorial agrifood
process system: quantitative
tracing and tracking [2, 3, 4,
10]
• Process design: Recirculating
Aquaculture Systems
• Process operation: scheduling
of a multi-product dairy plant

18. Tracing and tracking of food products

19. Challenges
• Heterogeneous actors (plant cultivation,
fodder production, animal breeding, food
processing, commerce, etc.)
• Data service varies from the log book of
family farms to the sophisticated ERP
systems;
• Need for tracking and tracing of (sometimes
suddenly appearing) harmful and useful
components.

20. Test systems
• Family farm (plant cultivation, vegetables)
• Meat chain (from plant cultivation to slaughtering)
Technical partner
• GS1 Hungary Nonprofit Ltd.

21. Example: web based data supply

22. What is it for?
• Qualitative tracking
• Qualitative tracing
• Quantitative tracking
• Quantitative tracing
• Balance control
• Value chain analysis (ongoing)

23. Example: tracking report

24. Recently studied demand-led examples
• Complex environmental
process system:
management of a sensitive
watershed
• Trans-sectorial agrifood
process system: quantitative
tracing and tracking
• Process design:
Recirculating Aquaculture
Systems [5, 6, 8]
• Process operation:
scheduling of a multi-
product dairy plant

25. Recirculation Aquaculture System

26. Basic scheme of RAS

27. Increasing volume of multiple tanks in RAS

28. Challenges
• Technical:
– Fish growth, feeding strategy (quality, amount,
scheduling) transporting fishes between stages
and wastewater treatment are highly interacting;
– Design and control of this whole system has an
enormous complexity.
• Organizational:
– Push: long-term contracts for buying fingerlings;
– Pull: long-term contracts for selling products.

29. Example for the modeling interface

30. What is it for?
• To design a new system for:
– optimal tank structure of the subsequent stages
(adapted to the fish species and to commercial
strategy) ;
– optimal waste water treatment unit.
• To control an existing system for:
– optimal operation (e.g. minimize fresh water use);
– optimal transporting strategy between the stages.

31. Example: optimization in a „fictitious tank”

32. Example: considering diversification in weight

33. Recently studied demand-led examples
• Complex environmental
process system:
management of a sensitive
watershed
• Trans-sectorial agrifood
process system: quantitative
tracing and tracking
• Process design:
Recirculating Aquaculture
Systems
• Process operation:
scheduling of a multi-
product dairy plant [7, 9]

34. Demand driven scheduling of a dairy plant

35. PLANNING –
Monthly
••Target: Equilibrated material balance
••Push feature: Yearly based contracts for raw milk
••Pull feature: EXPECTED selling with EMPIRICAL ESTIMATION
SCHEDULING –
Weekly
••Target: 100% serving rate
••Pull feature: last week selling and production numbers + real
orders (fixed + ad-hoc)
••Push feature: MUST process the milk in less than 48 hours
RESCHEDULING
– Daily / Shift
••Target: 100% serving rate with minimum number of conversions
••Pull features:
••Orders of the coming 4 days
••Available stock
Planning, scheduling and re-scheduling

36. Example flowsheet of dairy plant processes
Raw milk
takeover
Milk processing
(pasteurisation,
homogenization,
skimming)
Batches
••Cheese
••Yogurt
••Processed
cheese
Packaging
Ripening/ Storing
Selling

37. What is it for?
Dynamic simulation based reasoning with the
knowledge of demands and stocks supports:
• fast scheduling/rescheduling according to the
actual consumers’ demands;
• consideration of many operational constraints
(e.g. cleaning periods, cooling times, changing
machine capacity, etc.).

38. 0
1000
2000
3000
4000
5000
6000
7000
8000
2014.4.3.18
2014.4.3.14
2014.4.3.10
2014.4.3.6
2014.4.3.2
2014.4.2.22
2014.4.2.18
2014.4.2.14
2014.4.2.10
2014.4.2.6
2014.4.2.2
2014.4.1.22
2014.4.1.18
2014.4.1.14
2014.4.1.10
2014.4.1.6
2014.4.1.2
2014.3.31.22
2014.3.31.18
2014.3.31.14
2014.3.31.10
2014.3.31.6
Quantity, piece
Changing stocks of three products
(in line with packaging and selling)
Joghurt
A
Joghurt
B
Example for decision supporting results

39. Conclusions
We can use unified modelling and simulation
methods
- for analysis, design and operation
- of the various sub-systems of water, food, energy
and ecosystem related processes.
This ought to be combined with utilization of
available big data to generate better models for
the actual and possible solutions.
Outlook: In this way the interacting complex
systems can be overviewed and evaluated for a
quantitatively founded decision support.

40. Outlook

41. Outlook

42. Outlook

43. Outlook

44. R&D for necessary new
technological alternatives for
food-water-energy processes
Process models, built from the
various technological
alternatives
Multi-objective evaluations for
various time horizons
Outlook

45. efita2017.org/

46. References
1. 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 Mapping
Lecture Notes in Engineering and Computer Science II: pp. 847-852. (2015) World Congress on
Engineering 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 Dynamic
Simulation Model In: Mildorf T, Charvat K (szerk.) ICT for Agriculture, Rural Development and
Environment: 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 for
integration of small family farms in sector spanning traceability. Agriculture Informatics 2013 - The
past, 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 of
complexity in design and control of Recirculating Aquaculture Systems INFORMATION PROCESSING
IN AGRICULTURE 3:(3) pp. 146-156. (2016)
6. Varga Mónika, Balogh Sándor, Kucska Balázs, Yaoguang Wei, Daoliang Li, Csukás Béla Testing of
Direct 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 based
scheduling 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.