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MINLP PROCESS SYNTHESIS FOR BIOGAS PRODUCTION FROM ORGANIC AND ANIMAL WASTE

MINLP PROCESS SYNTHESIS FOR BIOGAS PRODUCTION FROM ORGANIC AND ANIMAL WASTE. Summer Workshop, Veszprém 2009. Scientific research centre Bistra Ptuj. Rozalija Drobež, Zorka Novak Pintarič, Bojan Pahor, Zdravko Kravanja. University of Maribor Faculty of Chemistry and Chemical Engineering.

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MINLP PROCESS SYNTHESIS FOR BIOGAS PRODUCTION FROM ORGANIC AND ANIMAL WASTE

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  1. MINLP PROCESS SYNTHESIS FOR BIOGAS PRODUCTION FROM ORGANIC AND ANIMAL WASTE Summer Workshop, Veszprém 2009 Scientificresearch centre Bistra Ptuj Rozalija Drobež, Zorka Novak Pintarič, Bojan Pahor, Zdravko Kravanja University of Maribor Faculty of Chemistry and Chemical Engineering

  2. Outlay • Introduction • Biogas process superstructure • Aggregated mathematical model • Mass balance and biogas production • Logical and other constraints • Heat balance of biogas production • Objective function • Solution of the industrial case study

  3. Introduction • Waste from slaughterhouse and other animal wastes are often mishandled and underutilized • Leading to many serious environmental and economic problems: • Efficient, economical and sustainable solution is needed, preferably one which converts waste into valuable products and • Biogas production is one: • Significantly reduce impact on greenhouse gas emissions • Preventing the accumulation of organic and animal waste • Used for heat, electricity and liguid fuel production

  4. Introduction • Industry for the operation need a lot of energy, which is still mainly produced from fossil fuels • Process integration is an efficient tool which enables the reducing consumption of heat, electricity, as well as freshwater and other resources • An aggregated mathematical model - mixed-integer nonlinear programming (MINLP) problem • Applied to a large-scale existing meat company in order to optimize the biogas production

  5. Biogas process superstructure

  6. Biogas process superstructure • Optimal choice for the processing of animal and organic waste from the large-scale meat company: • Biogas production by anaerobic fermentation at mesophilic or thermofilic condition • With or without a combination of the rendering plant • Reconstruction of existing production plant: • Reconstruct the existing pig farm – continue with pork meat production • Adapt the existing pig farmto new poultry farm – start producing poultry as the company’s main activity

  7. Biogas process superstructure • Existing pig farm is adapted - necessary to provide an additional water source to fulfill all the production requirements • Water demands can be satisfied: • Asfreshwater from a local well or • From the another meat industrial plants as an industrial wastewater • Transportation of industrial wastewater: • By cisterns or • A pressurized sewage pipeline • Wastewater treatment processes: • Open water system – treated in a central treatment unit or • Closed water system – treated by ultrafiltation and reverse osmosis

  8. Aggregated mathematical model • Aggregated form as a mixed-integer nonlinear programming (MINLP) problem for the selection of an optimal process for processing animal and other bio-waste • Maximizes the net present worth (NPW) as an objectivefunction: • Concave investment cost correlations, • Subject to simultaneous mass and heat balances, • Simplified design equations • Simplified relationship without reaction kinetics and time constraints and • Daily available quantities of substrates - given as mean values

  9. Aggregated mathematical model • For simultaneous heat integration of continuous processes we had used amodified model, developed by Duran and Grossmann (1986): • Allow the determination of minimum utility consumption (cost) in an optimal process scheme • Basic model is based on a pinch point location method for the consumption of hot and cold utilities in the nonlinear optimization problem • We have modified basic nonlinear model to linear model: • Have a constant temperature process streams • Heat capacity of hot and cold process stream flows are variable • Avoid nonconvexity and model can obtain global optimal solutions

  10. Aggregated mathematical model Definition of sets and binary variables: • Set presented the inlet substrates and water supply • slaughterhouse waste of category III • inlet substrates from the pig farm • potential inlet substrates from the new poultry farm • freshwater • industrial wastewater • substrates purchased on the market • other inlet organic substrates • Set presented the solid product from the rendering plant • 1 = meat meal • 2 = animal fat • 3 = bone meal

  11. Data for inlet waste material, other substrates and water supply

  12. Aggregated mathematical model Definition of sets and binary variables: • Set presented the production processes • anaerobic conversion process • rendering plant • processes – can utilized the slaugh. waste of category III • 1 = thermophilicproces • 2 = mesophilic process + sterilization unit • 3 = mesophilic process • 4 = rendering plant • Set presented the cold processes streams • Set presented the hot processes streams

  13. Aggregated mathematical model Definition of sets and binary variables: • Set presented the remeining background alternatives • alternatives – some additional investment • existing pig farm • new poultry farm • water supply – freshwater • waster supply – industrial wastewater • transportation – industrial wastewater • wastewater treatment • closed water system • open water system • Binary variable for the selection of optimal process and background alternatives: • selection of optimal production process • selection of optimal remaining background amternative • selection of sterilization unit

  14. Mass balance and biogas production Mass balance for biogas production: (1) Biogas volume flow – rate production: (2) Mass balance for the production of solid product : (3) RWW – recirculated wastewater BG – biogas R – residue VSS – volative suspended solid SP – solid product S - total substrates Production of solid product: (4) Massflow – rateofsubstrates: (5)

  15. Mass balance and biogas production Mass balance for wastewater: (6) Mass flow rate of wastewater: (7) Mass flow rate of wastewater to closed water network: (8) Mass flow rate of the recirculated wastewater : WW – wastewater WWC – wastewater to closed water network WWO– wastewater to open water network DMC – dry matter content OF – organic fertilizer T - transported industrial wastewater (9) Mass balance for industrial wastewater: (10)

  16. Logical and other constraints Constraints for substrates: • Limited by the available daily amount of the substrates (11) • Constraint for the inlet substrates from the pig farm and new poultry farm (12) (13) • Constraint for the water supply as freshwater and industrial wastewater (14) (15) • Constraint for the residue (16) • Production of solid product is limited by the daily capacity of the process (17) LO – lower bounds UP – upper bounds

  17. Logical and other constraints Constraints for biogas production: (18) Constraints for wastewater: (19) • Constraint for the recirculated wastewater (20) • Constraint for an organic fertilizer (21) • Constraint for transported industrial wastewater (22) • Constraint for dry matter content (23)

  18. Logical and other constraints Constraints for the selection of process and background alternatives: • Selection between processes (24) • Existence/non – existence of background alternatives in the optimal solution (25) • If the new poultry is selected – necessary to supply some process water (26) • If industrial wastewater is selected – transportation (27) • Selection between wastewater treatment (28)

  19. Heat balance of biogas production Heat capacity flow rate for cold and hot streams in the processes: • Slaughterhouse waste of category III (29) • Substrates and water supply (30) • Recirculation wastewater (31) • Slaughterhouse waste of category III which can used for biogas production (32) • Wastewater which we treated in the treatment unit (33) • Biogas production (34) • Total heat combustion of cogeneration system (35)

  20. Heat balance of biogas production • In this model, it was assumed: • two hot utility – steam and heat from cogeneration system • one cold utility – cooling water • inlet/out let temperatures of hot and cold streams – fixed • specific heat capacity of substrates – fixed • heat recovery approach temperature – 20K • Heat loss of anaerobic fermentation – is considered (36) • Total heat balance for the cogeneration system : (37) • Total heat balance of heat integrated process : (39) (40) (38)

  21. Heat balance of biogas production • Upper bound for the heat excange of hot streams: (41) • Pinch temperatures (42) • Upper bound for the heat excange of cold streams: (43) • If the process is not heat integrated (45) (44)

  22. Objective function • The objective function maximizes the net present worth (NPW), in which investment cost is subtracted from discounted cash flows (46) • Investment for the processes (47) • Cash flow is defined by the following substitutive equation (48) • Incomes – revenue from selling electricity, heat, solid product, organic fertilizer (49) • Expenses – cost for purchasing electricity, substrates, treating and transport , utility (50)

  23. Solution of the industrial case study • The results of economical analysis indicate that the optimal solution is: • Biogas production under thermophilic conditions without a rendering plant • Includes potential substrates from the new poultry farm • All slaughterhouse wastes of category III • Process scheme comprises a freshwater source from a local well • Additional closed water network with the re-use of purified wastewater and by-product as an organic fertilizer

  24. Economic evaluation of results for mass and heat integration of biogas production

  25. Results for mass and heat integration of biogas production

  26. Solution of the industrial case study Net present worth (NPW) is 11.80 MEUR and payback period 3.59a. Almost complete consumption of hot 889 kW and 1/2 of cold utiliy 349 kW

  27. Solution of the industrial case study Net present worth (NPW) is 7.31 MEUR and payback period 4.23a. Almost complete consumption of hot 681 kW and 1/2 of cold utiliy 151 kW

  28. Thank you for your attention Summer Workshop, Veszprém 2009 zdravko.kravanja@uni-mb.si

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