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Additional Notes – Lecture 1 SPEED Additional Notes – Lecture 2 Rafiqul Gani PSE for SPEED Skyttemosen 6, DK-3450 Allerod, Denmark [email protected] www.pseforspped.com SPEED General problem definition & solution MINLP means Mixed Integer Non Linear Programming – optimization problems formulated as MINLP contains integer (Y) and continuous variables (x, y) as optimization variables, and, at least the objective function and/or one of the constraints is non-linear. T Fobj = min {C Y + f(x, y, u, d, θ ) + Se + Si + Ss + Hc + Hp} P = P(f, x, y, d, u, θ) Process*/product model 0 = h1(x, y) Process/product constraints 0 g1(x, u, d) “Other” (selection) constraints 0 g2(x, y) Alternatives (molecules; unit B x + CTY D operations; mixtures; flowsheets; ….) Chemical product centric sustainable process design - Lecture 1 (extra slides) 2 SPEED General problem definition & solution T (1) Fobj = min {C Y + f(x, y, u, d, θ ) + Se + Si + Ss + Hc + Hp} P = P(f, x, y, d, u, θ) Process*/product model (2) 0 = h1(x, y) Process/product constraints (3) 0 g1(x, u, d) “Other” (selection) (4a) constraints 0 g2(y) (4b) Alternatives (molecules; unit B x + CTY D operations; mixtures; (5) flowsheets; ….) Solution approaches •Solve all Eqs. (1-5) simultaneously •Solve 4b, 2, check 3, check 4a, check 5, calc. 1 Chemical product centric sustainable process design - Lecture 1 (extra slides) 3 SPEED General problem definition & solution Y = 0 or 1 x : process u : fixed d : equipment : parameters •Solve 4b, 2, check 3, check 4a, check 5, calc. 1 • Enumerate sets of Y that satisfy 4b • Given Y, u, d, , solve eq. 2 for x for all sets of Y • Given x, Y, check 3, then 4a, then 5 for remaining sets of Y to obtain the set of feasible solutions • For each feasible solution, calc. FOBJ and find the optimal Chemical product centric sustainable process design - Lecture 1 (extra slides) 4 SPEED Example : Simultaneous product-process design Find solvent or membrane, flowsheetY18 & optimal design DEC Y19 Y20 Y13 Y21 Y14 Y22 Y15 Y23 P1 Y6 Y8 Y1 Y9 D2 Y2 Y7 Feed D1 Y3 Y10 Y11 Y12 Y16 Y4 Y17 EX Y5 P2 Y24 Solvent1 Y25+1 Y25 Solvent make up Solvent2 Y25+2 . Y25+N SolventN Chemical product centric sustainable process design - Lecture 1 (extra slides) 5 SPEED Example – Simultaneous product-process design Find solvent or membrane, flowsheet Y18& optimal design DEC Y19 Y20 Y13 Y21 Y14 Y22 Y15 Y23 P1 Y6 Y8 Y1 Y9 S5 PERVAPORATOR S2 D2 Y7 DISTILLATION EXPANDER COMPRESSOR Y2 50 Feed D1 49 S7 48 46 S4 Y3 44 Y10 42 40 38 Y11 S1 36 34 32 30 28 Y12 S3 Y16 26 Y4 24 22 20 Y17 EX 18 16 14 Y5 12 P2 For fixed membrane and 10 8 6 4 Y24 process flowsheet, find 2 Solvent1 Y25+1 Y25 1 S6 optmalSolvent make up Solvent2designY25+2 . Y25+N SolventN Chemical product centric sustainable process design - Lecture 1 (extra slides) 6 SPEED Example – Simultaneous product-process design Find solvent or membrane, flowsheet Y18& optimal design DEC Y19 Y20 Y13 Y21 Y14 Y22 Y15 Y23 P1 Y6 Y8 Y1 Y9 D2 Y2 Y7 Feed D1 Y3 Y10 Y11 Y12 Y16 Y4 Y17 EX For fixed membrane andY5 P2 process flowsheet, find Y24 optmal designSolvent1 Y25+1 Y25 Solvent make up Solvent2 Y25+2 . Y25+N SolventN Chemical product centric sustainable process design - Lecture 1 (extra slides) 7 SPEED Conceptual product process design problem Balance equations Conditional/ constraint equations Constitutive equations Design constraints Chemical product centric sustainable process design - Lecture 1 (extra slides) 8 SPEED Model analysis Chemical product centric sustainable process design - Lecture 1 (extra slides) 9 SPEED Iterative design process 30 31 Number of eqs = 11 32 33 Number of variables = 13 34 35 Degrees of freedom = 2 36 38 Variables to specify = Z1, Z2 39 39 40 Solve Eqs. 30-39 for specified Z Solve Eqs. 39-40 for P If 39-40 not satisfied, assume new Z and repeat Chemical product centric sustainable process design - Lecture 1 (extra slides) 10 SPEED Non-iterative product-process design 30 31 Number of eqs = 11 32 33 Number of variables = 13 34 35 Degrees of freedom = 2 36 37 Variables to specify = Z 38 39 40 Solve Eqs. 39-40 for Y1, Y2 Solve Eqs. 31-36 for X1, X2, Y3, A1, A2, 1, 2 Match 1, 2 (target) with Z1, Z2 (product or material or chemical design) Chemical product centric sustainable process design - Lecture 1 (extra slides) 11 SPEED Superstructure based optimization to find optimal processing routes Chemical product centric sustainable process design - Lecture 1 (extra slides) 12 A 3-Stage approach to sustainable process design Focus on Stage 1 Given: set of feedstock & products Given: feasible design (base case) Find: processing route Find: alternative more sustainable design Stage 1 Stage 3 Synthesis Innovation Stage 2 Design Given: processing route Find: feasible design 2016 AIChE Annual Meeting | 13 Stage 1 Stage 3 Synthesis Innovation Stage 2 Stage 1: Synthesis Design The objective of Stage 1 is to obtain the processing route (including feedstock and products) START Step 1.1: Problem definition Objective, - Define objective, raw material(s), product(s), location(s) location(s), etc. Step 1.2: Superstructure generation and data collection - Generate superstructure (raw materials, products, routes, technologies) - Collect data (mass & energy balance data, location-dependent data) Superstructure, - Check data consistency data, model Generic - Modify generic model (if necessary) model Step 1.3: Solution of the optimization problem - Generate input file, solve optimization problem in GAMS, generate output file - Perform post-optimality calculations and interpret results Optimal network and results No Synthesis objectives met? Yes Yes Define new Workflow Tools scenarios? Super-O Data flow No interface To DESIGN Source: Bertran, M., Frauzem, R., Zhang, L., Gani, R. (2016). A generic methodology for superstructure optimization of different processing networks. Computer Aided Chemical Engineering (26th European Symposium on Computer Aided 2016 AIChE Annual Meeting | 14 Process Engineering, ESCAPE26). Stage 1 Stage 3 Synthesis Innovation Stage 2 Overview of the Synthesis stage Design The key elements of the framework require methods & tools Need: Superstructure representation RAW PROCESSING PROCESSING PROCESSING PRODUCTS Problem Superstructure MATERIALS STEP 1 STEP 2 STEP 3 of alternatives 1-1 2-1 3-1 P-1 RM-1 1-2 2-2 3-2 P-2 RM-2 1-3 2-3 3-3 P-3 Need: Generic process model Need: Knowledge Database Model management R i,k i,k i,k i,k FUT,1 FUT,2 FUT,3 i,k FOUT1 i,k,kk i,k,kk F i,k i,k i,k i,k F1 FIN FM FR FW i,k FOUT2 i,k,kk F2 W i,k LEGEND: Mixing Waste sep. Process stream Flow division Separation Added/removed Reaction Utility stream Solution Need: Integration with Solution strategy optimization environment Source: Bertran, M., Frauzem, R., Sanchez-Arcilla, A., Zhang, L., Woodley, J.M., Gani, R. (submitted). A generic methodology for processing route synthesis and design based on superstructure optimization. Computers and Chemical 2016 AIChE Annual Meeting | 15 Engineering (Special Issue ESCAPE26). Stage 1 Stage 3 Synthesis Innovation Stage 2 Data management through databases Design Specific databases are built on a common data structure that fits the problem requirements Basic Material Process Name Step name Component Feedstock Corn stover Step Conversion Location Interval name Mexico Reaction Product Interval Fermentation Availability Name 1.84e07 t/y Location Mexico Utility Connection Price Country code 130 USD/t MX Data CO2 Database Biorefinery Database Components 22 71 Utilities 4 4 Processing steps 5 21 Processing intervals 30 102 Feedstocks 2 11 Products 11 9 Reactions 21 63 Locations - 10 Source: Bertran, M., Frauzem, R., Sanchez-Arcilla, A., Zhang, L., Woodley, J.M., Gani, R. (submitted). A generic methodology for processing route synthesis and design based on superstructure optimization. Computers and Chemical 2016 AIChE Annual Meeting | 16 Engineering (Special Issue ESCAPE26). Stage 1 Stage 3 Synthesis Innovation Stage 2 Super-O Design An interface for formulating and solving process synthesis problems using superstructure optimization Super-O Superstructure & data DATABASES Structured problem data Consistency checks GAMS input file GAMS optimization Graphical representation (CPLEX) GAMS output file Linearization of capital cost Generic model file Edits on the model Modified model file Optimal solution Automated Optimal network & results Manual Source: Bertran, M., Frauzem, R., Zhang, L., Gani, R. (2016). A generic methodology for superstructure optimization of different processing networks. Computer Aided Chemical Engineering (26th European Symposium on Computer Aided 2016 AIChE Annual Meeting | 17 Process Engineering, ESCAPE26). Super-O Tools integration Partners Literature Database Research KNOWN ROUTES DATA Online databases NEW ROUTES DATA Input file ProCAMD ProCARPS Input file & generic model GAMS Computer-Aided Molecular Design, Computer-Aided Reaction Path Synthesis Super-O Output file Generic model Source: Bertran, M., Frauzem, R., Sanchez-Arcilla, A., Zhang, L., Woodley, J.M., Gani, R. (submitted). A generic methodology for processing route synthesis and design based on superstructure optimization. Computers and Chemical 2016 AIChE Annual Meeting | 18 Engineering (Special Issue ESCAPE26). Biorefinery network Sugar Sugarcane Sugar production Hardwood Citric acid Size reduction Molasses Citric acid chips fermentation Lactic acid Switchgrass Lignocellulose Lactic acid fermentation L-lysine Wheat straw L-lysine acid fermentation Cassava STEX NREL Ethanol Ethanol Int2 Int3 Int4 Ethanol rhizome pretreatment hydrolysis fermentation purification Dilute acid Conc. acid Corn stover pretreatment hydrolysis Sugarcane Lime Dilute acid bagasse pretreatment hydrolysis ARP LT Fischer- Gasoline-diesel Gasoline-diesel Int5 Reforming Int6 Gasoline pretreatment Tropsch mix sep. Controlled pH HT Fischer- Methanol to Methanol (int) Diesel pretreatment Tropsch gasoline AFEX Methanol Methanol Int7 Methanol pretreatment synthesis purification Gasification Alkali-cat.
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