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 & optimal design

Y18

DEC Y19

Y20 Y13

Y21 Y14

Y22 Y15

Y23 P1

Y6 Y8

Y1 Y9 D2 Y2 Y7 Feed D1

Y3 Y10

Y11

Y12 Y16 S5

PERVAPORATOR

Y4 S2

Y17 DISTILLATION

EXPANDER COMPRESSOR

EX 50

Y5 P2 49

S7 48

46

Y24 S4

44

Solvent1 Y25+1 Y25 42

40

38

Solvent make up Solvent2 Y25+2 S1 . 36 .

. Y25+N 34

SolventN 32

30

28

S3

26

24

22

20

18

16

14 12

For fixed membrane and 10

8

6 4

process flowsheet, find 2 1

optmal design S6

Chemical product centric sustainable process design - Lecture 1 (extra slides) 6 SPEED Example – Simultaneous product-process design

Find solvent or membrane, flowsheet & optimal design

Y18

DEC Y19

Y20 Y13

Y21 Y14

Y22 Y15

Y23 P1

Y6 Y8

Y1 Y9 D2 Y2 Y7 Feed D1

Y3 For fixed membrane and Y10 Y11

Y12 Y16 process flowsheet, find Y4 Y17 EX

optmal design 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) 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. Phase FAME Oil palm fruit Oil extraction Virgin palm oil - Triglycerides Int8 Organic phase Diesel transesterif. separation purification

Acid-cat. Acid-cat. Glycerol WC palm oil Aqueous phase Glycerol esterification transesterif. purification

Open pond Flocculation Microalgae GR in liquid SFEC Microalgae Int9 Int10 GR-assisted LE Lipids cultivation polyelectrolyt. (int) nitrogen transesterif.

Flocculation US-assisted LE CSEC PBR cultivation DR+US Int11 NaOH by IL transesterif.

Flocculation DR+GR+ US+MW- Int12 PGA MW+US assisted LE

Flocculation - Int13 Wet LE chitosan

Bio- SE (Bligh-Dyer Drying Int14 flocculation method)

SE (modifed Enzymatic Centri-fugation Residue Int15 Fast pyrolysis Bio-oil Bligh-Dyer) hydrolysis

Autofloccula- Supercritical Ethanol Ethanol tion (high pH) fluid extract. fermentation

Microfilt. + Extraction IL Alkaline insitu Anaerobic Biogas centrifugat. mixture transesterif. digestion

Extraction IL Acidic insitu [Bmim][MeSo5] transesterif.

Dry micro- Enzym. insitu - algae (int) transesterif.

ARP: Ammonia Recycle Percolation, AFEX: Ammonia Fiber EXplosion, STEX: STeam EXplosion, LT: Low-Temperature, HT: High Temperature, WC: waste cooking, SFEC: Solvent-Free Enzyme-Catalyzed, CSEC: Co-Solvent Enzyme-Catalyzed, PBR: PhotoBioReactor, 2016 AIChE Annual Meeting | 19 DR: DRying, US: UltraSound, GR: GRinding, MW: MicroWave, LE: Lipid Extraction, SE: Solvent Extraction, IL: Ionic Liquid Application example

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. Phase FAME Oil palm fruit Oil extraction Virgin palm oil - Triglycerides Int8 Organic phase Diesel transesterif. separation purification

Acid-cat. Acid-cat. Glycerol WC palm oil Aqueous phase Glycerol esterification transesterif. purification

Open pond Flocculation Microalgae GR in liquid SFEC Microalgae Int9 Int10 GR-assisted LE Lipids cultivation polyelectrolyt. (int) nitrogen transesterif.

Flocculation US-assisted LE CSEC PBR cultivation DR+US Int11 NaOH by IL transesterif.

Flocculation DR+GR+ US+MW- Int12 PGA MW+US assisted LE

Flocculation - Int13 Wet LE chitosan

Bio- SE (Bligh-Dyer Drying Int14 flocculation method)

SE (modifed Enzymatic Centri-fugation Residue Int15 Fast pyrolysis Bio-oil Bligh-Dyer) hydrolysis

Autofloccula- Supercritical Ethanol Ethanol tion (high pH) fluid extract. fermentation

Microfilt. + Extraction IL Alkaline insitu Anaerobic Biogas centrifugat. mixture transesterif. digestion

Extraction IL Acidic insitu [Bmim][MeSo5] transesterif.

Dry micro- Enzym. insitu - algae (int) transesterif.

ARP: Ammonia Recycle Percolation, AFEX: Ammonia Fiber EXplosion, STEX: STeam EXplosion, LT: Low-Temperature, HT: High Temperature, WC: waste cooking, SFEC: Solvent-Free Enzyme-Catalyzed, CSEC: Co-Solvent Enzyme-Catalyzed, PBR: PhotoBioReactor, 2016 AIChE Annual Meeting | 20 DR: DRying, US: UltraSound, GR: GRinding, MW: MicroWave, LE: Lipid Extraction, SE: Solvent Extraction, IL: Ionic Liquid Application example Ethanol from biomass: Superstructure

PURCHASE BIOMASS RAW MATERIAL WATER ADDITION PRETREATMENT HYDROLYSIS FERMENTATION SEPARATION PURIFICATION PRODUCT SALE LOCATION LOCATION REMOVAL

WADD- DILAC PRE-DILAC WADD- DILAC/CR BRAZIL BRAZIL WADD- SEP2- WS ARP RECTZEO CANADA PRE-ARP CANADA WADD- ARP/CR SEP2- CS HYD- RECTSIL NRELENZ CHINA WADD- CHINA AFEX SEP2- SB PRE-AFEX GLYCER INDIA WADD- HYD- FERM- BIOR- SEP1- ETHANOL AFEX/CR INDIA CONAC ETOH CENTR BEERDIST SEP2- FG SG ETHYL WADD- MEXICO CPH MEXICO PRE-CPH SEP2- HYD- EMIMBF4 HWC WADD- DILAC CPH/CR THAILAND THAILAND SEP2- WADD- CR BMIMCL LIME USA PRE-LIME USA WADD- LIME/CR

BYPASS PRE-STEX

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 | 21 Engineering (Special Issue ESCAPE26). Application example Ethanol from biomass: Different locations yield different solutions

Location RM WADD PRE HYD FERM BIOR SEP1 SEP2 PROD Profit

BR SCB WADD-ARP ARP NREL FERM CENTR BEER BMIM ETOH 11.38

CA WS - STEX NREL FERM CENTR BEER BMIM ETOH -28.78

CN SCB WADD-DA DILAC NREL FERM CENTR BEER BMIM ETOH 37.86

IN SCB WADD-DA DA NREL FERM CENTR BEER BMIM ETOH 82.13

MX WS - STEX NREL FERM CENTR BEER BMIM ETOH -4.46

TH CR - STEX DA FERM CENTR BEER BMIM ETOH 116.03

US HWC - STEX CONCA FERM CENTR BEER BMIM ETOH 47.63

Lowest profit 0 Highest profit Given

RM WADD PRE HYD FERM BIOR SEP1 SEP2 PROD Location Profit

CR - STEX DA FERM CENTR BEER BMIM ETOH TH 116.03

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 | 22 Engineering (Special Issue ESCAPE26). Application example Ethanol from biomass: The output of Stage 1 is the processing route (flowsheet)

PRETREATMENT HYDROLYSIS Off-gas

Gypsum Cassava rhizome Sulfuric acid T1: Lime HP steam Blowdown Sulfuric acid tank C1: Centrifuge C3: Centrifuge R1: Pretreatment reactor R3: R2: Reacidification Overliming reactor reactor

FERMENTATION BIOMASS SEPARATION 1 Off-gas SEPARATION 2 REMOVAL C2: Anhydrous Centrifuge ethanol Waste T2: Blowdown tank

R4: R5: Saccharification Fermentation T5: reactor reactor Solvent recovery

Solid waste T3: Beer T4: distillation Extractive distillation

Waste

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 | 23 Engineering (Special Issue ESCAPE26). Overview of problems & applications

• Synthesis of a new process • Selection of potential products • Residue revalorization • Supply-chain management • Process retrofitting

Specialty • Plant allocation chemicals • … Chemical Water processes management

CO2 utilization

Biorefinery

2016 AIChE Annual Meeting | 24 Overview of problems & applications Synthesis problems in various fields have been solved using Super-O

Problem size Model size Case (problem type) Reference NF NP NI NC NU NR NL NEQ NV (NDV) Network benchmark 2 4 12 5 - 2 1 3,476 3,235 (120) Quaglia et al. (2012) problem (d)

Wastewater network (d) 2 6 24 15 - 37 1 112,147 108,742 (74) Handani et al. (2014)

Sugarcane molasses 1 3 32 12 - 26 1 76,360 73,141 (52) Bertran et al. (2015a) biorefinery (b)

DMC from CO2 (a) 1 5 16 11 - 7 1 8,546 7,985 (26) Frauzem et al. (2015)

Biodiesel biorefinery (d) 3 6 46 27 - 91 1 1,210,227 1,193,507 (182) Bertran et al. (2015b)

MeOH, DME, DMC from 1 8 13 16 - 14 1 51,373 49,573 (60) - CO2 (b) Bertran et al. Bioethanol biorefinery (c) 6 1 35 34 3 47 7 985,666 951,826 (287) (submitted)

(a) (b) (c) (d) (e)

NF: number feedstocks, NP: number products, NI: number intervals, NC: number components, NU: number utilities, NR: number reactions, NL: number locations, NEQ: number equations, NV: number variables, NDV: number discrete variables 2016 AIChE Annual Meeting | 25