SPEED

Sustainable Process Synthesis- Intensification

Rafiqul Gani Department of Chemical & Biochemical Engineering Technical University of Denmark DK-2800 Lyngby, Denmark [email protected]

IETS Experts Workshop, Berlin, 4-5 April 2017 1 SPEED Chemical and bio-based industry faces enormous challenges to achieve and/or respond to:

Establish Survive sustainable global Raw Product(s) production Materials competition Adopt to Demands for changing Utilities Process Waste innovative markets products

Processes need to be: Sustainable (Economically feasible; Reduced waste; Utility efficient; Environmentally acceptable); Safe; Operable; …….

IETS Experts Workshop, Berlin, 4-5 April 2017 2 SPEED Find innovative solutions Target: Intensify (reduce number of operations) as well as operational costs

Methyl acetate in multifunctional reactor (Eastman Chemicals)

IETS Experts Workshop, Berlin, 4-5 April 2017 3 SPEED Industrial example of process intensification

Deordorization Plant – Alfa Laval, Copenhagen

IETS Experts Workshop, Berlin, 4-5 April 2017 4 SPEED Concept of 3-stages synthesis-design approach Decompose the problem into stages to manage the complexity

Given: set of feedstock & products Given: feasible design (base case) Find: processing route Find: alternative more sustainable design

Define problem Generate sustainable intensified alternatives

Babi et al. (2015) Generate Stage 1 Stage 3 superstructure Synthesis Innovation Mathematical formulation

Solve optimization problem Stage 2 Quaglia et al. (2012) Design Detailed analyses to identify process bottlenecks

Carvalho et al. (2013) Given: processing route Find: feasible design

IETS Experts Workshop, Berlin, 4-5 April 2017 5 SPEED Sustainable Product-Process Development

Raw material

Synthesis stage: find the optimal processing route

IETS Experts Workshop, Berlin, 4-5 April 2017 6 SPEED Synthesis problem solution: 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.

IETS Experts Workshop, Berlin, 4-5 April 2017 7 SPEED Stage-2: Design Stage – CO2 conversion Superstructure for stage -1

CO2 Conversion Network Capture Mixing Intermediate Purification Feedstock Capture Step 1 Capture Step 2 Splitter Mixing 1 Intermediate Production Mixing 2 Product 1 Conversion Purification Mixing 3 Product 2 Conversion Products Mixing Intermediate Production 2 2

H2

BP M3 meohdirsyn S1 MeOH

Purge CH4, H2O

M1 comref meohsynsyn meohsynsyn* S2

CH4 H2 Purge H2O M2 dryref dryref* M4 meohsyn2syn S3

H2 Purge BP M3 meohdirsyn S1 DME CH4, H2O Purge

M1 comref meohsynsyn meohsynsyn* S2 dmesyn S4 MEA, CH4 H2 H2O Purge CO2 Purge Cap coalCO2_1 CapABS1 CapDES1 Splitter M2 dryref dryref* M4 meohsyn2syn S3 M1

Purge Flue MEA, gas H2O CH4 DMC

M2 dryref dryref* dmedirsyn dmedirsyn* dmedirsyn** PurgeS5

H2

BP M3 meohdirsyn S1 NH3

CH4, H2O Purge

M1 comref meohsynsyn meohsynsyn* S2 EG CH4 H2 Purge

M2 dryref dryref* M4 meohsyn2syn S3

Purge PG M8 DMCDirSyn S6

NH3 Purge Model Number of equations 450663 M5 ureasyn M9 DMCUreaSyn S7 C2H4O and Purge M6 ecsyn M10 DMCECSyn S8 Solver C3H6O Number of variables 444505 Purge M7 pcsyn M11 DMCPCSyn S9

Discrete variables 176 Purge Problem type MIP Solver CPLEX IETS Experts Workshop, Berlin, 4-5 April 2017 8 SPEED Sustainable Product-Process Development

Design targets: more profit; less energy consumption; less waste; lower environmental impact; …..

IETS Experts Workshop, Berlin, 4-5 April 2017 9 SPEED Biodiesel production: 2-stage analysis

RCY-3

RCY-1 E-103

T-101

MIX-101 V-100 T-103 V-101

P-102 MIX-100 MIX-103 E-102 T-104 E-100 E-105 Flash-1 R-101 E-101 Cutter-1 P-103 P-101 E-104

OP 3 RCY-2 E-107 OP 4 T-102 Cutter-2 CP 2 Flash-2 Path MVA Probability E-106Path TVA Probability

OP3 –14174.30098 High OP3 –14898.0917 High

OP4 –2047.234859 High - - -

C 2 496.6545095 High C2 496.6545095 High

IETS Experts Workshop, Berlin, 4-5 April 2017 10 SPEED Stage-2: Design analysis

HP steam

Off-gas Fuel gas Purge

HP steam Flue gas Recycle T-101 T-102 F-101 F-102 Air E-102

C-103 E-101 Dry syngas E-104 P-101

Natural gas R-101 R-102 R-103 Clean flue gas

H-101 C-102 Water P-102 E-103 CO2 MEA C-101

F-MEOH

Flue gas PURIFICATION 2

Absorber Desorber PURGE CAPTURE R-GAS STEP 1 CAPTURE RECYCLE STEP 2 F-CO2 PRODUCT 2 CONVERSION V-204 Parameter Value Unit PROD-2 P-DMC F-EO MIXING 1 V-203 F-EC CAPEX 20.39 Million $ V-101 F-MIX 2 PROD-1 R-EC synthesis P-EG EG OPEX 13.41 Million $/year INTERMEDIATE MIXING 3 PRODUCTION V-201 Excess R-DMC synthesis Feed Net CO2 4.97 kgCO2/kgDMC EC route V-202

Indirect CO2 (energy) 4.76 kgCO2/kgDMC Bottleneck:

CO2 utilized 1.79 kgCO2/kgDMC energy intensive separation CO2 direct 2.00 kgCO2/kgDMC

IETS Experts Workshop, Berlin, 4-5 April 2017 11 SPEED Innovative, sustainable & intensfied designs for stage 3

Note: for existing process, stage-1 is not necessary and we start with stage-2 to define the targets for sustainable & innovative design

IETS Experts Workshop, Berlin, 4-5 April 2017 12 SPEED Sustainable Product-Process Development

Design targets: more profit; less energy consumption; less waste; lower environmental impact; …..

IETS Experts Workshop, Berlin, 4-5 April 2017 13 SPEED Tasks to Phenomena (SPB)

R, Ml, MT, MR MV, SPB Interconnection Phenomena In Out 2phM, PC(V-L), PT(V- SPB.1 M 1..n(L) 1(L) L), PT(P:V-L), PS (V- SPB2 M=R 1..n(L) 1(L) SPB.7 M=R=2phM=PC=PT(VL) 1..n(L,VL) 1(V/L) L), D, H, C 13 in SPB.8 M=R=2phM=PC=PT(VL)=PS(VL) 1..n(L,VL) 2(V;L) total SPB.9 M=R=2phM=PC=PT(PVL)=PS(VL) 1..n(L,VL) 2(V;L) SPB.58 D 1(L;VL,V) 1..n(L;V; VL) Reduced from 4017→58 using connectivity rules SPB Interconnection Phenomena In Out Connectivity Rules: M=R=H=C 1..n(L) 1(L) 1. H+C should not exist in the same SPB 2. PC phenomena exists SPB Interconnection Phenomena In Out together with PT SPB.7 M=R=2phM=PC=PT(VL) 1..n(L,VL) 1(V/L) phenomena 3. SPB can contain SPB Interconnection Phenomena In Out simultaneous R and SPB.8 M=R=2phM=PC=PT(VL)=PS(VL) 1..n(L,VL) 2(V;L) separation SPB.9 M=R=2phM=PC=PT(PVL)=PS(VL) 1..n(L,VL) 2(V;L) Lutze et al. (2013)

IETS Experts Workshop, Berlin, 4-5 April 2017 14 SPEED Combine phenomena: New operations

MeAC HOAc

MeOH

H2O Not feasible*

MeAC Feasible* HOAc MeAC MeOH

H2O HOAc

MeOH

H2O * With respect to the target

IETS Experts Workshop, Berlin, 4-5 April 2017 15 SPEED Innovative solution

Non- reactive Zone

Reactive Zone

Non- reactive Zone

IETS Experts Workshop, Berlin, 4-5 April 2017 16 SPEED Design of hybrid –intensified modules

Which is the product design problem?

xD1

xD1 VP

xF CD 1 xF xD2 6

PV

xB1 xB2

xD1

xF Permeate CD xF 2 5

Raffinate

xB1

xD1 xD2 xF1 xF1 RD 3 RD CD 4 xF2 xF2

xB1 xB1 xB2 Close to 50% or more energy reduction compared to original process achievable

IETS Experts Workshop, Berlin, 4-5 April 2017 17 SPEED Stage 3: Targeted rocess improvement

RCY-3

RCY-1 E-103

T-101

MIX-101 V-100 T-103 V-101

P-102 MIX-100 MIX-103 E-102 T-104 E-100 E-105 Flash-1 R-101 E-101 Cutter-1 P-103 P-101 E-104

OP 3 RCY-2 E-107 OP 4 T-102 Cutter-2 CP 2 Flash-2 Path MVA Probability E-106Path TVA Probability

OP3 –14174.30098 High OP3 –14898.0917 High

OP4 –2047.234859 High - - -

C 2 496.6545095 High C2 496.6545095 High

IETS Experts Workshop, Berlin, 4-5 April 2017 18 SPEED Biodiesel production: Identify tasks

Methanol Recycle

Methanol Methanol Mixing Separation Task Separation Task Separation Task Feed Recycle

Wa ter

Waste cooking oil Mixing Reaction Task Separation Task Feed Separation Task Separation Task Biodiesel

Separation Task Methanol Separation Task Recycle

Wa ste Oil Separation Task Wa ter

Glycerol

IETS Experts Workshop, Berlin, 4-5 April 2017 19 SPEED Biodiesel production: Identify phenomena

Methanol Recycle

M, 2phM, C/H, Methanol M, C/H, PC(LL), M, C/H, PC(LL), Methanol M, PC PC(VL), PT(VL), Recycle PS(LL) PS(LL) Feed PS(VL)

Wa ter

M, 2phM, C/H, Waste cooking oil M, 2phM, C/H, M, PC M, 2phM, C/H, R PC(VL), PT(VL), M, C/H, PC(LL), Feed PC(VL), PT(VL), PS(LL) PS(VL) PS(VL) Biodiesel

M, 2phM, C/H, PC(VL), PT(VL), M, 2phM, C/H, PS(VL) Methanol PC(VL), PT(VL), Recycle PS(VL) M, 2phM, C/H, Wa ste Oil PC(VL), PT(VL), Wa ter PS(VL)

Glycerol

Mansouri et al. 2013

IETS Experts Workshop, Berlin, 4-5 April 2017 20 SPEED Identify more sustainable (PI) solutions

RCY-3

RCY-1 E-103

T-101

MIX-101 V-100 T-103 V-101

P-102 MIX-100 MIX-103 E-102 T-104 E-100 E-105 Flash-1 R-101 E-101 Cutter-1 P-103 P-101 E-104

OP 3 RCY-2 E-107 OP 4 T-102 Cutter-2 CP 2 Flash-2 E-106

Mansouri et al. 2013

IETS Experts Workshop, Berlin, 4-5 April 2017 21 SPEED Identify more sustainable (PI) solutions

RCY-3 Methanol Recycle RCY-1 E-103

MethanolT-101 MIX-101 Recycle V-100 T-103 Water V-101

P-102 MeOH MIX-100 MIX-103 E-102 T-104 Biodiesel E-100 E-105 Flash-1 R-101Reactive Distillation E-101 Cutter-1 Column FFA P-103 P-101 E-104 Waste Oil TriOP-glycerides 3 Waste cooking RCY-2 E-107 oil OP 4 T-102 Cutter-2 CP 2 Flash-2 E-106 Methanol Recycle

Water

Glycerol Mansouri et al. 2013

IETS Experts Workshop, Berlin, 4-5 April 2017 22 SPEED Compare more sustainable (PI) alternatives

Base case Intensified Sustainability Metrics %Improvement design alternative Total utility cost ($/year) 7,790,000 4,660,000 40.2 Total energy consumption (GJ/h)Methanol 119.163 73.104 38.6

Recycle product/raw material (kg/kg) 0.94 0.94 0 Energy/ products (GJ/kg) 0.0025 0.0017 32 metrics MethanolNet water added to the system (m3) 0 0 0 Recycle Water for cooling/product (m3/kg) 0.017 0.017 0 Water Waste/raw material (kg/kg) 0.032 0.026 18.8

MeOH Performance Waste/products (kg/kg) 0.034 0.028 17.6 Hazardous raw material/product (kg/kg) 0 0 0 Biodiesel Number of unit operations 9 7 22

Total carbon footprint (kgReactive CO2 eq.) Distillation 0.183 0.143 21.8 HTPI - Human Toxicity PotentialColumn by Ingestion (1/LD ) 0.51811 0.51111 0 FFA 50 3 HTPE - Human Toxicity Potential by Exposure (mgemiaaion/m ) 0.03558 0.03564 0 Waste GWP - Global Warming Potential (CO2 eq.) 0.55214 0.55241 0 Oil ODP - Ozone Depletion Potential (CFC-11 eq.) 5.18E–09 5.18E–09 0

Tri-glycerides Waste cooking PCOP - Photochemicaloil Oxidation Potential (C2H2 eq.) 0.04968 0.04976 0 + LCA AP - Acidification Potential (H eq.) 0.00010 0.00010 0

ATP - Aquatic Toxicity Potential (1/LC50) 0.00366 0.00366 0 Methanol TTP - Terrestrial Toxicity Potential (1/LD ) 0.51811 0.51111 0 50Recycle HTC (Benzene eq.) - human toxicity (carcinogenic impacts) 2062.7 1794.5 13 HTNC (Toluene eq.) - human toxicity (non-carcinogenic impacts)Water 1.3301 1.1795 11.3 ET (2, 4-D eq.) - Fresh water ecotoxicity 0.00525 0.00490 6.7

LC50 is lethal concentration (mgemission/kgfathead minnow) Glycerol LD50 is one kg body weight of rat administered in milligrams of toxic chemical by mouth (mgemission/kgrat) Mansouri et al. 2013

IETS Experts Workshop, Berlin, 4-5 April 2017 23 SPEED Compare more sustainable (PI) alternatives

Base case Intensified Sustainability Metrics %Improvement design alternative Total utility cost ($/year) 7,790,000 4,660,000 40.2

Total energy consumption (GJ/h)Methanol Operational Cost/kg Product 119.163 73.104 38.6

Recycle product/raw material (kg/kg) 100% 0.94 0.94 0 Energy/ products (GJ/kg) 90% 0.0025 0.0017 32 Methanolmetrics 3 80% Net waterHTNC added (kg C6H6to the Eq.) system (m ) Utility0 Cost/kg Product 0 0 Recycle 70% Water for cooling/product (m3/kg) 0.017 0.017 0 Water 60% Waste/raw material (kg/kg) 0.032 0.026 18.8 50% MeOH Performance Waste/products (kg/kg) 0.034 0.028 17.6 40% Hazardous raw material/product (kg/kg) 0 0 0 30% Biodiesel Number of unit operations 9 7 22 20% Total carbon footprint (kgReactive CO2 eq.) Distillation 10% 0.183 0.143 21.8 HTPI - Human Toxicity PotentialColumn by Ingestion (1/LD ) 0.51811 0.51111 0 FFA PCOP (C2H2 Eq.) 0% 50 Energy usuage/kg Product 3 HTPE - Human Toxicity Potential by Exposure (mgemiaaion/m ) 0.03558 0.03564 0 Waste GWP - Global Warming Potential (CO2 eq.) 0.55214 0.55241 0 Oil ODP - Ozone Depletion Potential (CFC-11 eq.) 5.18E–09 5.18E–09 0

Tri-glycerides Waste cooking PCOP - Photochemicaloil Oxidation Potential (C2H2 eq.) 0.04968 0.04976 0 + LCA AP - Acidification Potential (H eq.) 0.00010 0.00010 0

ATP - Aquatic Toxicity Potential (1/LC50) 0.00366 0.00366 0 Methanol TTPCarbon - Terrestrial Footprint (CO2Toxicity Eq) Potential (1/LD ) 0.51811Raw Material cost/kg Product0.51111 0 50Recycle HTC (Benzene eq.) - human toxicity (carcinogenic impacts) 2062.7 1794.5 13 HTNC (Toluene eq.) - human toxicity (non-carcinogenic impacts)Water 1.3301 1.1795 11.3

ET (2, 4-D eq.) - Fresh water ecotoxicity Profit 0.00525 0.00490 6.7 LC50 is lethal concentration (mgemissionBase Case/kgfathead minnowAlt. 1) Alt. 2 Alt. 3 Alt. 4 Glycerol LD50 is one kg body weight of rat administered in milligrams of toxic chemical by mouth (mgemission/kgrat) Mansouri et al. 2013

IETS Experts Workshop, Berlin, 4-5 April 2017 24 SPEED More examples (synthesis of dioxolane products)

Non- reactive Zone

Reactive Zone

Non- reactive Zone

Landero et al. ESCAPE-27, 2017

IETS Experts Workshop, Berlin, 4-5 April 2017 25 SPEED More examples (toluene to p-xylene)

Anantasarn et al, CACE 2017 (PI Special Issue)

IETS Experts Workshop, Berlin, 4-5 April 2017 26 SPEED CACE Special Issue on Process Intensification

Special Issue on process Intensification Guest Editors: Jeff Siirola & Rafiqul Gani

Publication date: June 2017

Highlights: 25 articles covering a wide range of topics within PI – includes the idea of any process design feature that retains the primary process objectives (production rate and fitness-for-use criteria) while also improving one or more performance parameters which could include economics (raw material consumption, energy requirement, labor requirement, other operating costs, equipment capital, working capital, etc.), environmental impacts, physical plant size, employment, flexibility, controllability, robustness, reliability, safety, etc.

Topics covering synthesis issues, modelling issues, design issues, analysis issues, new application examples, process improvement, etc. are included in this special issue.

IETS Experts Workshop, Berlin, 4-5 April 2017 27 SPEED Application example

B-Ibutane Min Purity 99.

Col-3 A-Propane

B- Ibutane-85% C-Nbutane Min Purity 99.5 Ibutane > 99.5 ABCDE Col-1 Col-2 D- Ipentane Min Purity 99.5 Membrane Col-3 A- Propane

Col-4 C- NButane

Col-1 Ya = 0.995 E-Npentane Col-2 Min Purity 99.5 Yb = 0.005 Ipentane- D- 85% Membrane

Xa = 0.005 Xb=0.995

Col-4 Energy reduction: 44.4% in E-NPentane Col-3 and 37.6% in Col-4

IETS Experts Workshop, Berlin, 4-5 April 2017 28 SPEED General problem definition & solution

T Fobj = min {C y + f(x, y, u, d, θ ) + Se + Si + Ss + Hc + Hp}

Process-product model Problems: P = P(f, x, y, d, u, θ) LP, NLP, MILP, MINLP, process simulation, ..... Process-product Solution strategies: 0 = h (x, y) 1 Direct, Equipment-material Decomposition based 0 ≥ g (x, u, d) 1 x: real-process variables; y 0 ≥ g2(x, y) integer-decision variables Flowhseet-chemical alternatives B x + CTy ≥ D

IETS Experts Workshop, Berlin, 4-5 April 2017 29 SPEED Stage-1: Synthesis problem

START

Step 1.1: Problem definition Objective, location(s), etc. - Define objective, raw material(s), product(s), location(s)

Step 1.2: Superstructure generation and data collection

- Generate superstructure (raw materials, products, routes, technologies) Superstructure, - Collect data (mass & energy balance data, location-dependent data) data, model Generic - Check data consistency model - Modify generic model (if necessary)

Step 1.3: Solution of the optimization problem

- Generate input file, solve optimization problem in GAMS, generate output file Optimal network - Perform post-optimality calculations and interpret results and results

No Synthesis objectives met?

Yes

Yes Define new Workflow Tools scenarios? Super-O Data flow interface No

IETS Experts Workshop,To DESIGN Berlin, 4-5 April 2017 30 SPEED Framework for synthesis stage

Need: Superstructure RAW PROCESSING PROCESSING PROCESSING PRODUCTS Problem Superstructure representationMATERIALS 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: Database Model Knowledge 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 management F i,k i,k i,k i,k 1 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 Solution strategy with optimization environment

IETS Experts Workshop, Berlin, 4-5 April 2017 31 SPEED Stage-1: Super-O interface 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

IETS Experts Workshop, Berlin, 4-5 April 2017 32 SPEED Stage-2: Design Stage

Step 2.1: Detailed design and simulation Base case design, stream Perform detailed equipment design and simulation of selected tables, etc. process

Step 2.2: Process optimization Optimized Optimize process operating conditions, design parameters, design, stream etc. (if necessary) tables, etc.

Economic parameters, Step 2.3: Analysis sustainability Perform economic and sustainability analyses indicators, targets for Use hot spots to determine targets for improvement improvement

Stage 3

IETS Experts Workshop, Berlin, 4-5 April 2017 33 SPEED Biorefinery network: Ethanol production

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

IETS Experts Workshop, Berlin, 4-5 April 2017 34 SPEED Biorefinery network: Ethanol production

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

Highest profit Given Lowest profit 0

RM WADD PRE HYD FERM BIOR SEP1 SEP2 PROD Location Profit

CR - STEX DA FERM CENTR BEER BMIM ETOH TH 116.03

IETS Experts Workshop, Berlin, 4-5 April 2017 35 SPEED Stage-2: Design Stage – CO2 conversion Superstructure for stage -1 Raw reaction 1 product Reaction 2 material product Succinic acid Generate CO2 (SA) based reaction Dimethyl paths through Propylene carbonate carbonate (PC) CAMD (DMC) Dimethyl Ethylene carbonate carbonate (EC) (DMC) Captured CO2 Dimethyl Urea carbonate (DMC) Dimethyl Methanol +CO2 carbonate (DMC) Dimethyl carbonate (DMC)

IETS Experts Workshop, Berlin, 4-5 April 2017 36 SPEED Stage-3: Innovation Stage

Parameter Intensified Origina Unit Value l Value CAPEX 18.95 20.39 Million $/year OPEX 13.34 13.41 Million $/year

Net CO2 4.41 4.97 kgCO2/kgDMC

Indirect CO2 (energy) 4.22 4.76 kgCO2/kgDMC

CO2 utilized 1.6 1.79 kgCO2/kgDMC CO direct 1.79 2.00 kgCO2/kgDMC 2 PROD-DMC R-GAS

F-EO

PROD-2 V-203 EXES-FEC

Mixer 1 F-MIX

P-7 Mixer 2 PROD-1 F-CO2 P-EG F-MIX1

V-101 V-201 R-EC synthesis F-MIX2 RD-201 Reactive distillation for DMC synthesis F-MEOH Intensified unit operation

IETS Experts Workshop, Berlin, 4-5 April 2017 37 SPEED Example: Biodiesel production • Two raw material sources (palm oil & waste (cooking) palm oil) having different compositions • Flowsheet depnds on the conversion technique employed (11 different catalysts found) • Same product specifications

Mansouri et al. (2013)

IETS Experts Workshop, Berlin, 4-5 April 2017 38 SPEED Biodiesel production superstructure

Feedstock Pretreatment Reaction Separation 1 Separation 2 Separation 3 Products

Biodiesel

Palm Oil DC DC Wat er Waste oil LLE

R1 R2 R3 D

D D D RMeOH & RWater MeOH

Co-solvent DC

Glycerine S S Acid Catalyst N

F S

Waste Oil

Vent Alkali F gas N

R

S S Glycerol

WC

LD S S

Wat er LW

S

LW LW LW

Wat er Wat er Wat er

E - Esterification Tank LS - Liquid separator Legend: R - Reactor Sep 1 - Methanol recovery Title: Superstructure biodiesel production LLE - Liquid-liquid extraction F - Flash D - Decanter Sep 2 - Water washing and neutralization DC - Distillation column N - Neutralization tank Details: Combination of flowsheets S - Separator Sep 3 - Biodiesel and glycerine purification WC - Washing column LD - Liquid dryer LW - Liquid washing

IETS Experts Workshop, Berlin, 4-5 April 2017 39 SPEED Biodiesel production: Base case analysis

40% RCY-3 35% 35% RCY-1 E-103 30%T-101

MIX-101 26% V-100 T-103 25% V-101

P-102 MIX-100 20% 18% MIX-103 E-102 T-104 E-100 15% E-105 Flash-1 R-101 E-101 Cutter-1

Percentageutility of totalcost 10% P-103 7% E-104 P-101 5% 4% 5% 1% 0% 0% 1% 0% 0% 1% 0% 0% 1% OP 3 0% RCY-2 E-107 OP 4 T-102 Cutter-2 CP 2 Flash-2 Path MVA Probability E-106Path TVA Probability Activity/Unit operation OP3 –14174.30098 High OP3 –14898.0917 High

OP4 –2047.234859 High - - -

C 2 496.6545095 High C2 496.6545095 High

IETS Experts Workshop, Berlin, 4-5 April 2017 40 SPEED Biodiesel production: Base case analysis

40% RCY-3 0,06 35% 35% RCY-1 E-103 30% T-101 0,05 MIX-101 26% V-100 T-103 25% V-101 0,04 P-102 MIX-100 20% 18%

Equivalent Equivalent MIX-103 E-102 T-104 2 E-100 0,03 15% E-105 Flash-1 R-101 E-101 Cutter-1

Percentageutility of totalcost 10% 0,02 P-103 7% E-104 P-101 5% 4% 5% Kilograms CO 1% 0,01 0% 0% 1% 0% 0% 1% 0% 0% 1% OP 3 0% RCY-2 E-107 OP 4 T-102 Cutter-2 Flash-2 CP 2 0,00 Path MVA Probability E-106Path TVA Probability Activity/Unit operation OP3 –14174.30098 High OP3 –14898.0917 High Activity/Unit operation

OP4 –2047.234859 High - - -

C 2 496.6545095 High C2 496.6545095 High

IETS Experts Workshop, Berlin, 4-5 April 2017 41 SPEED Design specification versus energy cost Analysis of design: distillation column

Qc

Qr

Decreasing driving force

Increasing driving force

IETS Experts Workshop, Berlin, 4-5 April 2017 42 SPEED Hybrid distillation + membrane scheme

1 1.4 Membrane separation DF

Distilla tion DF

Reboiler duty 1.3

0.8

Efficient operating region for 1.2 Membrane

0.6 1.1 Efficient operating region for Distillation

Driving Force Force Driving 1 Efficient operating region for 0.4 Efficient operating region for Reboiler duty duty Reboiler GJ/Kg of feed Distillation Membrane

0.9 Ya = 0.90 Ya = 0.999 0.2 Yb = 0.001 0.8

0 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 X Liq mol fraction of Methanol Feed Distillation Membrane

Xa = 0.001 Xb=0.999

Xa = 0.005 Xb=0.999

IETS Experts Workshop, Berlin, 4-5 April 2017 43