Integrating Bioprocesses into Industrial Complexes for Sustainable Development

Debalina Sengupta Department of Chemical Engineering, Louisiana State University

CO2

Introduction

• Sustainable Development• Overview• Biomass conversion designs• Superstructure formulation• Optimal complex• Case studies• Conclusions

Sustainability“Sustainable development is development that meets the needs

of the present without compromising the ability of future generations to meet their own needs.” – Brundtland Report, United Nations

There are numerous approaches to apply sustainable development by world organizations, countries and industries.

Carbon Dioxide Sequestration (CCS, bio-sequestration, chemical sequestration)

Life Cycle Assessment (LCA)Eco-Efficiency Analysis

Sustainability Indicators: Metrics and Indices

Total Cost Assessment Methodology (TCA)(Economic Costs, Environmental Costs,

Societal Costs)

Industrial Ecology

AIChE Total Cost Assessment Methodology

•Methodology developed by an industry group •Assesses economic, environmental and societal costs•Detailed report on total cost assessment (Constable et al.,

1999).

•Project Team

AD Little (Collab. & Researcher) Bristol-Myers SquibbDOE DowEastman Chemical Eastman KodakGeorgia Pacific IPPC of Business Round Table Merck Monsanto Owens Corning Rohm and Haas SmithKline Beecham (Lead) Sylvatica (TCAce Dev.)

•TCA Users Group created in May 2009. Work is ongoing to update the costs identified in the report.Constable, D. et al., “Total Cost Assessment Methodology; Internal Managerial Decision Making Tool”, AIChE, ISBN 0-8169-

0807-9, July ,1999.

Corporate Sustainability

• A company’s success depends on maximizing profit

• The profit equation expanded to include environmental costs and societal costs to meet the “Triple Bottomline” criteria

Profit = Product Sales – Raw Material Costs – Energy Costs

Triple Bottom Line = Product Sales + Sustainable Credits – Raw Material Costs – Energy Costs

– Environmental Costs – Sustainable Costs

Triple Bottom Line = Profit - Environmental Costs + Sustainable (Credits – Costs)

Industries in Louisiana• Petrochemical complex in the lower Mississippi River Corridor

– Dow– DuPont– BASF– Shell– Exxon– Monsanto– Mosaic– Union Carbide

…. and others

Photo: Peterson, 2000

Objectives of Research

• Identify and design new industrial scale bioprocesses that use renewable feedstock as raw materials with Aspen HYSYS®

• Construct block models of bioprocesses for optimization • Integrate new bioprocesses into a base case of existing plants

to form a superstructure of plants (using the chemical production complex in the Lower Mississippi River Corridor)

• Optimize the superstructure based on triple bottomline• Obtain the optimal configuration of existing and new plants

(chemical complex optimization)• Demonstrate use of the superstructure for parametric studies

Overview

• Biomass based processes integrated into a chemical production complex.

• Utilize carbon dioxide from processes in the integrated complex.• Assign costs to the Triple Bottomline Equation.• Mixed Integer Non-Linear Programming problem

– maximize the Triple Bottomline – multiplant material and energy balances– product demand and raw material availability– plant capacities

• Chemical Complex Analysis System used to obtain optimal solution to the MINLP problem (including Pareto optimal sets)

• Monte Carlo simulation used to determine sensitivity of optimal solution to price of raw materials and products

Biomass Processes

Biomass conversion processes designed for integration into the chemical complex– Fermentation – Anaerobic digestion– Transesterification– Gasification– Algae oil production

Pretreatment of biomass is needed to make feedstock available for conversion to products

Aspen HYSYS® - Process simulationAspen ICARUS Process Evaluator® - Cost Estimation

Proposed Biomass-Based Complex Extension

Natural Oils

Sugars

Starches

Cellulose and Hemicellulose

Transesterification

Fermentation

Enzyme Conversion

Acid or Enzyme Hydrolysis

Gasification

Anaerobic Biodigestion

Methanol

C6 Sugars

C5/C6 Sugars

Syngas

Ammonia

Carbon Nanotubes

Ethylene

1,3- propanediol

Propylene glycol

Polyurethane polyols

Ethanol

Succinic Acid

Acid dehydration

Ethylene derivatives

Levulinic Acid

Acetic AcidCH4

Glycerol

FAME or FAEE

Levulinic acid derivatives

Succinic acid derivatives

Glycerol derivatives

Methanol derivatives

Acetic acid derivatives

EthanolMethanol

Ethanol derivatives

Single Walled CNT

Ammonia derivatives

Butanol Butanol derivatives

Design Description of Transesterification

• 10 million gallons per year 1 of Fatty Acid Methyl Ester (FAME) produced

• FAME is utilized in manufacture of polymers

• Glycerol is used in manufacture of propylene glycol

Transesterification

Glycerol

FAME or FAEE

1 Design based on “A process model to estimate biodiesel production costs”,M.J. Haas et al., Bioresource Technology 97 (2006) 671-678

4250 kg/hr

393 kg/hrNatural Oils

4250 kg/hr

612 kg/hr

Methanol

Transesterification

Thermodynamic model

UNIQUAC

Reactants MethanolSoybean Oil

Catalyst 1.78% (w/w) Sodium Methylate in methanol

Products Methyl EsterGlycerol

Temperature 60oC

Methyl Ester Purification

Wash agents WaterHCl

Glycerol Recovery and Purification

Purification Agents

NaOHWaterHCl

HYSYS Design of Transesterification Process

Methyl ester purificationTransesterification Reaction

Glycerol recovery and purification

Propylene GlycolGlycerol

Design description of Propylene Glycol

Hydrogenolysis

Thermodynamic model UNIQUAC

Reactants GlycerolHydrogen

Catalyst Copper Chromite

Products Propylene GlycolWater

Temperature 200oC

Pressure 200 psi

Hydrogen, 200oC, 200 psi

• The design is based on a process for hydrogenation of glycerol to propylene glycol 1

• ~65,000 metric ton of propylene glycol is produced per year2

1 Design based on experimental results from Dasari, M. A. et al. 2005, Applied Catalysis, A: General, Vol. 281, p. 225-231.2 Capacity based on Ashland/Cargill joint venture of process converting glycerol to propylene glycol

246 kg/hr

15,000 kg/hr 9,300 kg/hr

HYSYS Design of Glycerol to Propylene GlycolHydrogenolysis Reaction

Purification of Propylene Glycol

S3001S3020

S3002

S3003

S3004

S3005

S3021

S3006

S3022

S3023

TRANSESTERIFICATION

Process Flow Design to Block Flow Model for Optimization

Biomass-Based Complex Extension

Base Case of Plants in the Lower Mississippi River Corridor

Plants in the Base Case

•Ammonia•Nitric acid•Ammonium nitrate•Urea•UAN•Methanol•Granular triple super phosphate•MAP & DAP•Sulfuric acid•Phosphoric acid•Acetic acid•Ethylbenzene•Styrene

Integrated Chemical Production Complex

Biomass Complex

Base Case Complex

Air, Methanol, Ammonia

Hydrogen,CO2

CO2

Chemicals like methylamines,

methanol, acetic acid etc. from CO2

Algae growth for use as biomass

Energy Costs

Raw Material Costs

Product Sales

Profit

Environmental Costs

Sustainability (Credits – Costs)

Triple Bottom Line = Profit - Environmental Costs + Sustainable (Credits – Costs)

Superstructure

SuperstructurePlants in Base Case Plants Added to Form the Superstructure (blue)AmmoniaNitric acidAmmonium nitrateUreaUANMethanolGranular triple super phosphate (GTSP)MAP and DAPContact process for sulfuric acidWet process for phosphoric acidAcetic acid – conventional methodEthyl benzeneStyrenePower generation

Bioprocesses and CO2 consumption by Algae (green)Fermentation ethanol (corn stover)Fermentation ethanol (corn)Anaerobic Digestion to acetic acid (corn stover)Algae Oil ProductionTransesterification to FAME and glycerol (soybean oil and algae)Gasification to syngas (corn stover)Ethylene from dehydration of ethanolPropylene glycol from glycerolCO2 consumption for Chemicals (red)Methanol – Bonivardi, et al., 1998Methanol – Jun, et al., 1998Methanol – Ushikoshi, et al., 1998Methanol – Nerlov and Chorkendorff, 1999Ethanol Dimethyl etherFormic acidAcetic acid - new methodStyrene - new methodMethylaminesGraphiteHydrogen/Synthesis gasPropylene from CO2Propylene from propane dehydrogenationChoice for phosphoric acid production and SO2 recovery (yellow)Electric furnace process for phosphoric acidHaifa process for phosphoric acidSO2 recovery from gypsum wasteS and SO2 recovery from gypsum waste

Continuous Variables: 969Integer Variables: 25Equality Constraints: 978 Inequality Constraints: 91

Maximize: Triple Bottom Line

Triple Bottom Line = Profit - Environmental Costs + Sustainable (Credits – Costs)

Subject to: Multiplant material and energy balanceProduct demand Raw material availabilityPlant capacities

Optimal structure obtained by using Global Optimizers

Optimization Problem

Optimal SolutionExisting Plants in the Optimal Structure

New Plants in the Optimal Structure

AmmoniaNitric acidAmmonium nitrateUreaUANMethanolGranular triple super phosphate (GTSP)MAP and DAPContact process for Sulfuric acidWet process for phosphoric acidPower generation

Fermentation to ethanol (corn)Bio-ethylene from dehydration of bio-ethanolTransesterification to FAME and glycerol (soy oil and algae)Algae oil production Bio-propylene glycol from glycerolGasification to syngas (corn stover)Formic acidGraphitePropylene from CO2Propylene from propane dehydrogenation

Existing Plants Not in the Optimal Structure

New Plants Not in the Optimal Structure

Acetic acidEthylbenzeneStyrene

Fermentation to ethanol (corn stover)Anaerobic Digestion to acetic acid (corn stover)Methanol – Bonivardi, et al., 1998Methanol – Jun, et al., 1998Methanol – Ushikoshi, et al., 1998Methanol – Nerlov and Chorkendorff, 1999Methylamines (MMA and DMA)EthanolDimethyl etherHydrogen/synthesis gasAcetic acid – new processStyrene - new methodElectric furnace process for phosphoric acidHaifa process for phosphoric acidSO2 recovery from gypsum wasteS and SO2 recovery from gypsum waste

Comparison of Base Case with Optimal Structure(Triple Bottomline)

Base CaseMillion $/year

Optimal StructureMillion $/year

Income from Sales 2,026 2,490

Economic Costs 697 516

Raw Material Costs 685 470

Utility Costs 12 46

Environmental Costs 457 313

Sustainable Credits(+)/Costs(-) -18 -10

Triple Bottomline 854 1,650

Comparison of Base Case with Optimal Structure(Energy Requirement)

Base Case (TJ/yr) Optimal Structure (TJ/yr)

Ammonia 3,820 3,820Methanol 2,165 1,083Sulfuric acid -14,642 -14,642Wet process phosphoric acid 5,181 5,181Corn Ethanol na 4,158Fatty Acid Methyl Esters na 1,293Others 4,374 5,512 Total Energy 898 6,405

Base Case Optimal Structure-1.66533453693773E-16

0.2

0.4

0.6

0.8

1

1.2

0.75 0.75

0.32

Pure Carbon Dioxide Sources

Pure CO2 (ammonia plant)Pure CO2 (bioprocesses)

mill

ion

met

ric

tons

per

yea

r

Base Case Optimal Structure-1.66533453693773E-16

0.2

0.4

0.6

0.8

1

1.2

0.140.07

0.84

0.16

Pure Carbon Dioxide Consumption

Pure CO2 (existing chemical plants) Pure CO2 (algae)

Pure CO2 (new CO2 chemicals)

mill

ion

met

ric

tons

per

yea

r

1.07 1.07

Base Case Emission (million metric tons per year) : 0.75-0.14 = 0.61Optimal Structure Emission (million metric tons per year) : 1.07-1.07 = 0

Comparison of CO2 use in Base Case and Optimal Structure

Multicriteria Optimization Problem

Maximize: w1P+w2S

P = S Product Sales – S Economic Costs – S Environmental CostsS = S Sustainability (Credits – Costs)

w1 + w2 = 1Subject to:

Multiplant material and energy balanceProduct demand Raw material availabilityPlant capacities

1100 1200 1300 1400 1500 1600 1700-15

-10

-5

0

5

10

15

20

25

30

Pareto Optimal Solutions

Profit (million dollars per year)

Su

stai

nab

le C

red

it(+

)/C

ost(

-)(m

illio

n d

olla

rs p

er y

ear) P=$1,369 M/yr

S=$24.7 M/yrw1: 0.036-0.106

P=$1,660 M/yr S=-$ 9.98 M/yrw1: 0.107-1.000

P=$1,194 M/yrS=$26 M/yrw1: 0.000-0.003

P=$1,346 M/yrS=$25.6 M/yrw1: 0.004-0.035

Sensitivity of Optimal Solution

20% probability of Triple Bottomline equal or below $1,650 million per year80% probability of Triple Bottomline equal or below $2,150 million per year

1200

1300

1400

1500

1600

1700

1800

1900

2000

2100

2200

2300

2400

2500

2600

2700

2800

2900

3000

3100

3200

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%Cumulative Probability of Triple Bottomline

Triple Bottomline (million dollars per year)

Cu

mu

lati

ve P

rob

abili

ty (

%)

$2,150 million/yr

$1,650 million/yr

20%

80%

Case Study ResultCase Study I – Superstructure without carbon dioxide use

Triple bottomline decreased to $984 million per year in optimal structure without CO2 use from $1,650 million per year in optimal structure with CO2 use.

Case Study II – Effect of sustainable costs and credits on the triple bottomline

The highest triple bottomline was $1,700 million per year for CO2 cost of $5 and credit of $50 per MT/ton and the lowest was $1,652 million per year for CO2 cost of $125 and credit of $25 per MT/ton.

Case Study III – Effect of algae oil production costs on the triple bottomline

Comparative study of algae oil production costs based on strain (30% or 50% oil content) and technology (HP,LP,AP). High performance plant for 30% and high and average performance plant for 50% oil content strain were included in optimal solutions. Algae production costs comparable to soybean oil purchased price were included in optimal structure.

Case Study IV – Multicriteria optimization using 30% oil content algae production and sustainable costs/credits

30% oil content high performance and low performance algae oil production with $125/MT CO2 cost and $25/MT CO2 credit. Pareto optimal sets obtained for multicriteria of maximizing profit and sustainable credits.

Case Study V – Effect of corn and corn stover costs and number of corn ethanol plants on the triple bottomline

Corn stover is competitive when corn price is high. Constraints on corn ethanol plants showed that decreasing the number of corn ethanol plants decreased the triple bottomline, as corn stover ethanol plants used more energy and emitted impure CO2.

Case Studies with Superstructure

Summary

• Extend the Chemical Production Complex in the Lower Mississippi River Corridor to include:

Biomass feedstock based chemical productionCO2 utilization from the complex

• Obtained the process designs and constraints• Assigned Triple Bottomline costs:

Economic costsEnvironmental costsSustainable credits and costs

• Solved Mixed Integer Non Linear Programming Problem with Global Optimization Solvers to obtain optimal solution (including Pareto optimal sets)

• Uses Monte Carlo Analysis to determine sensitivity of the optimal solution

Conclusions• Demonstrated a new methodology for the integration of bioprocesses in an existing

industrial complex producing chemicals.– Five processes designed in Aspen HYSYS® and cost estimations performed in

Aspen ICARUS®. – Three processes converted biomass to chemicals, and two processes converted

the bioproducts into ethylene and propylene chain chemicals. – Fourteen bioprocess blocks were integrated into a base case of plants in the

Lower Mississippi River corridor to form a superstructure.

• Optimal configuration was determined by optimizing a triple bottom line profit equation.

– Renewable resources as feedstock and carbon dioxide utilization had the triple bottomline profit increase by 93% from the base case.

– Algae oil production and other chemical processes consumed all the pure carbon dioxide emitted from the complex.

– Sustainable costs to the society decreased by 44% due to complete consumption of pure CO2.

– Total energy required by the optimal complex was 6,405 TJ/yr. – Total utility costs for the complex increased to $46 million per year from $12

million per year in the base case.

Conclusions

• Multicriteria optimization of the complex gave Pareto optimal solutions . A range of profit and sustainable credits/costs was obtained for a range of weights on the multiple objectives.

• Monte Carlo simulations of the complex gave sensitivity of triple bottomline with respect to price of raw materials and products.

• Five case studies demonstrated the use of chemical complex optimization for

sustainability analysis.

• The methodology could be applied to other chemical complexes in the world for reduced emissions and energy savings.

Recommendations

• The methodology can be applied to other chemical complexes of the world. Plants in the Gulf Coast Region (Texas, Louisiana, Mississippi, Alabama) could be included in the base case.

• Raw material availability constraints related to crop cycles and transportation costs can be included in the model (supply-chain).

• Price elasticities can be used as leading indicators to estimate future prices of chemicals in the complex and have optimization over time periods.

• HYSYS designs for algae oil production and gasification processes can be made when more data becomes available for these processes.

Acknowledgements

• Dr. R. Pike, Dr. F.C. Knopf, Dr. J. Romagnoli, Dr. K.T. Valsaraj and Dr. J. Dowling

• The Cain Department of Chemical Engineering, LSU for support

• Tom Hertwig for industrial expertise• Lise Laurin (Earthshift) for Total Cost Assessment

Methodology• Aimin Xu and Sudheer Indala for the base case

Questions

Comments