Draft PhD Presentation Robert Brunet

Robert Brunet Page 1 of 45

ROBERT BRUNET SOLÉSupervisors: Dr. Gonzalo Guillén and Dr. Laureano Jiménez

Department of Chemical EngineeringUniversitat Rovira i Virgili, Tarragona

Tarragona, 19th December 2012

Optimal design of sustainable chemical processes via combined simulation-optimization approach


1. Introduction (Ch1)

2. Bioprocesses (Ch 2 & 3)

3. Thermodynamic cycles (Ch 4 & 5)4. Biofuels (Ch 6 & 7)

5. Conclusions (Ch 8)


1. Introduction


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Multi-objective optimization for sustainable

chemical process design

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Case Study IndicatorsTools

Key basis of my research


Aim of the work •Develop systematic tools to achieve reductions in production costs and environmental impact of bioprocesses

• Systematic method based on the combined use of simulation and optmization tools

Main motivation

•Chemical companies need to develop more sustainable processes:

• Plant profitability increase• Emissions and enviromental impact reduction

Aim of the work


Chemical processes


Mathematical Programming

Algebraic eq. (f, h, g)

Linear

Non-linear

Variables (x, y)

Continuous

Discrete {0,1]

LPLinear

Programming

NLPNon-Linear

Programming

MILP Mixed Integer

Linear Programming

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

MINLPMixed Integer

Non-Linear Programming

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

}1,0{,

0),(

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Advanced customized solution methods required


Discrete variables (logical decisions denoting the potential existence of process units)

Process equations:• Non-linear performance of the system (mass and energy balances)• Thermodynamic properties

•Continuous variables:• Flows• Operating conditions (pressures, temperatures, etc.)• Sizes of equipments

•Design specifications (linear inequalities)

Objective functions (cost and environmental impact)

How can we measure the environmental impact

?

Mathematical formulation


Economical Evaluation (Net Present Value or Total Capital Investment or Operating Cost)

Life Cycle Assessment (LCA)

Economic and Environmental Analysis

Life Cycle Assessment

(LCA)

Life Cycle Assessment

(LCA)

Evaluate the environmental loads associated with a product or process

Evaluate the environmental loads associated with a product or process

Quantifying energy and materials used and waste released

Quantifying energy and materials used and waste released

to evaluate opportunities for improvements

to evaluate opportunities for improvements


Translate inventory into damage

• Human health

• Ecosystem quality

• Depletion of resources

Direct emissions from

the process

Express the life cycle inventory as a function of some continuous variables:

Damage in each impact indicator (11 indicators)

Damage in each damage category (3 damage

categories)

Waste generationProduction of raw materials

Operation phase

Construction phase

Process variables (pressures, temperatures,

flows, etc.)

LCA methodology


Combined Simulation-Optimization

Dependent variables:(Heat flow, Area,

Power)

Decision variables(Temperature, Pressure,

mass flow)

Using process simulators instead generic modeling systems…

Index calculator &Constraints evaluation(economic, LCA, etc.)

Optimization solvers

NLP solver (fmincon)

MILP solver (CPLEX)

Process Simulator

(Aspen Plus, Aspen HYSYS, SuperPro)


Environmental Impact

Cost

Multiobjective optimization problems (economic and environmental concerns)

Epsilon constraint methodology:

Solve a set of single objective problems for different values of ε

Epsilon constraint methodology


2. Bioprocesses


0

100

200

300

400

500

600

0 10 20 30 40 50 60

Annual Volume[m3/year] *103

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Health Care

DetergentsFood/feed

Basic Chemicals

Bioprocesses


L-lysine production plant (Heinzle et al, 2006)

Raw materials preparation

Biomass removal and concentration

Bioreactor


Optimization problem (Mathematical formulation)

Mixed Integer Dynamic Optimization (MIDO)

The bioreactor is treated as dynamic, while the rest of the batch process with algebraic equations, involves also discrete decisions.

Time invariant equality and inequality constraints

Objective function (cost and environmental impact)

Set of differential and algebraic equations (DAEs) describes the dynamic system

Initial conditions

Enforce conditions must be satisfied at specific time instances

Problem posed as a MIDO


PRIMAL PROBLEM (NLPk)

END

MASTER PROBLEM (MILPk)Determine plant topology

Initial (NLP)Fixed topology

k=k+1Sup. hyp. + int. cuts

No

COM

NLP solver (determine operating conditions)

Set of differential equations (bioreactor model)

Process model1. Mass & energy balances2. Economic & environmental analysis

COM

Yes

NLP worsening?

Reduced space method


Optimization results Process of L-Lysine Production

Objective function- maximize NPV- minimize Environmental Impact

Decision variable - Threonine Concentration

- Glucose Concentration- Vo reactor- Reaction time- Equiments in parallel (discrete)

Constrains- Production = Demand- Product Purity

NPV improved 13.1%

Combine

Article 1. Hybrid Simulation-Optimization based approach for the Optimal Design of Single-Product Biotechnological Processes. Computers and Chemical Engineering 2012.

Results Base Case

Optimal Case

Net present value [M$] 172.003 195.688

Total capital investment [M$] 101.766 79.885

Operating cost [M$/year] 10.631 8.830

Production rate [ tons MP/year]

6,202 6,202

Batch Throughput [tons MP/batch]

29.647 44.30

Recipe Cycle time [h] 37.51 55.81

Fermentors [equipment] 3 2



END

MASTER PROBLEM (MILPk)Determine plant topology



Yes

No

New epsilon value

COM

NLP solver (determine operating conditions)

Set of differential equations (bioreactor model)

Process model1. Mass & energy balances2. Economic & environmental analysis

COM

Yes

NLP worsening?

Termination criterionNo

Multi-objective Reduced space method


PCA

Reduction 2-dimensional Pareto sets


Minimum EI

EI (YOA )↓Glucose ConsumptionNPV (STY )↑Volume equip. ↑Batch time

Maximum NPV

NPV (STY )↓ Volume equip. ↓ Batch timeEI (YOA )↑Glucose Consumption

Reduced Pareto Set of optimal solutions

Article 2. Cleaner design of single-product biotechnology facilites through the integration of process simulation, multi-objective optimization, LCA and principal component analysis. Industrial & Engineering Chemistry Research 2012.


3. Thermodynamic Cycles


Energy consumption increase in the last 25 years

Increase of 66% in the last 25 years

1981: 6,600 Mtones oil eq.

2006: 11,000 Mtones oil eq.


Optimization of Thermodynamic Cycles

Develop a systematic method for the optimal design of thermodynamic cycles based on the combined use of process simulation and optmization tools

Reduce cycle costsMake a better use of resources

Thermodynamic CyclesPower production Rankine CycleCooling and refrigeration Absorption Cycle

Aim of the work


Decision variables:

(continuous variables) Pressure, Mass flow, Temperature, Composition(discrete variables) Number of trays, Feed tray

Absorption cooling cycle

Absorber

Pump

Desorber

Condenser & subcooler

Evaporator

Cooling capacity



END

MASTER PROBLEM (MILPk)Determines new cycle topology



Yes

No

New epsilon value

NLP solver (determine operating

conditions)

Process model

1. Mass & energy balances2. Economic & environmental analysis

COM

Yes

NLP worsening?


Combined Simulation-optimization


Results Absorption cycle optimization

TAC = 9.35%

Article 3. Combined simulation-optimization methodology for the design of environmental conscious absorption systems. Computers and Chemical Engineering 2012.

Design COP [-] TAC [€/yr] ECO99 [Points]

Cooling

ECO99 0.686 23,445 15,601

Cost 0.629 21,916 16,926

Refrigeration

ECO99 0.516 32,293 20,807

Cost 0.453 28,771 23,451

EI = 7.82%

TAC = 10.90%

TAC = 11.27%


Decision variables:

Pressure, Mass flows, Temperature

(continuous variables)

Modified Steam Rankine Cycle


NLP

ENDYes

New epsilon value

NLP solver (determine operating

conditions)

Process model

1. Mass & energy balances2. Economic & environmental analysis

COM


Combined Simulation-optimization


TAC HH EQ DR

Parallel coordinates plot

minTAC

minHH

minEQ

MinDR

Cost [€] 659.876 689.017 678.386 689.017

HH [Poitns] 18.849 17.901 18.106 17.901

EQ [Points] 10.294 9.881 9.767 9.767

NR [Points] 197.993 189.894 187.646 187.646

EI [Points] 227.136 217.675 215.520 215.314

Min TAC↓ Exchange area↓Turbine size↑Energy consumptionMin impact↑Exchange area↓Energy consumption

Article 4. Minimization of the LCA impact of thermodynamic cycles using a combined simulation-optimization approach. Applied Thermal Engineering 2012.


4. Biofuels


Objectives• Reducing the energy consumption of biofuel plants

through their integration with a solar thermal energy system that generates steam

• Bi-criteria NLP for the simultaneous minimization of cost and energy consumption.

• Two different biofuel processes are optimized a alkali-catalyzed biodiesel process using vegetable oil and a dry-grind corn to bioethanol.

Main motivation• Petroleum-based fuels play a vital role in industrial

development, transportation, agricultural sector and many other human needs.

• To be a viable alternative, a biofuel should provide a net energy gain, have environmental benefits, be economically competitive, and be producible in large quantities without reducing food supplies.

Aim of the work


Process combined with solar collectors


Computer implementation


Solar assisted steam generation system

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Biodiesel production from vegetable oil


Pareto set of biodiesel production plant


Summary of the different design alternatives

Article 5. Reducing the environmental impact of biodiesel production from vegetable oil using a solar assisted steam generation system with heat storage. Industrial & Engineering Chemistry Research 2012.


Dry-grind corn bioethanol production


Pareto set of bioethanol production plant


Summary of the different design alternatives

Article 6. Minimization of the energy consumption in bioethanol production processes using a solar assisted steam generation system with heat storage.


5. Conclusions


Conclusions

General•A new methodology for optimization of chemical processes based on a combined use of simulation and optimization tools

•The methodology introduces the environmental impact (measured following the LCA principles) in the multi-objective optimization

•Very efficient with “non-standard” unit operations (complex reaction kinetics,…) modeled and optimized via external solver

Bioprocesses•The capabilities of this method have been tested in a typical fermentation process and the production of the amino acid L-lysine. From numerical results, we concluded that it is possible to significantly improve the economic and environmental performance of bioprocesses by optimizing them as a whole.


Thermodynamic cycles•The capabilities of this approach were tested in two thermodynamic cycles: a steam power cycle and an ammonia-water absorption cooling cycle, for which we minimized the total annualized cost and a set of environmental impacts measured in three LCA damage categories.

Biofuel•We demonstrate the capabilities of this strategy with two case studies in which we address the design of a 12,000 ton/year alkali-catalyzed biodiesel process using vegetable oil modeled in Aspen Plus and a 120,000 tones/year dry-grind corn-to-ethanol production plant modeled in SuperPro Designer.

•The results obtained show that is possible to achieve reductions in environmental impact up to 15 % for the biodiesel and energy consumption of up to 25% for the bioethanol with respect to the minimum cost design.

Conclusions


Systematic methods based on combined simulation-optimization for the optimal design of chemical processes

Thanks for your Thanks for your attention!attention!

ROBERT BRUNET SOLÉ

Supervisors: Dr. Gonzalo Guillén and Dr. Laureano Jiménez

Department of Chemical Engineering, URV, Tarragona

SUSCAPE

Education

Draft PhD Presentation Robert Brunet