Draft PhD Presentation Robert Brunet

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

ROBERT BRUNET SOLÉ
Supervisors: Dr. Gonzalo Guillén and Dr. Laureano Jiménez

Department of Chemical Engineering
Universitat Rovira i Virgili, Tarragona
Tarragona, 19th December 2012

Robert Brunet Page 1 of 45

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


Key basis of my research

Multi-objective
optimization for sustainable
chemical process design

Process Simulation
Mathematical
Programming

Assessment
Evaluation
Chemical
Processes

Economic
Packages

Life Cycle
Case Study Tools Indicators


Aim of the work

Main motivation
•Chemical companies need to develop more sustainable processes:
• Plant profitability increase
• Emissions and enviromental impact reduction

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


Chemical processes


Mathematical Programming

min f ( x, y ) Variables (x, y) Algebraic eq. (f, h, g)

s.t. h( x, y ) = 0 Continuous Linear
Discrete {0,1] Non-linear
g ( x, y ) ≤ 0
x ∈ℜ, y ∈{0,1}

LP NLP MILP MINLP
Linear Non-Linear Mixed Integer Mixed Integer
Programming Programming Linear Programming Non-Linear Programming

● ● ● ● ● ● ● ●
● ● ● ● ● ● ● ●
● ● ● ● ● ● ● ●
● ● ● ● ● ● ● ●

Advanced customized solution methods required


Mathematical formulation

Objective functions (cost and environmental impact)

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

•Design specifications (linear inequalities)

•Continuous variables:
• Flows
• Operating conditions (pressures, temperatures, etc.)
• Sizes of equipments
Discrete variables
(logical decisions denoting the potential existence of process units)

How can we measure the environmental
impact
?

Economic and Environmental Analysis
Economical Evaluation (Net Present Value or Total Capital Investment or Operating Cost)

Life Cycle Assessment (LCA)

Life Cycle Assessment
Life Cycle Assessment Evaluate the environmental loads associated with
Evaluate the environmental loads associated with
a product or process
a product or process
(LCA)
(LCA)
Quantifying energy and materials used to evaluate opportunities for
to evaluate opportunities for
Quantifying energy and materials used
and waste released improvements
improvements
and waste released


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

Process variables (pressures, temperatures,
flows, etc.)

Direct emissions from
the process Production of raw materials Construction phase
Waste generation
Operation phase
Translate inventory into damage
• Human health
• Ecosystem quality
• Depletion of resources

Damage in each impact indicator (11 indicators)

Damage in each damage category (3 damage
categories)


Combined Simulation-Optimization

Using process simulators instead generic modeling
systems…

Process Simulator
(Aspen Plus, Aspen HYSYS, SuperPro)

Dependent variables: Decision variables
(Heat flow, Area, Power) (Temperature, Pressure,
mass flow)

Index calculator & Optimization solvers
Constraints evaluation NLP solver (fmincon)
(economic, LCA, etc.)
MILP solver (CPLEX)


Epsilon constraint methodology

Multiobjective optimization problems (economic and environmental concerns)

Epsilon constraint methodology:
Solve a set of single objective problems for different values of ε

Cost

Environmental Impact


2. Bioprocesses


Bioprocesses

600
Pharma

500
3
Market Price [M$/kg] *10

400 Health Care

300
Detergents
Food/feed
200
Basic Chemicals

100

0
0 10 20 30 40 50 60
3
Annual Volume[m3/year] *10


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

Bioreactor

Raw materials preparation

Biomass removal and concentration


Problem posed as a MIDO
Optimization problem (Mathematical formulation)
The bioreactor is treated as dynamic, while the rest of the batch process with
algebraic equations, involves also discrete decisions.

Mixed Integer Dynamic Optimization (MIDO)
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

Time invariant equality and inequality constraints


Reduced space method

Initial (NLP)
Fixed topology

MASTER PROBLEM (MILPk)
Determine plant topology

PRIMAL PROBLEM (NLPk)
NLP solver (determine operating conditions)
COM
COM Process model
Set of differential equations 1. Mass & energy balances
(bioreactor model) 2. Economic & environmental analysis

No
k=k+1
NLP worsening?
Sup. hyp. + int. cuts
Yes
END


Optimization results Process of L-Lysine Production
Objective function
- maximize NPV
- minimize Environmental Impact
Decision variable
- Threonine Concentration
- Glucose Concentration
Combine - Vo reactor
- Reaction time
- Equiments in parallel (discrete)
Constrains
- Production = Demand
- Product Purity

Results Base Case Optimal Case
Net present value [M$] 172.003 195.688
Total capital investment [M$] 101.766 79.885 NPV improved
Operating cost [M$/year] 10.631 8.830 13.1%
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
Article 1. Hybrid Simulation-Optimization based approach for the Optimal Design of Single-Product
Biotechnological Processes. Computers and Chemical Engineering 2012.

Multi-objective Reduced space method
Initial (NLP)
Fixed topology

Determine plant topology

NLP solver (determine operating conditions)
COM
COM Process model
Set of differential equations 1. Mass & energy balances
(bioreactor model) 2. Economic & environmental analysis

No
k=k+1
NLP worsening?
Yes
No Yes
New epsilon value Termination criterion END


Reduction 2-dimensional Pareto sets

PCA


Reduced Pareto Set of optimal solutions

Minimum EI Maximum NPV
EI (YOA ) NPV (STY )
↓Glucose Consumption ↓ Volume equip. ↓ Batch time
NPV (STY ) EI (YOA )
↑Volume equip. ↑Batch time ↑Glucose Consumption
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

1981: 6,600 Mtones oil eq.
2006: 11,000 Mtones oil eq.
Increase of 66% in the last 25 years


Optimization of Thermodynamic Cycles

Thermodynamic Cycles
Power production  Rankine Cycle
Cooling and refrigeration Absorption Cycle

Reduce cycle costs
Make a better use of resources

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


Absorption cooling cycle

Desorber

Condenser &
subcooler

Evaporator Pump

Cooling Absorber
capacity

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


Combined Simulation-optimization
Initial (NLP)
Fixed topology

Determines new cycle topology


Process model NLP solver
1. Mass & energy balances COM (determine operating
2. Economic & environmental analysis conditions)

No
k=k+1
NLP worsening?
Yes
No Yes


Results Absorption cycle optimization
Design COP [-] TAC [€/yr] ECO99 [Points]
TAC = 9.35%
Cooling
ECO99 0.686 23,445 15,601 EI = 7.82%

Cost 0.629 21,916 16,926
TAC = 10.90%
Refrigeration
ECO99 0.516 32,293 20,807 TAC = 11.27%
Cost 0.453 28,771 23,451

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

Modified Steam Rankine Cycle

Decision variables:
Pressure, Mass flows, Temperature
(continuous variables)


Combined Simulation-optimization

NLP

Process model NLP solver
1. Mass & energy balances COM (determine operating
2. Economic & environmental analysis conditions)

No Yes


Parallel coordinates plot

TAC HH EQ DR

min min min Min
Min TAC TAC HH EQ DR
↓ Exchange area
Cost [€] 659.876 689.017 678.386 689.017
↓Turbine size
HH [Poitns] 18.849 17.901 18.106 17.901
↑Energy consumption
Min impact EQ [Points] 10.294 9.881 9.767 9.767
↑Exchange area NR [Points] 197.993 189.894 187.646 187.646
↓Energy consumption EI [Points] 227.136 217.675 215.520 215.314

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

4. Biofuels


Aim of the work
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.
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.


Process combined with solar collectors


Computer implementation


Solar assisted steam generation system

_ _ _ _ _
Q k ,t + Qk ',t ' + Qk '',t −1 = Qk ''',t + Qk '',t k = col , k' = GFH , k' ' = TES , k' ' ' = B, ∀t
Qk = mNG ·LHV ·ηk k = GFH
_
QTES ,t ≤ CAP ∀t
Qk ,t = Gt ⋅ Ak ⋅ ηk ,t k = col , ∀t
2
 Ttav − Ttamb   Ttav − Ttamb 
η k ,t = ηt 0 − a1 (Tt − Tt ) − a2 
av amb
  − a3 
   , k = col , ∀t

 Gt   Gt 
TOUTk − TINk
Ttav = k = col , ∀t
2

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.


Conclusions
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.


Thanks for your attention!

Systematic methods based on combined simulation-
optimization for the optimal design of chemical processes
ROBERT BRUNET SOLÉ
Supervisors: Dr. Gonzalo Guillén and Dr. Laureano Jiménez

Department of Chemical Engineering, URV, Tarragona
SUSCAPE


Draft PhD Presentation Robert Brunet

Recommended

Recommended

More Related Content

Similar to Draft PhD Presentation Robert Brunet

Similar to Draft PhD Presentation Robert Brunet (20)

More from Robert Brunet

More from Robert Brunet (16)

Recently uploaded

Recently uploaded (20)

Draft PhD Presentation Robert Brunet

Editor's Notes