OSMOSELife cycle assessment (LCA) Competing indicators Trade-offs assessment Energy integration model ... Survival of the fittest 1. Random choice of set of decision variables Objective

1

OSMOSE

-

Tool for process integration

and optimization

Dr. Laurence TOCKa

aIndustrial Process and Energy Systems Engineering

Ecole Polytechnique Fédérale de Lausanne

[email protected]

mailto:[email protected]

2013 2 OSMOSE EPFL-IPESE

Context

Methodology

Physical model

Energy integration model

Performance evaluation model

Multi-objective optimization

Osmose platform

Concept

Implementation

Documentation

Outline


Rational use and conversion of energy in industrial energy systems

Systematic approach to design complex integrated energy conversion systems

Computer-aided tool for process integration & optimization

Context

Process Resources

Technologies

Products

Services

Process

Configurations Energy

Efficiency Costs

Environmental

Impact


Rational use and conversion of energy in industrial energy systems

Systematic approach to design complex integrated energy conversion systems

Computer-aided tool for process integration & optimization

Context


Illustrative example: SOFC-GT hybrid system

Methodology


Process design platform

Technology models separated from analysis models1

Matlab based platform

Structured data transferred between models

Analysis models

Energy integration

Economic evaluation

Environmental impacts

Methodology

1Bolliger et al. (2009), Gassner et al. (2009), Bolliger et al. (2010), Gerber et al. (2011)

Process

$

ε

GHG

CO2


Process design platform

Technology models separated from analysis models1

Methodology



Platform for studying energy conversion systems2

Methodology

Global problem

Multi-objective

optimization

min fobj(x,z)

h(x,z)=0

g(x,z)≤0

xiL≤xi ≤ xi

U

fobj(x,z)

Pareto set

Obj1

Obj2

Physical model

Energy integration model (MILP resolution)

Economic model & LCA model

WtotfAir

fNG

fsyngas

fexhaust

fH2O

q1

q2

q3

Physical model

Aspen Plus: CO2 capture model

Belsim Vali: Generic reheat

GT model

Belsim Vali: CO2 compression model

W1 W2

q1 q2 q3

q4

fCO2

fH2O

fin

T, P,

Xi, MFG

T,

P,

Xi,

MOG

Model preprocessing

Model (external software)

Model post-processing



Process models

Physical model

Global problem

Physical model

Model preprocessing



xi Process units operation

Physical and chemical transformations

Heat transfer requirement


Process models

Physical model

Global problem

Physical model

Model preprocessing



xi Preprocessing

Selecting process model from superstructure

Transferring decision variables xi to model


Process models

Physical model

Global problem

Physical model

Model preprocessing



xi Simulation phase

Calculating process unit

Solving equations set for given decision variables and unit model parameters


Process models

Process simulation:

different flow sheeting software !

Physical model

Global problem

Physical model

WtotfAir

fNG

fsyngas

fexhaust

fH2O

q1

q2

q3

Physical model

Aspen Plus: CO2 capture model

Belsim Vali: Generic reheat

GT model

Belsim Vali: CO2 compression model

W1 W2

q1 q2 q3

q4

fCO2

fH2O

fin

T, P,

Xi, MFG

T,

P,

Xi,

MOG

Model preprocessing



xi


Process models

Physical model

Global problem

Physical model

Model preprocessing



xi Post-processing

Extracting data from simulation results

Define unit interface with rest of process


Process models

Physical model

Global problem

Physical model

Model preprocessing



xi Model organization

Input (decision variables)

- Output entity

Internal mathematical formulation appearing as black box for process synthesis model


Pinch analysis


Global problem

Physical model

Model preprocessing



xi Best integration of the

process units3

Hot and cold streams definition

Maximal heat recovery

Optimal combined heat and power production

Resolution

Linear programming minimizing operating cost, mechanical power or exergy losses


3Maréchal and Kalitventzeff, Computers & Chemical Engineering 22(1998)

Q, T


Pinch analysis


Global problem

Physical model

Model preprocessing



xi


3Maréchal and Kalitventzeff, Computers & Chemical Engineering 22(1998)

Q, T


Performance evaluation

Economic & environmental model

Global problem

Physical model

Model preprocessing



xi Economic performance

Equipment sizing

Capital investment estimation

Environmental impacts4

Life cycle assessment (LCA)

Competing indicators

Trade-offs assessment


4Gerber and Maréchal Computers & Chemical Engineering 35 (7) (2011)



Multi-objective optimization Moo

Process optimization

Pareto set

Obj1

Obj2

Global problem

Multi-objective

optimization

min fobj(x,z)

h(x,z)=0

g(x,z)≤0

xiL≤xi ≤ xi

U

fobj(x,z)

Physical model



Model preprocessing



MINL problem5

Evolutionary algorithm

Optimal values of decision variables

Pareto frontier

5Molyneaux et al., Energy 35 (2) (2010)



Principles: evolutionary algorithm

Survival of the fittest

1. Random choice of set of decision variables





1. Random choice of set of decision variables

Objective function evaluation

2. Selection of the fittest (best solution with regard to obj.)

3. Generation of new dec. var.

4. Selection of the fittest

5. etc. …





Solutions representation

Pareto frontier

Two main parameters:

number of initial individuals ni

number of total individuals generated by optimization nt

Obviously: ni < nt

No stop criteria


Optimization procedure

Energy

integration

Performance

evaluation OSMOSE

Multi-objective

optimization

Physical model

(Aspen, Vali,…)

Process simulation:

• Mass and energy

balances

Decision

variables

Process

variables

State

Q-T

profiles

Pinch analysis

• Heat cascade resolution

• Optimal utility integration

Utility choice

Utility flow

Energy integr.

results

• Size

• Cost

• LCA

Objective

function

Objective

function

Evolutionary algorithm


The functionalities of OSMOSE are organized in a three-layer architecture

Osmose platform

1. Model interaction layer

2. Analysis and optimization layer

3. Results abstraction and communication layer


1. Model interaction layer

Cross-software communication

Superstructure generation

Osmose platform


2. Analysis and optimization layer

Organize and handle computations

Store results

Osmose platform


3. Results abstraction and communication layer

Results analysis

Results and models sharing

Osmose platform


Frontend

Launches OSMOSE

Model description

Describes the model, software, tags, energy integration

Pre-/Post-computations

Performance calculations

…

Implementation


Command part :

choice OneRun, Sensi, Moo

Model selection:

Energy integration:

Software

Heat cascade

optimization

MER, Exergy losses

OperatingCost

MechanicalPower

Frontend


For details look at EnergyTechnologies doc

Main features

Model files

Software

Tags definition

Energy integration

definition

Model description


Energy integration definition

Order (has to be followed strictly!)

1. Units

2. Streams

3. Groups

Case sensitive .Units(nu).Type= {'process'}

{'utility'}

Model description


Energy integration definition

Streams definition

% Long definition

model.EI.Streams.Type = {'qt'}; % Type of stream.

model.EI.Streams.Hin % Inlet flow enthalpy of the stream [kW]

model.EI.Streams.Hout % Outlet flow enthalpy of the stream [kW]

model.EI.Streams.Tin % Inlet temperature [K]

model.EI.Streams.Tout % Outlet temperature [K]

model.EI.Streams.DTmin_2 % Minimum temperature approach [K]

model.EI.Streams.h % (optional) heat transfer coefficient [kW/Km^2]

model.EI.Streams.AddToProblem % (optional) heat transfer coefficient [kW/Km^2]

Short definition: % type,unit,tag_name, T_in[K], h_in[kW], T_out[K], h_out[kW],deltaTmin

Temperature in [K]

Heat load in [kW]

Model description


Tags structure (Input : cst)

General

Aspen Tags

.DefaultValue has to be given not .Value!

Model Tags


Tags structure (Output : off)

General

Aspen Tags

Model Tags

no .DefaultValue field!


IPESE website -> Resources

IPESE wiki:

Videos to get started

Documentations

Main OSMOSE doc

EnergyTechnologies doc

Introduction to IPESE software

Papers Gassner, Martin, and François Maréchal. ‘Methodology for the Optimal Thermo-

economic, Multi-objective Design of Thermochemical Fuel Production from Biomass’. Computers & Chemical Engineering 33, no. 3 (2009): 769–781.

Gerber, Léda, Martin Gassner, and François Maréchal. ‘Systematic Integration of LCA in Process Systems Design: Application to Combined Fuel and Electricity Production from Lignocellulosic Biomass’. Computers & Chemical Engineering , 2010.

Gerber, Léda, Samira Fazlollahi, and François Maréchal. ‘A Systematic Methodology for the Environomic Design and Synthesis of Energy Systems Combining Process Integration, Life Cycle Assessment and Industrial Ecology’. Computers & Chemical Engineering. 2013.

Documentations

Documents

OSMOSELife cycle assessment (LCA) Competing indicators Trade-offs assessment Energy integration model ... Survival of the fittest 1. Random choice of set of decision variables Objective