Methodology using process integration for identifying suitable
Organic Rankine
Cycles for waste heat valorisationMatthias Bendig*, Prof.
François Maréchal, Prof. Daniel Favrat
Industrial Energy Systems Laboratory (LENI)École Polytechnique
Fédérale de Lausanne (EPFL)
*corresponding author: [email protected]
World energy-related CO emission savings by policy measure in
the 450 scenario of the IEA.
Source:IEA WEO 2009.
ContextEnergy efficiency is essential: CO -abatement Cost
efficiency
ProblemThe optimal integration of an electricity production
cycle into a process.
ObjectivePropose a methodology in order to identify a
pareto-optimal ORC: quantitative criteria: efficiencies, cost,
CO2-equivalents, ODP, LCA qualitative criteria: toxicity,
flammability
AcknowledgementsThe authors thank Nestlé Suisse SA for all the
support and letting us “spook around” their factory. The research,
leading to these results, has as well received support from the
European Community's Seventh Framework Programme (FP7/2007-2013)
under grant agreement n° 256790 (‘LOVE').
References
Methodology
How to adapt the ORC to heat sources and sinks of a
process?Which are the relevant Constraints?Many parameters:
Borel, L. and Favrat, D., 2010, Thermodynamics and Energetics
for Engineers. EPFL Press, Switzer-land.International Energy
Agency, 2009, World Energy Outlook, volume 2009. OECD/IEA,
Paris.Maréchal, F., 2008, Pinch analysis, Chapter 3.19.1.7, UNESCO
Encyclopedia of Life Support Systems, EOLSS Publishers Co
Ltd.Muller, D.C.A., Maréchal, F., Wolewinski, T., Roux, P.J.,
November 2007, An energy management method for the food industry,
Applied Thermal Engineering, Volume 27, Issue 16, Pages
2677-2686.
2
2
>50%
Working Fluids and MixturesSingle- or Multi-Stage,
ExtractionOptimal Size
Industrial Process Model for Thermodynamicsof Industrial
Process
Technology Models ofLow Temperature Heat Valorisation (LTHV)
Iterate until
optima found.
DataStudies
Measurements
Deduce energy require-ments and integrate with
Pinch-Analysis
Evaluation of Objectives
ConstraintsThe number of decision variables can be reduced by:
Limiting CO2-equivalents/ GWP Limiting ODP Excluding flammability
Excluding toxicity
The decision variable range can be reduced by: Limiting maximum
pressure Limiting number of ORC-stages Limiting maximum compo-
nents in working fluid
Thermodynamics: - Energy-Efficiency - Exergy-EfficiencyCost
(relative and NPV)Global Warming PotentialOzone Depletion
PotentialLife Cycle Analysis
Mutations
Alleles from high performing “Parent Con�gu-rations”
“Genes” �xing the Con�guration
Evolutionary MultiobjectiveOptimisation (MOO) Pareto-Optimal
Solutions
Obj
ectiv
e A
Objective B
Di�erent solution “families” repre-senting di�erent
Technologies
Solutions are pareto-optimal for at least two objectives.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
11000−50
0
50
100
150
200
250
300
Heat (Q) [kW]
corr
ecte
d Te
mpe
ratu
re (T
*) [°
C]
cold MER 1600 kW hot MER 789.2 kW
internal heat recovery 6150 kW
current cold utilities 3600 kW current hot utility 2789.2 kW
current internal heat recovery 4150 kW
ResidualHeat 1237 kW
