Efficient algorithm for simultaneous synthesis of heat exchanger networks

The mathematical programming based simultaneous approaches for heat exchanger network synthesis (HENS) result in large, complex, non-convex mixed-integer nonlinear programming (MINLP) models, for which finding even a feasible solution is a challenge. We propose a tailor-made search strategy that rep...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Chemical engineering science Ročník 105; s. 53 - 68
Hlavní autori: Huang, Ke feng, Karimi, I.A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 24.02.2014
Predmet:
Algorithms
Computational
Design
Genetic algorithms
Heat exchanger network synthesis (HENS)
Heat exchangers
Mathematical modeling
Mathematical models
Mixed-integer nonlinear programming (MINLP)
Network synthesis
Optimization
Searching
Strategy
Streams
BB
PP
AMTD
Mixed-integer nonlinear programming (MINLP)
HENS
MP
OA/ER/AP
MILP
GBD
RMINLP
MINLP
Heat exchanger network synthesis (HENS)
Optimization
HEN
Design
OA
ECP
NLP
TAC
Mathematical modeling
HE
Computational
ISSN:0009-2509, 1873-4405
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The mathematical programming based simultaneous approaches for heat exchanger network synthesis (HENS) result in large, complex, non-convex mixed-integer nonlinear programming (MINLP) models, for which finding even a feasible solution is a challenge. We propose a tailor-made search strategy that repeatedly revives the outer approximation (OA) algorithm of Viswanathan and Grossmann (1990), which in its original form is mostly ineffective for solving large HENS problems. We propose three smaller and simpler perturbations of the master problem in the OA algorithm by prioritizing, fixing, eliminating, or limiting exchangers in various ways to avoid premature termination. Our approach needs no feasible starting point, and solves much faster and better than some commercial MINLP solvers. We illustrate the application of our strategy with two recent HENS models on seven literature examples with up to 39 process streams. The algorithm solves them very efficiently and obtains solutions as good or better than those reported in the literature. Its robustness and effectiveness are exemplified by a large literature problem involving 39 process streams, where it obtains a 0.32% better solution than the best reported in the literature via a genetic algorithm. •Iterative mathematical programing based strategy for simultaneous HENS.•Modified outer-approximation algorithm with no need for feasible start point.•Good or better solutions for seven literature HENS examples.•0.32% lower cost than the best reported via genetic algorithm for a 39-stream example.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0009-2509
1873-4405
DOI:10.1016/j.ces.2013.10.040