TY - JOUR
ID - 11265
TI - An Efficient Coupled Genetic Algorithm-NLP Method for Heat Exchanger Network Synthesis
JO - Iranian Journal of Chemical Engineering(IJChE)
JA - IJCHE
LA - en
SN - 1735-5397
Y1 - 2008
PY - 2008
VL - 5
IS - 1
SP - 22
EP - 33
KW - Heat exchanger networks (HENs)
KW - optimization
KW - Genetic Algorithm (G.A.)
KW - NLP formulation
DO -
N2 - Synthesis of heat exchanger networks (HENs) is inherently a mixed integer and nonlinear programming (MINLP) problem. Solving such problems leads to difficulties in the optimization of continuous and binary variables. This paper presents a new efficient and robust method in which structural parameters are optimized by genetic algorithm (G.A.) and continuous variables are handled due to a modified objective function for maximum energy recovery (MER). Node representation is used for addressing the exchangers and networks are considered as a sequence of genes. Each gene consists of nodes for generating different structures within a network. Results show that this method may find new or near optimal solutions with a less than 2% increase in Hen annual costs.
UR - http://www.ijche.com/article_11265.html
L1 - http://www.ijche.com/article_11265_299929d99e2ad5849da9b17742059be8.pdf
ER -