The heat exchanger network design as a paradigm of technology integration

Tantimuratha, L.; Kokossis, Antonis; Müller, Fabian

doi:10.1016/s1359-4311(00)00025-9

Cited by 23 publications

(3 citation statements)

References 17 publications

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“…The price of the hot utility is assumed at $60/kWyr. The price of furnace fuel is 174 $/kWyr [43]. Reactors are not heat integrated with the rest of the process because of controllability issues.…”

Section: Appendix B Problem Data For Illustrative Examplementioning

confidence: 99%

“…The capital cost for the heat exchanger network is estimated according to Luyben [40] assuming heat exchangers with equal area [44]. The fixed cost of the furnace is estimated according to Tantimuratha et al [43]. The compressors are assumed to perform adiabatic compression of ideal gases with 75% efficiency and are costed according to [40].…”

Section: Appendix B Problem Data For Illustrative Examplementioning

confidence: 99%

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Optimal conceptual design of processes with heterogeneous catalytic reactors

Montolio-Rodriguez

Linke

et al. 2010

Chemical Engineering Journal

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Section: Appendix B Problem Data For Illustrative Examplementioning

confidence: 99%

Section: Appendix B Problem Data For Illustrative Examplementioning

confidence: 99%

Optimal conceptual design of processes with heterogeneous catalytic reactors

Montolio-Rodriguez

Linke

et al. 2010

Chemical Engineering Journal

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“…This approach was also followed by Athier et al, where SA is used at the first level and an NLP solver for the second level optimization. Tantimuratha et al used the solutions from MILP as feasible initial configurations and design pool, which restricts the search space for SA, and gets the optimized networks. Detailed reviews on HEN have been presented by Gundersen and Naess and Furman and Sahinidis …”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimal Design of Heat Exchanger Networks Using New Adaptations of the Elitist Nondominated Sorting Genetic Algorithm, NSGA-II

Agarwal

Gupta

2007

Ind. Eng. Chem. Res.

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A new approach for generating optimal heat exchanger networks (HENs) is described that does not use any heuristics. This approach involves generating the number of intermediate temperatures in each of the hot and cold streams and their values, randomly, using the binary coded NSGA-II-sJG. The substreams so generated are then matched randomly. This procedure results in a variable number of decision variables in each solution (chromosome). Dummy decision variables are introduced so as to make the length of each chromosome the same. A new crossover strategy, crossA, as well as a few other adaptations, are described that enable faster convergence to the optimal solution(s). Three single-objective problems involving the minimization of the annualized cost are solved and the results compared with those reported in the literature. Thereafter, a few problems with two-and three-objective functions are solved. In these, the objective functions are selected from among the annualized cost, the amount of (hot + cold) utilities required (these are important due the environmental issues associated with them), the energy recovery, and the total number of units. To the best of our knowledge, such multiobjective optimization of HENs has not been reported in the open literature yet. A decision maker can choose any of the solutions from among the set of several nondominated (equally good) Pareto-optimal solutions generated. These are more meaningful than those obtained using single objective functions. Though the algorithm developed is specific to HENs, it can easily be applied to other similar optimization problems.

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