Approximation Algorithms for Process Systems Engineering

Letsios, Dimitrios; Baltean-Lugojan, Radu; Ceccon, Francesco; Mistry, Miten; Wiebe, Johannes; Misener, Ruth

doi:10.1016/b978-0-444-64235-6.50101-7

Cited by 1 publication

(8 citation statements)

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“…Section 9.3 evaluates experimentally the heuristic methods and compares the obtained results with those reported by Furman and Sahinidis (2004). All result tables are provided in Appendix G. Letsios et al (2017) provide test cases and source code for the paper's computational experiments.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We use CPLEX 12.6.3 with default settings to solve all LP models within the heuristic methods. Letsios et al (2017) plot, where the computed objective value is normalized with the one found by CPLEX for the transshipment MILP. Figure 11 shows a box and whisker plot of the CPU times of all heuristics in log 10 scale normalized by the minimum CPU time for each test case.…”

Section: Heuristic Methodsmentioning

confidence: 99%

“…Table G.4 reports bibliographic information on the origin of these test cases. We manually digitize this data set and make it publicly available for the first time (Letsios et al 2017). Table G.4 lists the 26 problem instance names and information on their sizes.…”

Section: System Specification and Benchmark Instancesmentioning

confidence: 99%

“…The number of variables and constraints are computed with respect to the transshipment model. All test cases are available online(Letsios et al 2017).…”

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confidence: 99%

“…CPLEX solves the small models slightly faster than Gurobi while Gurobi closes more of the optimality gap for large problems. All exact method results are available online(Letsios et al 2017).…”

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Heuristics with performance guarantees for the minimum number of matches problem in heat recovery network design

Letsios

Kouyialis

Misener

2018

Computers & Chemical Engineering

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Heat exchanger network synthesis exploits excess heat by integrating process hot and cold streams and improves energy efficiency by reducing utility usage. Determining provably good solutions to the minimum number of matches is a bottleneck of designing a heat recovery network using the sequential method. This subproblem is an N P-hard mixedinteger linear program exhibiting combinatorial explosion in the possible hot and cold stream configurations. We explore this challenging optimization problem from a graph theoretic perspective and correlate it with other special optimization problems such as cost flow network and packing problems. In the case of a single temperature interval, we develop a new optimization formulation without problematic big-M parameters. We develop heuristic methods with performance guarantees using three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy packing. Numerical results from a collection of 51 instances substantiate the strength of the methods.This manuscript is dedicated, with deepest respect, to the memory of Professor C. A. Floudas.Professor Floudas showed that, given many provably-strong solutions to the minimum number of matches problem, he could design effective heat recovery networks. So the diverse solutions generated by this manuscript directly improve Professor Floudas' method for automatically generating heat exchanger network configurations. 1988, Furman and Sahinidis 2002, Escobar and Trierweiler 2013). Floudas et al. (2012) review the critical role of heat integration for energy systems producing liquid transportation * fuels (Niziolek et al. 2015). Other important applications of HENS include: refrigeration systems (Shelton and Grossmann 1986), batch semi-continuous processes (Zhao et al. 1998, Castro et al. 2015 and water utilization systems (Bagajewicz et al. 2002).Heat exchanger network design is a mixed-integer nonlinear optimization (MINLP) problem (Yee and Grossmann 1990, Ciric and Floudas 1991, Papalexandri and Pistikopoulos 1994, Hasan et al. 2010. Mistry and Misener (2016) recently showed that expressions incorporating logarithmic mean temperature difference, i.e. the nonlinear nature of heat exchange, may be reformulated to decrease the number of nonconvex nonlinear terms in the optimization problem. But HENS remains a difficult MINLP with many nonconvex nonlinearities. One way to generate good HENS solutions is to use the so-called sequential method (Furman and Sahinidis 2002). The sequential method decomposes the original HENS MINLP into three tasks: (i) minimizing utility cost, (ii) minimizing the number of matches, and (iii) minimizing the investment cost. The method optimizes the three mathematical models sequentially with: (i) a linear program (LP) (Cerda et al. 1983, Papoulias andGrossmann 1983), (ii) a mixed-integer linear program (MILP) (Cerda and Westerberg 1983, Papoulias and Grossmann 1983), and (iii) a nonlinear program (NLP) (Floudas et al. 1986). The sequential method may not return the global solution of the origi...

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Heuristic Methodsmentioning

confidence: 99%

Section: System Specification and Benchmark Instancesmentioning

confidence: 99%

“…The number of variables and constraints are computed with respect to the transshipment model. All test cases are available online(Letsios et al 2017).…”

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confidence: 99%

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confidence: 99%

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