LAPSE:2021.0093
Published Article
LAPSE:2021.0093
MILP Formulation for Solving and Initializing MINLP Problems Applied to Retrofit and Synthesis of Hydrogen Networks
Patrícia R. da Silva, Marcelo E. Aragão, Jorge O. Trierweiler, Luciane F. Trierweiler
March 1, 2021
The demand for hydrogen in refineries is growing due to its importance as a sulfur capture element. Therefore, hydrogen management is critical for fulfilling demands as efficiently as possible. Through mathematical modeling, hydrogen network management can be better performed. Cost-efficient Mixed-Integer Linear Programming (MILP) and Mixed-Integer Nonlinear Programming (MINLP) optimization models for (re)designing were proposed and implemented in GAMS with two case studies. Linear programming has the limitation of no stream mixing allowed; therefore, to overcome this limitation, an algorithm-based procedure called the Virtual Compressor Approach was proposed. Based on the MILP optimal solution obtained, the streams and compressors were merged. As a result, the number of compressors was reduced, along with the inherent investment costs. An operational cost reduction of more than 28% (example 1) and 26% (example 2) was obtained with a linear model. The optimal MILP solution after rearranging compressors was then provided as a good starting point to the MINLP. The operating costs were decreased by more than 31% (example 1) and 32% (example 2). Most of the cost reduction was obtained only with the usage of the MILP model. Besides, a higher level of cost reduction was only obtained when the linear model was used as the starting point.
Keywords
hydrogen network, initialization strategy, mathematical programming, MILP optimization, MINLP optimization, virtual compressor approach
Suggested Citation
Silva PRD, Aragão ME, Trierweiler JO, Trierweiler LF. MILP Formulation for Solving and Initializing MINLP Problems Applied to Retrofit and Synthesis of Hydrogen Networks. (2021). LAPSE:2021.0093
Author Affiliations
Silva PRD: Group of Intensification, Modeling, Simulation, Control, and Optimization of Processes, Chemical Engineering Department, Federal University of Rio Grande do Sul (UFRGS), R. Eng. Luiz Englert, s/n, Campus Central, Porto Alegre 90040-060, RS, Brazil
Aragão ME: Food and Chemistry School, Federal University of Rio Grande (FURG), R. Barão do Cahy, 125, Cidade Alta, Santo Antônio da Patrulha 95500-000, RS, Brazil
Trierweiler JO: Group of Intensification, Modeling, Simulation, Control, and Optimization of Processes, Chemical Engineering Department, Federal University of Rio Grande do Sul (UFRGS), R. Eng. Luiz Englert, s/n, Campus Central, Porto Alegre 90040-060, RS, Brazil
Trierweiler LF: Group of Intensification, Modeling, Simulation, Control, and Optimization of Processes, Chemical Engineering Department, Federal University of Rio Grande do Sul (UFRGS), R. Eng. Luiz Englert, s/n, Campus Central, Porto Alegre 90040-060, RS, Brazil
Journal Name
Processes
Volume
8
Issue
9
Article Number
E1102
Year
2020
Publication Date
2020-09-04
Published Version
ISSN
2227-9717
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Original Submission
Other Meta
PII: pr8091102, Publication Type: Journal Article
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LAPSE:2021.0093
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doi:10.3390/pr8091102
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Calvin Tsay
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