An energy‐efficiency evaluation method for high‐sulfur natural gas purification system using artificial neural networks and particle swarm optimization

International Journal of Energy ResearchVolume 46, Issue 3 p. 3213-3232 RESEARCH ARTICLE An energy-efficiency evaluation method for high-sulfur natural gas purification system using artificial neural networks and particle swarm optimization Min Qiu, Min Qiu orcid.org/0000-0002-9668-1757 Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of PetroleumSearch for more papers by this authorZhongli Ji, Zhongli Ji Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of PetroleumSearch for more papers by this authorLimin Ma, Corresponding Author Limin Ma [email protected] orcid.org/0000-0002-7468-8173 Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum Correspondence Limin Ma, Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China. Email: [email protected]Search for more papers by this author Min Qiu, Min Qiu orcid.org/0000-0002-9668-1757 Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of PetroleumSearch for more papers by this authorZhongli Ji, Zhongli Ji Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of PetroleumSearch for more papers by this authorLimin Ma, Corresponding Author Limin Ma [email protected] orcid.org/0000-0002-7468-8173 Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum Correspondence Limin Ma, Beijing Key Laboratory of Process Fluid Filtration and Separation, College of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China. Email: [email protected]Search for more papers by this author First published: 12 October 2021 https://doi.org/10.1002/er.7376 Funding information: National Science and Technology Major Project of the Ministry of Science and Technology of China, Grant/Award Number: 2016ZX05017-004 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Summary Natural gas purification, especially with high sulfur content, is an energy-intensive chemical production process, for which there are considerable differences in the energy consumption patterns of different purification plants. Therefore, these purification plants are required to establish a universal evaluation standard for energy consumption performance. This article proposed a novel approach to evaluate the energy efficiency of the natural gas purification process from the systems engineering perspective. An evaluation system is established for the hierarchical indicators of energy consumption using this technique providing the detailed definition of evaluation indicators for process, unit, and device. At the same time, a technical route is proposed for intelligent algorithm optimization and artificial neural network modeling based on historical operation data of the plant to discover the energy consumption benchmarks under various raw gas flow rates. Using this proposed method, the energy consumption efficiency can be evaluated while analyzing the energy-savings potential of these natural gas purification plants with various process types or raw gas characteristics. Furthermore, the model based on historical operating data can objectively and truly reflect the plant's energy consumption features; therefore, the plant's energy consumption can be decreased to benchmark by adjusting the corresponding operation parameters. Ultimately, the computational process of the energy consumption benchmark is described thoroughly for a high-sulfur natural gas purification plant. CONFLICT OF INTEREST We declare that there is no commercial or associative interest conflicting the interest in connection with the work submitted. Open Research DATA AVAILABILITY STATEMENT Research data are not shared. Volume46, Issue310 March 2022Pages 3213-3232 RelatedInformation