Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties
In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/52 |
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| author | Xueteng Wang Jiandong Wang Mengyao Wei Yang Yue |
| author_facet | Xueteng Wang Jiandong Wang Mengyao Wei Yang Yue |
| author_sort | Xueteng Wang |
| collection | DOAJ |
| description | In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability. |
| format | Article |
| id | doaj-art-8ee04c5de4464d288ae28799401c0722 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-8ee04c5de4464d288ae28799401c07222025-01-24T13:31:49ZengMDPI AGEntropy1099-43002025-01-012715210.3390/e27010052Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling UncertaintiesXueteng Wang0Jiandong Wang1Mengyao Wei2Yang Yue3College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Rongxin Group Co., Ltd., Zoucheng 273517, ChinaIn gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability.https://www.mdpi.com/1099-4300/27/1/52gas-to-methanol processesmulti-energy systemsentropystochastic optimizationmodeling uncertainties |
| spellingShingle | Xueteng Wang Jiandong Wang Mengyao Wei Yang Yue Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties Entropy gas-to-methanol processes multi-energy systems entropy stochastic optimization modeling uncertainties |
| title | Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties |
| title_full | Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties |
| title_fullStr | Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties |
| title_full_unstemmed | Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties |
| title_short | Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties |
| title_sort | entropy based stochastic optimization of multi energy systems in gas to methanol processes subject to modeling uncertainties |
| topic | gas-to-methanol processes multi-energy systems entropy stochastic optimization modeling uncertainties |
| url | https://www.mdpi.com/1099-4300/27/1/52 |
| work_keys_str_mv | AT xuetengwang entropybasedstochasticoptimizationofmultienergysystemsingastomethanolprocessessubjecttomodelinguncertainties AT jiandongwang entropybasedstochasticoptimizationofmultienergysystemsingastomethanolprocessessubjecttomodelinguncertainties AT mengyaowei entropybasedstochasticoptimizationofmultienergysystemsingastomethanolprocessessubjecttomodelinguncertainties AT yangyue entropybasedstochasticoptimizationofmultienergysystemsingastomethanolprocessessubjecttomodelinguncertainties |