替代模型
计算机科学
灵活性(工程)
分类
过程(计算)
遗传算法
接口(物质)
数学优化
建筑
人工智能
算法
机器学习
数学
并行计算
统计
气泡
最大气泡压力法
操作系统
艺术
视觉艺术
作者
Lu Yang,Shuoshi Liu,Chenglin Chang,Siyu Yang,Weifeng Shen
标识
DOI:10.1016/j.ces.2023.119553
摘要
The transience, cyclicity, and flexibility of process operation make the optimization of light olefins separation (LOS) system computationally intensive. An efficient and invertible architecture by incorporating machine learning techniques into optimization routines is proposed. Initially, a steady-state model of the LOS system was built allowing verification with industrial data and invocation through a COM interface. Then, artificial neuron networks (ANNs) were implemented to establish surrogate models for individual fitness calculation. Finally, to find the optimal operating parameters under the targets of energy and economy, an improved non-dominated sorting genetic algorithm was employed along with the ANN-based surrogate models. Optimization results demonstrates that the proposed architecture provides much better performance of computational efficiency with only 0.8 h, while the traditional surrogate models take more than 53 h. Furthermore, the reintroduction of optimized parameters into the LOS system model constructed by Aspen Plus V12.1 demonstrates the satisfactory fulfillment of the separation requirements and affirms the excellent invertibility of the proposed architecture. Essentially, it has been proved to be a powerful tool for multi-objective optimization that can be applied to large-scale chemical processes to derive insights for sustainable development and clean production.
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