化工进展 ›› 2023, Vol. 42 ›› Issue (10): 5059-5066.DOI: 10.16085/j.issn.1000-6613.2022-2119

• 化工过程与装备 • 上一篇    下一篇

基于机器学习的煤层气组成预测及液化过程的实时优化

曾思颖(), 杨敏博(), 冯霄   

  1. 西安交通大学化学工程与技术学院,陕西 西安 710049
  • 收稿日期:2022-11-16 修回日期:2022-12-22 出版日期:2023-10-15 发布日期:2023-11-11
  • 通讯作者: 杨敏博
  • 作者简介:曾思颖(1995—),男,硕士研究生,研究方向为化工过程的数字化与最优化。E-mail:sczsy123@163.com
  • 基金资助:
    国家自然科学基金(21908173)

Machine learning-based prediction of coalbed methane composition and real-time optimization of liquefaction process

ZENG Siying(), YANG Minbo(), FENG Xiao   

  1. School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, Shaanxi, China
  • Received:2022-11-16 Revised:2022-12-22 Online:2023-10-15 Published:2023-11-11
  • Contact: YANG Minbo

摘要:

撬装式液化装置是解决小、偏、散煤层气利用难题的途径之一。煤层气性质不断变化,给液化过程的最优操作带来挑战。研究煤层气组分流量的预测可及时提供优化所需的煤层气参数,使实时优化成为可能。本文基于流程模拟和软测量思想,开展煤层气液化过程模拟及煤层气组分流量预测的研究,建立一种混合制冷剂液化过程实时优化方法,并对随机生成的三组气源数据进行案例分析。结果表明:基于流程模拟方法获得的数据集具有较好的一致性及可信度;针对随机森林的调参显示,决策树数量处于20~40时,模型能获得最优或者接近最优的准确度,继续增大这一参数,提升的准确度极为有限;基于预测-优化的方法能够获得近似最优的运行参数,对实际生产的实时调优具有重要意义。

关键词: 计算机模拟, 过程系统, 煤层气, 粒子群, 随机森林

Abstract:

The skid-mounted liquefaction device is a promising way to solve the utilization problem of small, remote, and distributed coalbed methane (CBM). The CBM properties change with time, which brings a challenge to the optimal operation of liquefaction process. The research on the prediction of CBM component flowrate can provide CBM parameters required for optimization in time and make real-time optimization possible. Based on the idea of process simulation and soft measurement, the CBM liquefaction process was simulated and the CBM component flowrate prediction was carried out. A real-time optimization method for the mixed refrigerant liquefaction process was established, and three gas sources generated randomly were analyzed. The results showed that the data set obtained by the process simulation had good consistency and reliability. The parameter tuning of random forest showed that the model can obtain the optimal or near optimal accuracy when the number of decision trees was between 20 and 40. If this parameter continued to increase, the accuracy improvement was limited. The method based on prediction-optimization could obtain near-optimal operating parameters for the CBM liquefaction process, which was of great significance to the real-time optimization of industrial production.

Key words: computer simulation, process systems, coalbed methane, particle swarm, random forest

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