在使用人工神经网络的旋转包装床中从天然气中去除的建模
Amiza Surmi1,2, Azmi Mohd Shariff3, Serene Sow Mun Lock3
1Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia.
Molecules (Basel, Switzerland)
|July 29, 2023
概括
旋转包装床 (RPB) 提供了一种经济的方法来从天然气中去除. 人工神经网络 (ANN) 准确地预测了去除效率,而Lavenberg-Marquardt算法显示出最佳性能.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程强化 过程强化
- 分离技术 分离技术
背景情况:
- 天然气生产面临的挑战是含有高污染物如二氧化碳 (CO2) 的低质量油田.
- 天然气中的高度降低了其加热价值,需要去除以满足市场规格.
- 传统的去除方法可能耗费大量能源,需要大型设备.
研究的目的:
- 研究旋转包装床 (RPB) 技术在冷却条件下有效地从天然气中去除的潜力.
- 开发和验证人工神经网络 (ANN) 模型,用于预测RPB过程中的去除性能.
- 将RPB技术的经济可行性和设备足迹与传统方法进行比较.
主要方法:
- 在冷却条件下使用旋转包装床 (RPB) 来从天然气中分离.
- 开发了一个人工神经网络 (ANN) 模型,使用实验数据来预测去除效率.
- 评估了各种ANN反向传播算法,包括Lavenberg-Marquardt (LM),缩放联梯度 (SCG) 和贝叶斯规范化 (BR).
主要成果:
- RPB工艺显示出大量的除潜力,为传统蒸提供了更紧,更经济的替代方案.
- 开发的ANN模型准确地预测了去除,而Lavenberg-Marquardt (LM) 算法获得了最高的准确性.
- 最好的基于LM的ANN模型实现了99.56%的回归系数 (R2) 和0.0128.8的平均标准误差 (MSE).
结论:
- RPB技术是一个有前途的强化方法,可以从天然气中节省成本,有效地去除气,特别是在高二氧化碳矿中.
- 在这种情况下,ANN建模为预测和优化去除过程提供了强大而高效的工具,其性能优于第一原则模型.
- 在ANN中的LM算法为RPB系统中的去除提供了卓越的预测能力,促进了过程设计和操作.
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