Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

为从精炼厂气体的氢恢复的一个二阶段的膜分离过程被介绍。煤气的膜分离过程的原则和操作温度的影响被分析。因为常规 PID 控制器是困难的使操作温度稳定，一个模糊自我调节的 PID 控制算法被建议。申请证明算法是有效的，两个阶段的操作温度能稳定地被控制，并且氢恢复单位的操作灵活性和适应性安全地被提高。这研究打一个基础优化膜分离进程的控制并且因此保证膜性能。

Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases

Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control （APC）. For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function （RBF） neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.