Abstract:

Aiming at the problem of optimal operation of the process system, a global self-optimizing control (SOC) strategy based on Monte Carlo simulation was introduced. The nonlinear model was employed to calculate the average economic loss over the entire operating space. Reasonable assumptions were made for some conditions, and the analytical solution of the controlled variables (CVs) was obtained. Besides, mixed integer constraints were incorporated into the global SOC strategy with the aim of balancing the sensor investment and control system performance. The proposed method can handle additional structural constraints as well as determine the optimal subset of measurements with globally valid CVs. An evaporation process was investigated to show the effectiveness of dealing with the measurement subset selection, and a distillation column case was studied to illustrate the advantages of handling the structural constraints problem.

Key words: process systems, Monte Carlo simulation, self-optimizing control, nonlinear model, optimization, mixed integer constraints, subset selection

摘要：

针对过程系统的优化运行问题，介绍一种基于Monte Carlo模拟的全局自优化控制策略。利用非线性模型计算整个操作空间内的平均经济损失，通过对某些条件进行合理假设，得到全局被控变量的解析表达形式。为了平衡传感器成本和系统性能，在全局自优化控制策略的基础上，引入混合整数约束，对测量变量子集进行选择。通过求解混合整数规划问题，能够同时获得最优的测量变量子集以及由其构成的全局被控变量，此外上述子集选择方法还可以处理附加的结构性约束问题。通过对蒸发过程的研究表明，该方法可以更加高效地处理测量变量子集选择问题，通过对精馏塔案例的研究，进一步验证了该方法在处理结构性约束问题中的优势。

关键词: 过程系统, Monte Carlo模拟, 自优化控制, 非线性模型, 优化, 混合整数约束, 子集选择