半再生催化系统反应性能实时监控、评估与优化

项目名称： 半再生催化系统反应性能实时监控、评估与优化

项目编号： No.61203133

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 徐欧官

作者单位： 浙江工业大学

项目金额： 25万元

中文摘要： 本课题研究半再生催化系统反应性能实时监控、评估与优化的先进控制理论与方法，以保障催化反应系统安全、稳定运行，提高目标产品的收率。首先开发基于相对空间变换PLS和LS-SVM的MIMO混合统计模型评估系统的反应性能，研究多变量主成分提取和信息综合的方法，分析过程变量对反应性能的显著影响关系，查明多变量耦合作用机制，揭示反应性能的动态变化规律；然后提出结合投影技术和快速留一剪切算法的建模样本选择性稀疏策略及相应的递推算法，提高监控模型的实时计算性能和泛化能力，提出异常工况的快速识别方法与预警机制；最后基于定性分析，采用一种新的混合智能算法优化反应性能，提出操作条件的调整策略。因此，本项目具有非常重要的科学研究意义与实际应用价值。

中文关键词： 半再生催化过程；过失误差侦测；模型性能评估；双重校正技术；软测量

英文摘要： The advanced control theory and methods for real-time monitoring, evaluation and optimization of the performance are presented for the semi-regenerative catalytic systems in the project. The objective of the project is to keep the catalytic system safe and the operation stable and to improve the yield of the desired product. The hybrid statistical model with MIMO (Multi-input Multi-output) based on relative space transformation PLS (Partial Least Squares) and LS-SVM (Least Squares Support Vector Machines) was developed to evaluate the system's performance at first. The principal component extraction method of multi-varibles and information integration were studied. Significant effects of the process variables on the performance were analyzed and their coupling effect mechanism was also investigated, thus the dynamic change laws of the performance were eventually explored. Secondly, the strategy of modeling samples selective sparsity combined projection method and fast leave-one-out (FLOO) ctiterion and the corresponding recursive algorithms were proposed to improve real-time computing performance and generalization ability of the monitoring model. Meanwhile the rapid identification and the early warning system of the abnormal operating conditions were then presented. Finally, The system's performance was optimiz

英文关键词： Semi-regenerative catalytic process；Gross error detection；Model performance evaluation；Dual updating strategy；Soft sensor