Development of Computer Control Systems for Molecular Weight Distribution Control in Polymerization Reactors

近年来，合成聚合物在各个领域的使用不断增加，同时也对越来越高的质量提出了严格的要求。聚合物产品的质量与分子量分布密切相关。本文研究了自由基聚合反应器中分子量分布的计算机控制问题，提出了一种方法，通过该方法可以得到合适的聚合温度模式和引发剂加料速度，以达到所需的平均链长和多分散性。该方法包括两个阶段。在第一阶段中，获得瞬时平均链长和瞬时多分散性的曲线，以便产生所需的MWD。在第二阶段，根据得到的瞬时平均链长和瞬时多分散性曲线计算出引发剂的温度分布和加料速度，为了在实践中实现所得到的温度分布，需要一个复杂的计算机控制系统。自由基聚合过程是典型的强非线性化学过程之一。此外，间歇聚合反应在非稳态操作中进行。这种时变过程特性和非线性使得经典PI控制难以跟踪所获得的温度模式。针对这类系统的强非线性和时变特性，提出了两种全新的自适应控制方案（自适应内模控制、自适应推断控制系统）和模型预测控制方案。仿真计算和实验验证了这些控制方案的有效性。*

The recent increase in the use of synthetic polymers in various fields has been accompanied by strict demand for higher and higher quality. The quality of polymer products is closely related with the molecular weigh distribution (MWD). This research focused on the computer control probelm of the molecular weight distribution in a free radical polymerization reactor.We developed a method by which we derive the proper poerating temperature pattern and feeding speed of initiator in order to achieve a desired average chain length and polydispersity. The method consists of two stages. In the first stage, the profiles of instantaneous average chain length and instantaneous polydispersity are obtained so as to produce the desired MWD. In the second stage, temperature pattern and feeding speed of initiator are calculated from the obtained profiles of instantaneous average chain length and instantaneous polydispersity.In order to realize the obtained temperature pattern in practice, a sophisticated computer control system is required. The free radical polymerization process is one of the typical chemical processes with the strong nonlinearities. Furthermore batch polymerization reaction is carried out in non-steady state operation. Such time-varying process characteristics and nonlinearity make it difficult for classical PI control to track the obtained temperature pattern. To deal with the strong nonlinearity and time-dependent properties, two brand-new adaptive control schemes (Adaptive Internal Model Control, Adaptive Inferential Control System) and model predictive control scheme are newly developed. Simulation calculation and experiments were performed to validate these control schemes. *