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Evolution of Chemical Process by Using Neural Networks

使用神经网络进化化学过程

基本信息

批准号：
06453091
负责人：
ISHIDA Masaru
金额：
$ 3.71万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

The aim of this research is the evolution of chemical plant by adopting flexible neural network and self-learning-mechanism as follows :1.Distributed and coorinated neural networkSeveral number of PENN controllers for SISO process are made to work together. The interaction among control variables of a MIMO process can be automatically recognized by the proposed NN controller. Furthermore, a process with long dead time is controlled by adopting model prediction method. By this scheme, The self-learning-mechanism becomes very effective even in the control of complex chemical prosesses.2.Progress on characteristics of neural networkThe state prediciton of bulk polymerization of polystirene within an unsatble region is achieved with PENN.The global policies that indicate the general information on the process consist of several distinct rules. Moreover, the approximated mathematical model of the process is utilized to get detailed global policies. In this scheme, the ability of modeling is significantly improved. The acquired process model is applied to control the process as forward and inverse model, and excellent contorl is achieved.3.A solution of scheduling problems supporting distributed and cooperative systemCombination problem is seen quite often as batch chemical plants grow extensively. The job-shop scheduling problem is one of the typical combination problems. The prompt achievement of an efficient and practical solution is expected to make extensive improvement of productivity and to lead to the decrease in energy consumption. The solution combining GA (Genetic Algorithm) with mechanical searching mechanism is proposed. This solution has high ability in searching preferable solutions than the existing method.

本研究的目的是通过采用灵活的神经网络和自学习机制来实现化工厂的进化，具体如下： 1.分布式协调神经网络SISO过程的多个PENN控制器一起工作。所提出的 NN 控制器可以自动识别 MIMO 过程的控制变量之间的相互作用。此外，采用模型预测方法对长死区时间的过程进行控制。通过该方案，即使在复杂的化学过程的控制中，自学习机制也变得非常有效。2.神经网络特性的进展利用PENN实现了不稳定区域内聚苯乙烯本体聚合的状态预测。指示过程一般信息的全局策略由几个不同的规则组成。此外，利用该过程的近似数学模型来获得详细的全球政策。在该方案中，建模能力显着提高。将获得的过程模型作为正逆模型对过程进行控制，取得了良好的控制效果。3.支持分布式协作系统的调度问题的解决随着间歇式化工厂的广泛发展，组合问题屡见不鲜。车间作业调度问题是典型的组合问题之一。迅速实现高效实用的解决方案有望大幅提高生产率并减少能源消耗。提出了GA（遗传算法）与机械搜索机制相结合的解决方案。该解决方案比现有方法具有更高的搜索更好解决方案的能力。