| Project/Area Number |
06453091
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| Research Category |
Grant-in-Aid for General Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
化学工学一般
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
ISHIDA Masaru Tokyo Inst.of Tech., Res.Lab.of Resources Utilization, Professor, 資源化学研究所, 教授 (10016735)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥5,800,000 (Direct Cost: ¥5,800,000)
Fiscal Year 1995: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1994: ¥5,200,000 (Direct Cost: ¥5,200,000)
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| Keywords | Neural Networks / Cooperative Action / PENN / Scheduling / Process Control / State Prediction / 自己学習 / 多変数システム制御 / 複合ネットワーク / グローバルポリシ- |
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| Research Abstract |
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 network
Several 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 network
The 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 system
Combination 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.
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