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Online Controls of Mineral Processing Plants by Neurocomputing

Research Project

Project/Area Number 02452226
Research Category

Grant-in-Aid for General Scientific Research (B)

Allocation TypeSingle-year Grants
Research Field 資源開発工学
Research InstitutionThe University of Tokyo

Principal Investigator

INOUE Toshio  The Univ. of Tokyo, Faculty of Eng., Professor, 工学部, 教授 (80010742)

Co-Investigator(Kenkyū-buntansha) OKAYA Katsunori  The Univ. of Tokyo, Faculty of Eng., Instructor, 工学部, 助手 (80134493)
NONAKA Michio  The Univ. of Tokyo, Faculty of Eng., Instructor, 工学部, 助手 (70010981)
OKANO Yasuhiko  The Univ. of Tokyo, Faculty of Eng., Associate Professor, 工学部, 助教授 (30011092)
Project Period (FY) 1990 – 1992
Project Status Completed (Fiscal Year 1992)
Budget Amount *help
¥7,100,000 (Direct Cost: ¥7,100,000)
Fiscal Year 1992: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1991: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1990: ¥4,400,000 (Direct Cost: ¥4,400,000)
Keywordsneural network / process control / programming / back propagation / process identification / ニュ-ロ / ニュ-ラルネットワ-ク / 鉱物処理 / 制御器の調節 / 学習機能 / 教師信号 / ニュ-ロコンピュ-ティング / 学習 / 鉱物処理プロセス / オンライン制御
Research Abstract

The following items have been investigated in order to assess the potentiality of process control utilizing the learning function of artificial neural networks(ANN): (1) programming language environments fitting the ANN operations, (2) learning methods of the ANN, (3) process identifications by the ANN, (4) controls by the ANN and so on. The details for each item are as below. For (1), a programming language has been successfully developed,which can process a large amount of data and is also provided with high speed array operators. Concerning (2), the calculation formulae for the hierarchical ANN have been arranged and unified. At the same time it has been found that the serial moment method is effective in ANN learning, considering the options for the parameters as well as the learning methods of the ANN. About (3), the two types of research have been conducted; a trial in direct learning of process dynamics and a development of the ANN for estimation of process parameters. In the former, however, any stable solution has never been obtained and there remains a future problem. In the latter almost favorable results have been given except under the disturbance by observation noises. In consideration of (4), the two methods have bee investigated, one of which learns characteristics of the conventional controllers and the other utilizes an inverse model of the process considered. The first method, however, could yield no satisfiable results. In the second one, an optimal control could be attainable with a suitable reference model. Some pieces of useful information as well as some practical methods have been thus obtained for online controls of mineral processing plants by neurocomputing.

Report

(4 results)
  • 1992 Annual Research Report   Final Research Report Summary
  • 1991 Annual Research Report
  • 1990 Annual Research Report

URL: 

Published: 1990-04-01   Modified: 2016-04-21  

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