| Project/Area Number |
10555263
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (B).
|
| Allocation Type | Single-year Grants |
|---|
| Section | 展開研究 |
|---|
| Research Field |
化学工学一般
|
|---|
| Research Institution | Tokyo Institute of Technology |
|---|
Principal Investigator |
KURODA Chiaki Tokyo Inst. of Tech., Graduate School of Science and Engineering, Prof., 大学院・理工学研究科, 教授 (80114867)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
MATSUMOTO Hideyuki Tokyo Inst. of Tech., Graduate School of Science and Engineering, Research Assoc., 大学院・理工学研究科, 助手 (90313345)
YOSHIKAWA Shiro Tokyo Inst. of Tech., Graduate School of Science and Engineering, Assoc. Prof., 大学院・理工学研究科, 助教授 (40220602)
OGAWA Kohei Tokyo Inst. of Tech., Graduate School of Science and Engineering, Prof., 大学院・理工学研究科, 教授 (00016635)
|
|---|
| Project Period (FY) |
1998 – 2000
|
| Project Status |
Completed (Fiscal Year 2000)
|
| Budget Amount *help |
¥12,500,000 (Direct Cost: ¥12,500,000)
Fiscal Year 2000: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1999: ¥4,200,000 (Direct Cost: ¥4,200,000)
Fiscal Year 1998: ¥6,800,000 (Direct Cost: ¥6,800,000)
|
|---|
| Keywords | Genetic Algorithm / Neural Network / Nonlinear / Process / Information Processing / Scheduling / Modeling / Reaction Separation System / 反応分離システム / (1)遺伝的アルゴリズム / (2)ニューラルネットワーク / (3)非線形 / (4)プロセス / (5)スケジューリング / (6)モデル化 / (7)反応システム / (8)分離システム / 非線形 / 反応システム / 分離システム |
|---|
| Research Abstract |
This study aims to develop a new hybrid practical system "GANN" that is a new method using a three-layered neural network optimized by a genetic algorithm. This system was applied to operation and scheduling in batch processes with complicated constraints, and to modeling and control in nonlinear reaction-separation processes, and the following results were obtained.
1. In a polymerization process and a fractionation process using liquid chromatography, nonlinear process data could be precisely modeled using an artificial neural network that was a basis of GANN.Through the above investigation, it was made clear that the number of units in a hidden layer was an important factor for flexibility of networks, and an adequate genetic coding method of networks was designed.
2. A dynamic GANN scheduling system was developed for an integrated operational system that flexibly controlled fluctuating productions in real time. This system could appropriately cope with sudden changes of production plans, troubles of equipments and maintenances.
3. The GANN system was applied to designing the structure of a control system for a mixing-tank reactor, and it was found that the system was a useful optimization method for design of process controllers.
The above results made clear that this GANN system was a powerful and flexible tool for modeling, control and operation in nonlinear processes.
|
