Neural Network Based Modeling and Fuzzy Control of A Chemical Plant for Automatic Gas Yield Control
نویسندگان
چکیده
In this paper, a rectifying tower of a chemical plant is modeled using neural networks and human experts in charge of production control are modeled by fuzzy logic. The neural network models and fuzzy controllers are interconnected for automatic O2 production simulation. Standard multilayer perceptron architecture and backpropagation learning algorithm are used to model a rectifying tower. Real data gathered from operating rectifying tower are used to train and test the neural network models. Two widely used increase and decrease cases of O2 production quantity from 3,000 to 5,000 Nm3/h in rectifying tower is selected in this study. After implementing neural network based rectifying tower, fuzzy logic is used to model human experts and used as a controller instead of human experts. Through experiments using 35 real increase and decrease cases of gas production quantity, the neural network models and fuzzy controller show some possibility to mimic complex real plants and verify that those can be used as useful tools to train unskillful engineers and novices in chemical plants.
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