Neural network based controller for Cr⁶⁺¨CFe²⁺ batch reduction process

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• 作者：Chew Chun  ; Ming ; M.A.  ; Hussain  ; ;   ; ^{mohd_azlan@um.edu.my} ; M.K.  ; Aroua[Author vitae]
• 关键词：Neural Networks ; ORP ; Batch system ; Redox process
• 刊名：Neurocomputing
• 出版年：2011
• 出版时间：November 2011
• 年：2011
• 卷：74
• 期：18
• 页码：3773-3784
• 全文大小：1648 K

文摘

An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr⁶⁺¨CFe²⁺ reduction process. Simulated data from the Cr⁶⁺¨CFe²⁺ model derived are validated with online data and laboratory analysis using ICP-AES analysis method. The distinctive trend or patterns exhibited in the ORP profiles for the non-equilibrium model derived have been utilized to train neural network-based controllers for the process. The implementation of this process control is to ensure sufficient Fe²⁺ solution is dosed into the wastewater sample in order to reduce all Cr⁶⁺¨CCr³⁺. The neural network controller has been utilized to compare the capability of set-point tracking with a PID controller in this process. For this process neural network-based controller dosed in less Fe²⁺ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr⁶⁺ wastewater samples obtained from an electro-plating factory has also been tested on the pilot plant using the neural network-based controller to determine its effectiveness to control the reduction process for a real plant. The results indicate the proposed controller is capable of fully reducing the Cr⁶⁺¨CCr³⁺ in the batch treatment process with minimal dosage of Fe²⁺.