Neural network based controller for Cr6+–Fe2+ batch reduction process
An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr 6+-Fe 2+ reduction process. Simulated data from the Cr 6+-Fe 2+ model derived are validated with online data and laboratory analysis using ICP-AES analysis method. The di...
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Elsevier
2011
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| Online Access: | http://eprints.um.edu.my/7014/ https://doi.org/10.1016/j.neucom.2011.06.027 |
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| author | Chew, Chun Ming Hussain, Mohd Azlan Aroua, Mohamed Kheireddine |
| author_facet | Chew, Chun Ming Hussain, Mohd Azlan Aroua, Mohamed Kheireddine |
| author_sort | Chew, Chun Ming |
| building | UM Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaya |
| content_source | UM Research Repository |
| continent | Asia |
| country | Malaysia |
| description | An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr 6+-Fe 2+ reduction process. Simulated data from the Cr 6+-Fe 2+ 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 Fe 2+ solution is dosed into the wastewater sample in order to reduce all Cr 6+-Cr 3+. 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 Fe 2+ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr 6+ 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 Cr 6+-Cr 3+ in the batch treatment process with minimal dosage of Fe 2+. |
| format | Article |
| id | my.um.eprints-7014 |
| institution | Universiti Malaya |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.um.eprints-70142019-11-15T04:33:46Z http://eprints.um.edu.my/7014/ Neural network based controller for Cr6+–Fe2+ batch reduction process Chew, Chun Ming Hussain, Mohd Azlan Aroua, Mohamed Kheireddine TA Engineering (General). Civil engineering (General) TP Chemical technology An automated pilot plant has been designed and commissioned to carry out online/real-time data acquisition and control for the Cr 6+-Fe 2+ reduction process. Simulated data from the Cr 6+-Fe 2+ 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 Fe 2+ solution is dosed into the wastewater sample in order to reduce all Cr 6+-Cr 3+. 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 Fe 2+ solution compared to the PID controller which hence reduces wastage of chemicals. Industrial Cr 6+ 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 Cr 6+-Cr 3+ in the batch treatment process with minimal dosage of Fe 2+. Elsevier 2011 Article PeerReviewed Chew, Chun Ming and Hussain, Mohd Azlan and Aroua, Mohamed Kheireddine (2011) Neural network based controller for Cr6+–Fe2+ batch reduction process. Neurocomputing, 74 (18). pp. 3773-3784. ISSN 0925-2312, DOI https://doi.org/10.1016/j.neucom.2011.06.027 <https://doi.org/10.1016/j.neucom.2011.06.027>. https://doi.org/10.1016/j.neucom.2011.06.027 doi:10.1016/j.neucom.2011.06.027 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Chew, Chun Ming Hussain, Mohd Azlan Aroua, Mohamed Kheireddine Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title | Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title_full | Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title_fullStr | Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title_full_unstemmed | Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title_short | Neural network based controller for Cr6+–Fe2+ batch reduction process |
| title_sort | neural network based controller for cr6+–fe2+ batch reduction process |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
| url | http://eprints.um.edu.my/7014/ https://doi.org/10.1016/j.neucom.2011.06.027 |
| url_provider | http://eprints.um.edu.my/ |