Neural network based controller for Cr6+–Fe2+ batch reduction process

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

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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+.

Item Type: Article
Additional Information: Export Date: 5 March 2013 Source: Scopus CODEN: NRCGE Language of Original Document: English Correspondence Address: Hussain, M.A.; Department of Chemical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; email: Chemicals/CAS: chromium, 14092-98-9, 16065-83-1, 7440-47-3; ferrous ion, 15438-31-0 References: Eary, L.E., Rai, D., Chromate removal from aqueous waste by reduction with ferrous ion (1988) Environmental Science and Technology, 22, pp. 972-977; Hussain, M.A., Review of the applications of neural networks in chemical process control-simulation and online implementation (1999) Artificial Intelligence in Engineering, 13, pp. 55-68; Arundhati, P., Paul, A.K., Aerobic chromate reduction by chromium-resistant bacteria isolated from serpentine soil (2004) Microbiological Research, 159 (4), pp. 347-354; Gheju, M., Iovi, A., Kinetics of hexavalent chromium reduction by scrap iron (2006) Journal of Hazardous Materials, 135 (1-3), pp. 66-73; Wang, T., Zuohu, L., High-temperature reduction of chromium (VI) in solid alkali (2004) Journal of Hazardous Materials, 112 (1-2), pp. 63-69; Mustafa, M.M., Abdullah, S.R., Rahman, R.A., Robust on-line control of hexavalent chromium reduction process using a Kalman filter (2002) Journal of Process Control, 12, pp. 405-412; Brydson, J.A., (1997) Plastic Materials, , Butterworth Heinemann; Clevett, K.J., (1986) Process Analyzer Technology, , John Wiley and Sons; Filer, S., Power Plant Chemistry Measurement Advancements: Oxidation Reduction Potential (1998) Ultrapure Water, 15 (9), pp. 53-62; Aroua, M.K., Chew, C.M., Hussain, M.A., Modelling of chromium hexavalent reduction by ferrous ion in a batch stirred tank (2009) Chemical Product and Process Modeling, 4 (1). , (Article 12); Wahab, A.K., Hussain, M.A., Omar, R., Development of PARS-EX pilot plant to study control strategies (2009) Control Engineering Practice, 17, pp. 1220-1233; Callan, R., (1999) The Essence of Neural Networks, , Prentice Hall Europe; Lim, J.S., Hussain, M.A., Aroua, M.K., Control of a hydrolyzer in an oleochemical plant using neural network based controllers (2010) Neurocomputing, 73, pp. 3242-3255; Ekpo, E.E., Mujtaba, I.M., Evaluation of neural networks-based controllers in batch polymerisation of methyl methacrylate (2008) Neurocomputing, 71, pp. 1401-1412; Hussain, M.A., Kershenbaum, L.S., Implementation of an inverse-model-based control strategy using neural networks on a partially simulated exothermic reactor (2000) Chemical Engineering Research and Design, 78, pp. 299-311; Daosud, W., Thitiyasook, P., Arpornwichanop, A., Kittisupakorn, P., Hussain, M.A., Neural network inverse model-based controller for the control of a steel pickling process (2005) Computers & Chemical Engineering, 29, pp. 2110-2119; Mujtaba, I.M., Aziz, N., Hussain, M.A., Neural network based modelling and control in batch reactor (2006) Chemical Engineering Research & Design, 84, pp. 635-644
Uncontrolled Keywords: Batch system, Neural Networks, ORP, Redox process, Analysis method, Automated pilot plants, Batch systems, ICP-AES, Laboratory analysis, Network-based controllers, Neural network controllers, Nonequilibrium model, Online data, PID controllers, Reduction process, Set-point tracking, Simulated data, Treatment process, Wastewater samples, Atomic emission spectroscopy, Computer simulation, Controllers, Data acquisition, Electric control equipment, Pilot plants, Proportional control systems, Wastewater, Process control, chromium, ferrous ion, article, artificial neural network, atomic emission spectrometry, automation, batch process, electroplating industry, online monitoring, oxidation reduction potential, priority journal, reduction, simulation, waste water.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 01:27
Last Modified: 15 Nov 2019 04:33

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