Title: The use of artificial neural networks to predict furfural degradation in aqueous solution by advanced oxidation processes

Authors: Sinan J. Mohammed; Yasmen A. Mustafa; Ghaidaa Majeed; Raid R. Omran

Addresses: Department of Economics of Oil and Gas, University of Imam Jaafar Al-Sadiq, Baghdad, Iraq ' Department of Economics of Oil and Gas, University of Imam Jaafar Al-Sadiq, Baghdad, Iraq ' Department of Civil Engineering, University of Technology, Baghdad, Iraq ' Al Dora Refinery, Ministry of Oil, Baghdad, Iraq

Abstract: In this study, the wastewater polluted with furfural was treated by advanced oxidation processes. Both batch and continuous systems were used. Different variables in batch experiments, Fe⁺², H₂O₂, pH, furfural concentration and the relation with the mineralisation of furfural were examined. The results indicate that a 30 mg/L concentration of Fe⁺², a 1,300 mg/L concentration of H₂O₂, a pH of 3, and an irradiation time of 60 min at 30°C, were required to complete the mineralisation of 300 mg/L of furfural. In the continuous system, different flow rates were used. The results show that at a furfural concentration of 300 mg/L, a flow rate of 20 mL/min, and an irradiation time of 60 min, only a 64% mineralisation of furfural is achieved. The study examined the implementation of artificial neural networks (ANNs) for the prediction of furfural degradation in aqueous solution. A correlation coefficient of 0.97-0.99 was obtained between experimental and predicted output values.

Keywords: advanced oxidation process; AOP; Fenton process; artificial neuron network; furfural; photo-Fenton.

DOI: 10.1504/IJEWM.2022.121213

International Journal of Environment and Waste Management, 2022 Vol.29 No.2, pp.111 - 132

Received: 15 Aug 2019
Accepted: 22 Jan 2020
Published online: 01 Mar 2022 *

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