The use of artificial neural networks to predict furfural degradation in aqueous solution by advanced oxidation processes
by Sinan J. Mohammed; Yasmen A. Mustafa; Ghaidaa Majeed; Raid R. Omran
International Journal of Environment and Waste Management (IJEWM), Vol. 29, No. 2, 2022

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.

Online publication date: Tue, 01-Mar-2022

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