article · Sustainability
Industrial wastewater containing methylene blue dye poses environmental hazards, but photocatalytic treatment processes often require economic optimisation. This research demonstrates the synthesis of a zinc oxide and magnesium oxide (ZnO/MgO) composite photocatalyst and couples it with artificial intelligence to lower capital and running costs. The material displays an energy band gap of 2.96 eV and a surface area of 30.536 square metres per gram. Using an artificial neural network with a 4-8-1 topology, the model accurately modelled removal efficiency. Operating at the identified optimum conditions achieved over 99 percent dye degradation in 174 minutes. This optimised regime reduced the overall treatment cost to 8.52 US dollars per cubic metre, which is approximately 7 percent cheaper than an unoptimised setup.
Untreated dye pollution endangers human health and aquatic ecosystems, but high operational expenses often deter industrial adoption of advanced oxidation. Demonstrating that artificial intelligence can lower treatment expenditure by 7 percent while retaining 99 percent contaminant removal helps make sustainable industrial wastewater treatment economically viable.
This process is targeted at industrial effluent management, particularly for operations producing dye-contaminated wastewater. Prospective users include industrial plant operators and environmental remediation facilities. The research is currently applied and tested at laboratory scale, meaning engineering scale-up and on-site pilot trials would be needed prior to commercial deployment.
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Recently, removing dyes from wastewater by photocatalysis has been extensively studied by several researchers. However, there exists a research gap in optimizing the photocatalytic process parameters using artificial intelligence to maintain the associated techno-economic feasibility. Hence, this investigation attempts to optimize the photocatalytic degradation of methylene blue (MB) dye using an artificial neural network (ANN) model to minimize the capital and running costs, which is beneficial for industrial applications. A ZnO/MgO photocatalyst was synthesized, showing an energy band gap of 2.96 eV, crystallinity index of 71.92%, pore volume of 0.529 cm3/g, surface area of 30.536 m2/g, and multiple surface functional groups. An ANN model, with a 4-8-1 topology, trainlm training function, and feed-forward back-propagation algorithm, succeeded in predicting the MB removal efficiency (R2 = 0.946 and mean squared error = 11.2). The ANN-based optimized condition depicted that over 99% of MB could be removed under C0 = 16.42 mg/L, pH = 9.95, and catalyst dosage = 905 mg/L within 174 min. This optimum condition corresponded to a treatment cost of USD 8.52/m3 cheaper than the price estimated from the unoptimized photocatalytic system by ≈7%. The study outputs revealed positive correlations with the sustainable development goals accompanied by pollution reduction, human health protection, and aquatic species conservation.
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DOI: 10.3390/su16020529
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