DOI: 10.11648/j.ajche.20261404.13 ISSN: 2330-8613

Machine Learning-Based Prediction and Optimization of Membrane Fouling in a Hybrid Biochar-Membrane System Treating Real Cafeteria Wastewater

Uzono Isotuk, Ukpong Abel, Akwayo Job, Anaba Uloma
Show PDF Cite
The main characteristics of cafeteria effluents are the high organic load, varying concentration of fat/oil/grease content and nutrients, thus making them more difficult to treat than traditional domestic sewage. In this study, a combination of biochar-membrane technology was designed to treat real cafeteria wastewater from Akwa Ibom State University in Nigeria and ML for fouling prediction and process optimization. Biochar was prepared by pyrolysis and modified using iron oxide to increase the surface area (185.4 to 312.7 m 2 g -1 ) and functional groups, as indicated by FTIR spectroscopy (Fe-O at 580 cm -1 ), scanning electron microscopy and BET analysis (SEM). Adsorption of the main pollutant (COD) in batch mode followed Langmuir model (qm = 94.3 mg g -1 , R2 = 0.986) and first-order kinetics (R2 = 0.978). Removal of COD, oil and grease, phosphate and turbidity were mainly dependent on biochar dosage and contact time. Two-stage Plackett-Burman/Box-Behnken design (45 runs) was used to produce the data set on which four ML models were developed; XGBoost and artificial neural networks exhibited the best results in terms of predictive performance (R2 = 0.91-0.96) for six response variables, surpassing random forest and support vector regression. Combination of the top-performing model with genetic algorithm, particle swarm and Bayesian optimization was used to find the optimal process parameters (14 g L -1 dose, 105 minutes, TMP 1.05 bar), leading to 89-90% COD removal with minimized membrane fouling, confirmed experimentally within ±5% from the predictions. Biochar pretreatment decreased the fouling resistance as compared to membrane-only process, while preliminary techno-economic evaluation suggested the process cost of about $0.258 m -3 .

More from our Archive