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All Journal Equilibrium Journal of Chemical Engineering
Maudy Pratiwi Novia Matovanni
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Chemical Engineering, Chemistry & Bioengineering Chemistry Energy Engineering Materials Science & Nanotechnology

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Machine Learning vs. Real-World Data: Assessing ANN Performance in COD Removal in Animal Feed Processing Wastewater Beta Cahaya Pertiwi; Dean Tirkaamiana; Maudy Pratiwi Novia Matovanni; Ketut Sumada
Equilibrium Journal of Chemical Engineering Vol 9, No 1 (2025): Volume 9, No 1 July 2025
Publisher : Program studi Teknik Kimia UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/equilibrium.v9i1.104192

Abstract

This research established an artificial neural network (ANN) aimed at optimizing ozonation for chemical oxygen demand (COD) reduction in animal feed plant wastewater. Experimental data (200-1000 mg/L COD, 100-180 min treatment) were used to train a 10-8 neuron artificial neural network, resulting in a predicted removal rate of 97.4% at 180 minutes for 1000 mg/L COD (MSE=15.9, R²=0.34).  Experiments indicated a marginally higher efficiency of 97.83% at 160 minutes; however, the ANN's conservative recommendation of 180 minutes is more appropriate for industrial scalability. The model successfully identified non-linear degradation patterns of recalcitrant organics, illustrating the potential of artificial neural networks for optimizing wastewater treatment.  This study connects laboratory research with industrial application via machine learning, establishing a framework that balances efficiency and operational practicality. Future improvements may integrate real-time process data to increase accuracy.
Co-Authors Beta Cahaya Pertiwi Dean Tirkaamiana Ketut Sumada