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 (Program studi Teknik Kimia UNS, 2025)
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.