<![CDATA[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.]]>
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