Photocatalysis based hydrogen production and antibiotic degradation prediction using neural networks

This study investigates the application of an Artificial Neural Network (ANN) to forecast the efficiency of photocatalytic degradation of the antibiotic Amoxicillin (AMX) in the presence of Zirconium dioxide as a catalyst under UV radiation. The evaluation of photocatalytic degradation efficiency is based on the reduction in Chemical Oxygen Demand (COD). The experiments involved varying the pH and using catalyst doses ranging from 0.05 to 0.25 g/L with a duration of 180 min. Remarkably, under natural pH conditions and a catalyst dose of 0.20 g/L, a degradation efficiency of 66.66% was achieved within 0.5 h. Furthermore, under optimized experimental parameters, the photocatalytic process produced 62.7 mu mol/L of hydrogen gas after 90 min. The ANN model effectively predicted degradation efficiency by taking pH, catalyst dose, and time as input variables, and COD removal as the output variable. The study achieved a strong correlation coefficient of 95.00% between the predicted and experimental results, confirming the model's suitability for forecasting the degradation process.