Employing CNN and black widow optimization for sustainable wastewater management in an environmental engineering context

The study explores the application of convolutional neural networks (CNN) trained with black widow optimization (BWO) algorithms to enhance eco-friendly wastewater treatment processes. This study aims to address the pressing need for improved predictive models in environmental engineering by utilizing advanced AI techniques to explore the complexities of wastewater treatment. This work aims to enhance models for hyperparameter tuning using BWO and to utilize CNNs for pattern identification and prediction. The objective is to enhance the accuracy of forecasts regarding wastewater treatment outcomes. The technique entails constructing and refining a convolutional neural network (CNN) model, focusing on adjusting the model’s hyperparameters using Bayesian optimization to get optimal outcomes. The study evaluates the model’s effectiveness by comparing it to traditional models and other AI-based methodologies. Optimization led to a considerable enhancement in predictive performance, with the CNN-BWO model outperforming earlier models in efficiency and accuracy. This result has significant implications, indicating a novel approach to utilizing AI for managing wastewater treatment systems. This work demonstrates that enhanced CNN models can provide precise and reliable predictions, paving the way for improved and more sustainable wastewater management processes. The research highlights the significance of continuously enhancing AI and optimization algorithms to encourage their broader application in environmental engineering. Future research will explore the scalability of an AI model for enhancing wastewater management globally across various treatment facilities and conditions.