An intelligent predictive and optimized wastewater treatment plant.

ABSTRACT

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.