Multi-Objective Optimization of Wastewater Treatment Strategies under Hydrological Uncertainty

Abstract

Efficient wastewater treatment under variable hydrological conditions is critical for environmental sustainability and operational cost management. Increasing inflow variability, pollutant spikes, and high-dimensional operational data present significant challenges for conventional optimization methods. To develop an integrated framework capable of optimizing multiple conflicting objectives in wastewater treatment while effectively handling uncertainty induced by variable inflow and pollutant loads. This research introduces a Convolutional Neural Network (CNN)-assisted Multi-objective Decomposition Differential Evolution–driven Ant Colony Optimization (MO-DDE-ACO+CNN) algorithm, which integrates decomposition-based differential evolution with ant colony optimization to efficiently identify Pareto-optimal solutions under hydrological uncertainty. The treatment of wastewater data in different hydrological environment conditions are used in this research and are preprocessed with the missing value imputation, outlier detection based on Z-score and normalization techniques. A CNN is used to acquire nonlinear and spatial patterns on the high-dimensional data, which offer structured features to optimize the information. The ant colony optimization algorithm decomposed differential evolution algorithm is utilized to simultaneously optimize cost, energy consumption and effluent quality and ensures that it is diverse as well. MO-DDE decomposes complex multi-objective wastewater optimization problems into simpler sub problems to efficiently identify well-distributed Pareto-optimal solutions. The proposed achieves higher = 0.99 and lower MAE = 0.002, RMSE = 0.208, MAPE = 1.08%, indicating superior performance. MO-DDE-ACO+CNN has better convergence, uniformly distributed Pareto fronts and is also robust to large variations in inflow and pollutants than the base optimization methods.