Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm

Abstract

Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat nitrogen-containing wastewater. However, the treatment process is not fully understood in terms of reaction mechanisms, process simulation, and control. In this paper, a multi-objective control strategy mixed soft-sensing model (MCSSM) is developed to systematically design the operating variations for multi-objective control by integrating the developed model, a least square support vector machine optimized with principal component analysis (PCA-LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II). The results revealed that the PCA-LSSVM model is a feasible and efficient tool for predicting the effluent ammonia nitrogen concentration ( \( {C}_{NH_4^{+}-N, eff} \) ) and the total nitrogen removal concentration (C_{TN, rem} ) with determination coefficients (R²) were 0.997 for \( {C}_{NH_4^{+}-N, eff} \) and 0.989 for C_{TN, rem} , and gives us the reasonable solutions in influent by using NSGA-II. To achieve a better removal effect, the influent pH should be kept between 7.50 and 7.52, the COD/TN ratio is suggested to maintain at 0.15 and the NH₄⁺-N/NO₂⁻-N ratio is suggested to maintain at 0.61. The developed MCSSM approach and its general modeling framework have a high potential of applicability and guidance to bioprocess in wastewater treatment, and numerical models can be structured for predicting and optimization and experiments can be conducted for data acquisition and model establishment.