A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage

Moving average convergence and divergence indexes based online intelligent expert diagnosis system for anaerobic wastewater treatment process

Anaerobic wastewater treatment process is efficient but unstable due to various disturbances, such as refractory organics and influent organic overloading. Therefore, sensitive and accurate status diagnosis is important for reasonable control to improve the stability of anaerobic process. In this study, an online intelligent expert diagnosis system for anaerobic process was established based on moving average convergence and divergence (MACD) indexes of gas- and liquid-phase parameters, combined with online monitoring system and expert diagnosis database. The effect of this diagnosis system was verified through refractory organics and organic overloading shock experiments. Results showed that this diagnosis system could make rapid, accurate and comprehensive diagnosis, predictions and early-warning. MACD algorithm could enhance pattern recognition capacity of status parameters, overcome the lagging of anaerobic process and filter irregular noisy fluctuations of status parameters. MACD index of H₂ partial pressure is suitable as sensitive early-warning indicator in the initial shock stage.

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

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 ([Formula: see text]) and the total nitrogen removal concentration (C_{TN, rem}) with determination coefficients (R²) were 0.997 for [Formula: see text] 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.