Fuzzy Neural Cooperative Control of DO and Nitrate in Wastewater
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Solution Overview
Problem
Wastewater treatment plants face challenges in stabilizing dissolved oxygen (DO) and nitrate nitrogen (NO3—N) concentrations due to conflicting reaction conditions, which affect the efficiency of biochemical reactions and energy consumption, necessitating a method for cooperative control of these key variables.
Innovation Solution
A type-2 fuzzy neural network-based cooperative control method (T2FNN-CC) is employed, utilizing a five-layer type-2 fuzzy neural network with aeration and internal backflow values to control DO and NO3—N concentrations, incorporating a parameter cooperative strategy to optimize global and local parameters for precise and stable control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional control methods are used for WWTP, then system simplicity is maintained, but control precision and stability under variable conditions deteriorate
Solution Approach 1:
The control system continuously monitors DO concentration, ammonia nitrogen, and nitrate nitrogen levels using sensors, and automatically adjusts aeration valve positions and air flow rates based on feedback signals. This closed-loop control maintains precise control of water quality parameters despite variable influent conditions.
Solution Approach 2:
The patent replaces conventional mechanical/proportional control mechanisms with an intelligent neural network-based controller that processes sensor data and generates optimal control signals, achieving higher precision without proportionally increasing mechanical complexity.
2Productivity
If aeration intensity is increased to improve treatment efficiency, then biochemical reaction rate is improved, but energy consumption increases
Solution Approach 1:
The system changes operational parameters dynamically by adjusting aeration intensity, air flow rates, and oxygen transfer efficiency based on real-time water quality conditions. The neural network optimizer finds the minimum energy input required to achieve desired treatment outcomes, avoiding excessive aeration.
Solution Approach 2:
The control system uses online sensors to self-monitor water quality parameters and automatically adjusts aeration levels to maintain optimal treatment conditions. The system serves itself by making real-time decisions about energy allocation based on actual process needs rather than fixed schedules.
Data Source
AI summary
A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.