Multi-objective Optimized Fuzzy Neural Network for Total Nitrogen Detection
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Solution Overview
Problem
Current wastewater treatment methods face challenges in accurately and rapidly detecting total nitrogen concentrations, which are crucial for preventing water pollution and regeneration, due to the high requirements and long detection times of traditional chemical methods.
Innovation Solution
A total nitrogen intelligent detection method based on a multi-objective optimized fuzzy neural network using a multi-level learning approach with a particle swarm optimization algorithm, which automatically collects data and optimizes parameters and structure for improved generalization and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional chemical experiments are used to detect total nitrogen concentration, then detection accuracy is guaranteed, but detection time becomes long and operational requirements are high
Solution Approach 1:
The patent creates a virtual copy of the chemical detection process through a fuzzy neural network model. The model learns from historical chemical experiment data and replicates the detection function, enabling rapid prediction without actual chemical reactions. This copying approach maintains accuracy while eliminating time-consuming laboratory procedures.
Solution Approach 2:
The patent replaces the mechanical-chemical detection system with an intelligent computational system. Instead of using physical chemical reagents and equipment, the system uses a fuzzy neural network with multi-objective particle swarm optimization to predict total nitrogen concentration from water quality parameters, substituting physical chemistry with computational intelligence.
2Measurement precision
If traditional chemical experiments are used to detect total nitrogen concentration, then detection accuracy is guaranteed, but operational requirements and complexity increase
Solution Approach 1:
The patent replaces complex chemical detection equipment and procedures with a computational intelligence system. The fuzzy neural network model, optimized through multi-objective particle swarm optimization, processes electronic sensor data to predict total nitrogen concentration, eliminating the need for chemical reagents, specialized equipment, and trained laboratory personnel.
Solution Approach 2:
The system achieves automated self-service detection by integrating data collection from existing water quality sensors with the fuzzy neural network model. The model automatically processes sensor inputs and generates predictions without human intervention, making the system independent of operational expertise required for traditional chemical methods.
3Productivity
If fuzzy neural network is used for total nitrogen detection, then real-time detection is achieved, but generalization ability and prediction accuracy need improvement
Solution Approach 1:
The patent applies dynamic optimization to the fuzzy neural network by integrating multi-objective particle swarm optimization. This dynamic approach allows the model parameters and structure to adapt and evolve during the learning process, optimizing for both speed and accuracy simultaneously. The optimization algorithm dynamically adjusts weights, thresholds, and membership function parameters to achieve best performance.
Solution Approach 2:
The patent systematically changes and optimizes multiple parameters of the fuzzy neural network including membership function types, number of hidden layers, learning rates, and optimization weights. By adjusting these parameters through multi-objective particle swarm optimization, the model achieves both real-time processing capability and high prediction accuracy for total nitrogen concentration.
Data Source
AI summary
A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration. Meanwhile, in this system, the detection method can improve the prediction accuracy, as well as ensure the total nitrogen concentration be obtained in real-time and low-cost.