Effluent Total Nitrogen Prediction via Fuzzy Transfer Learning
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Solution Overview
Problem
Current methods for predicting effluent total nitrogen (TN) concentration in wastewater treatment plants are inefficient due to high costs and limited accuracy, especially under conditions of data insufficiency and time-varying dynamics, which hinders real-time monitoring and control.
Innovation Solution
An intelligent detection system utilizing a fuzzy transfer learning algorithm and fuzzy neural network to establish a prediction model, incorporating historical data and adjusting parameters through a particle filter algorithm to enhance prediction accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chemical experiments are used to predict TN concentration, then prediction accuracy is improved, but prediction time increases and real-time prediction cannot be achieved
Solution Approach 1:
The patent replaces the mechanical/chemical experimental system with an intelligent algorithmic system. Specifically, it uses fuzzy neural network combined with transfer learning and particle filter algorithms to substitute the traditional chemical experiment method, achieving both high accuracy and real-time prediction capabilities without requiring actual chemical reactions or laboratory equipment
Solution Approach 2:
The patent creates a virtual model (fuzzy neural network) that copies and simulates the complex chemical processes occurring in the wastewater treatment system. By building this computational copy of the treatment process, the system can predict TN concentration without performing actual chemical experiments, thus eliminating the time delay while maintaining prediction accuracy
2Productivity
If on-line instrument prediction is used, then automatic prediction and real-time monitoring are achieved, but instrument cost and maintenance cost increase
Solution Approach 1:
The patent employs inexpensive computational resources (software algorithms running on standard computers or processors) instead of expensive physical instruments. The fuzzy neural network and transfer learning algorithms can be implemented using off-the-shelf computing hardware, eliminating the need for costly specialized on-line instrumentation while maintaining automatic prediction capabilities
Solution Approach 2:
The patent substitutes physical measurement instruments with an information-based computational system. By using data processing and intelligent algorithms to replace physical sensors and analytical instruments, the system achieves automatic prediction functionality at a fraction of the cost of traditional on-line instrumentation
3Adaptability or versatility
If traditional fuzzy neural network is used for prediction, then learning ability is improved, but prediction accuracy deteriorates under data insufficiency conditions
Solution Approach 1:
The patent applies transfer learning by pre-training the fuzzy neural network on abundant historical data from a source domain (reference model) before deploying it to the target domain with limited data. This preliminary action of learning from extensive historical datasets enables the model to acquire robust features and patterns that can be transferred to situations with data insufficiency, maintaining prediction accuracy despite limited available data
Solution Approach 2:
The patent dynamically adjusts the parameters and structure of the fuzzy neural network using particle filter algorithms based on the available data conditions. By changing parameters such as membership function shapes, rule weights, and network architecture adaptively, the system optimizes its performance for data-scarce environments while retaining its learning capabilities
Data Source
AI summary
An intelligent detection system of effluent total nitrogen (TN) based on fuzzy transfer learning algorithm belongs to the field of intelligent detection technology. To detect the TN concentration, the artificial neural network can be used to model wastewater treatment process due to the nonlinear approximation ability and learning ability. However, wastewater treatment process has the characteristic of time-varying dynamics and external disturbance, artificial neural network prediction method cannot acquire sufficient data to ensure the accuracy of TN prediction, and data loss and data deficiency will make the prediction model invalid. The invention proposed an intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm; the proposed system contains several functional modules, including detection instrument, data acquisition, data storage and TN prediction. For the TN prediction module, the fuzzy transfer learning algorithm build the fuzzy neural network based intelligent prediction model, which the parameters are adjusted by the transfer learning method.