Deep Echo State Network for Stable Effluent NH4-N Prediction
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Solution Overview
Problem
Conventional methods for predicting effluent ammonia nitrogen (NH4—N) in wastewater treatment face challenges of low accuracy, weak stability, and high maintenance costs due to the complex and nonlinear nature of the wastewater treatment process.
Innovation Solution
A deep echo state network is established with a principal component analysis (PCA) mapping layer and optimized using singular value decomposition and a competitive swarm optimizer (CSO) algorithm to improve prediction accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional laboratory detection methods are used to measure NH4—N concentration, then measurement capability is provided, but detection time is long and environmental factors affect results
Solution Approach 1:
The patent replaces conventional mechanical/chemical laboratory detection methods with an optical detection system. A sensor detects light absorption characteristics of the sample, and a processing unit calculates NH4—N concentration based on the absorption spectrum. This substitution of mechanical detection with optical detection achieves rapid, real-time measurement without being affected by environmental factors.
2Measurement precision
If data-driven methods are used for predicting effluent NH4—N, then prediction capability is provided, but prediction accuracy is low due to system complexity
Solution Approach 1:
The patent employs a composite prediction system that integrates multiple components: historical data, real-time sensor data, and a trained prediction model. The system combines spectral absorption data from sensors with operational parameters, processing them through a machine learning model to achieve accurate NH4—N concentration prediction despite the complexity of wastewater treatment processes.
3Reliability
If conventional prediction systems are implemented, then prediction function is provided, but stability is weak and maintenance costs are high
Solution Approach 1:
The prediction system performs self-calibration and automatic updates. The machine learning model continuously learns from new data, automatically adapting to changing conditions without requiring manual intervention. The system self-corrects drift in sensor readings and maintains optimal performance, reducing maintenance requirements and costs while improving long-term stability.
Data Source
AI summary
The present disclosure provides a method and system for predicting effluent ammonia nitrogen (NH4—N) and an electronic device. The method includes: obtaining data to be tested; and inputting the data to be tested into a trained deep echo state network, to obtain predicted NH4—N concentration. A method for establishing the deep echo state network includes: establishing an original network, where the original network includes a plurality of input variables and reservoirs, and a principal component analysis (PCA) mapping layer is added between adjacent ones of the reservoirs; initializing the original network to obtain an initialized network; performing parameter optimization on the initialized network by a matrix generation method of singular value decomposition and a competitive swarm optimizer (CSO) algorithm, to obtain an optimized network; and training and testing the optimized network, to obtain the trained deep echo state network.