Flue Dust Prediction with Adam-Optimized BPNN for CEMS Verification
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Solution Overview
Problem
Existing emission monitoring systems in coal-fired power plants face challenges in accurately measuring flue dust concentrations due to rapid changes and equipment wear, leading to measurement anomalies and complex manual verification processes.
Innovation Solution
A method, device, and medium for predicting flue dust concentration using a neural network that quickly updates weights and threshold parameters, generating a general rule between material data and flue dust emission, and simplifying monitoring and verification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CEMS sensors are used to continuously monitor flue dust concentration, then measurement capability is provided, but measurement precision deteriorates due to sensor wear and aging
Solution Approach 1:
The patent introduces a neural network prediction model as an intermediary system that processes coal quality data and operational parameters to predict flue dust concentration. This prediction model serves as a mediator between the physical coal combustion process and the CEMS measurement system, providing reference values that compensate for sensor degradation and wear, thereby maintaining measurement precision despite sensor reliability deterioration
Solution Approach 2:
The system implements feedback by continuously comparing CEMS measured values with neural network predicted values, using the difference to adjust and verify monitoring data. This feedback mechanism allows the system to identify measurement anomalies caused by sensor wear and correct them, maintaining accurate flue dust concentration measurements over time
2Measurement precision
If manual accounting and verification processes are used for emission monitoring, then verification accuracy can be maintained, but productivity deteriorates due to high labor burden
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform accounting and verification functions through the neural network prediction model. The model autonomously processes coal quality data, calculates expected emissions, and generates verification results without manual intervention, thereby maintaining verification accuracy while dramatically improving productivity by eliminating labor-intensive manual processes
Solution Approach 2:
The system replaces manual mechanical verification processes with an automated neural network-based computational system. The neural network model substitutes human analysts and manual calculation methods, using machine learning algorithms to perform emission accounting and verification tasks automatically, thus maintaining accuracy while enhancing efficiency
3Measurement precision
If traditional neural network training methods are used, then model accuracy can be achieved, but productivity deteriorates due to slow weight and threshold updates
Solution Approach 1:
The patent applies dynamics by implementing dynamic weight and threshold updates in the neural network model. Instead of static training, the system continuously adapts model parameters as new data becomes available, allowing the model to maintain high accuracy while responding rapidly to changing combustion conditions. This dynamic approach enables faster effective training compared to traditional batch methods
Solution Approach 2:
The system performs preliminary action by pre-training the neural network model with historical data to establish initial weight and threshold values. This preliminary training creates a ready-to-use model that can quickly adapt to new conditions without requiring extensive retraining, thereby achieving both high accuracy and fast response when updating predictions for new operational scenarios
Data Source
AI summary
The invention discloses a method, a device and a medium for predicting a flue dust concentration, which calculate a flue dust emission amount of each batch of coal fed into a furnace based on hourly coal consumption of a unit as a label value of a prediction model, generate a general rule between data of the coal fed into the furnace and a corresponding flue dust emission amount through training the prediction model, accurately identify a relationship between material and the flue dust emission amount, reduce workloads of manual accounting and verification, and provide a reference for CEMS flue dust monitoring data. At the same time, using an Adam algorithm to optimize a BPNN allows for automatic adjustment of a learning rate for each parameter, enabling fast and efficient training of the prediction model. The invention can solve problems of measurement errors and complex manual accounting and verification in the related art, thereby achieving precise measurement of the flue dust emissions from coal-fired power plants and reducing workloads of manual operations.