Waste Incineration Flue Gas Control Using Multi-Pollutant Prediction
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Solution Overview
Problem
Current waste incineration plants face challenges in accurately predicting and controlling multiple pollutants (HCl, SO2, NOx, and PM) in flue gas due to measurement delays and high maintenance costs of CEMS, and existing methods only address single pollutants, leading to subjective and inaccurate control strategies.
Innovation Solution
A collaborative prediction model using an LSTM network and multi-objective optimization algorithm to simultaneously predict and control multiple pollutants, integrating DCS and CEMS data, and optimizing absorbent dosages based on cost and environmental protection indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CEMS is used to monitor pollutant concentrations, then measurement accuracy is improved, but measurement delays and high maintenance costs occur
Solution Approach 1:
The system uses historical operating parameter data and emission concentration data to train an LSTM prediction model in advance. The trained model can then predict future pollutant concentrations without requiring real-time CEMS measurements, thereby eliminating measurement delays while maintaining accuracy.
Solution Approach 2:
The invention creates a virtual copy of the CEMS monitoring function through the LSTM prediction model. Instead of relying on physical CEMS equipment for real-time measurements, the system uses the trained model to generate predicted concentration values that replicate the monitoring capability without the associated delays and maintenance requirements.
2Device complexity
If single-pollutant prediction models are used, then model complexity is reduced, but collaborative prediction capability for multiple pollutants is lost
Solution Approach 1:
The invention merges multiple single-pollutant prediction tasks into a single unified LSTM model that simultaneously predicts concentrations of multiple pollutants (HCl, SO2, NOx, PM). The model takes operating parameters as input and generates predictions for all four pollutant types together, achieving collaborative prediction while maintaining reasonable complexity through shared feature extraction.
Solution Approach 2:
The LSTM prediction model is designed with universal functionality to handle multiple pollutant prediction tasks. By using a shared model architecture that processes operating parameter data and outputs predictions for different pollutant types, the system achieves multi-functionality without requiring separate specialized models for each pollutant.
3Ease of operation
If manual control strategies based on CEMS data are used, then operational flexibility is maintained, but control accuracy and response time deteriorate
Solution Approach 1:
The system implements automated feedback control by using the LSTM prediction model to continuously predict pollutant concentrations and automatically adjusting absorbent dosages based on these predictions. This closed-loop control system replaces manual decision-making with automated feedback mechanisms, improving both accuracy and response time while maintaining operational flexibility through configurable control parameters.
Solution Approach 2:
The control system performs self-service by automatically adjusting absorbent dosages based on predicted pollutant concentrations without requiring manual intervention. The system monitors operating parameters, predicts emissions, and autonomously optimizes control actions, thereby improving control accuracy and response time while reducing human labor requirements.
4Ease of operation
If traditional control methods are used, then operational simplicity is maintained, but environmental protection indicator compliance is compromised
Solution Approach 1:
The system performs preliminary prediction of pollutant concentrations using the trained LSTM model before actual emissions occur. By predicting future concentrations based on current and historical operating parameters, the system can proactively adjust absorbent dosages to ensure compliance with emission standards, rather than reacting to measurements after pollutants are already emitted.
Solution Approach 2:
The automated control system continuously monitors operating parameters, predicts pollutant concentrations, and adjusts absorbent dosages in real-time to maintain compliance with emission standards. This feedback mechanism ensures that the system adapts to changing conditions and maintains reliability in meeting environmental regulations, while the automation preserves operational simplicity.
Data Source
AI summary
A method and a system for collaborative prediction and intelligent control of multiple pollutants in waste incineration flue gas are provided. The method includes: constructing a multi-pollutant collaborative prediction model for four types of flue gas pollutants in waste incineration flue gas based on a deep learning algorithm; constructing a cost index function considering an absorbent dosage and an environmental protection index function considering pollutant emission amounts by integrating multi-objective optimization methods; determining optimal dosage data of absorbents corresponding to the four types of flue gas pollutants; controlling and adjusting an opening degree of the dispensing valve of the each absorbent based on the optimal dosage data, thereby achieving intelligent control of multiple pollutants in waste incineration flue gas.