Reinforcement Learning Model for Cement Calciner Coal Feed Optimization
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Solution Overview
Problem
The cement calcination process is complex and energy-intensive, with high coal and electricity consumption, and existing technologies fail to effectively manage coal feed amounts to optimize costs and efficiency.
Innovation Solution
A reinforcement learning model is constructed using simulation models and a prediction model to represent the association between coal feed amounts and free calcium content, guiding calciner and kiln head coal feeding operations based on an Actor-Critic reinforcement learning architecture, optimizing coal feed to achieve target free calcium content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional control methods are used for coal feeding in calcination, then the process can be maintained with existing simplicity, but coal consumption is high and cost control is ineffective
Solution Approach 1:
The patent replaces traditional mechanical/control-based coal feeding systems with an intelligent control system that uses reinforcement learning algorithms. The system substitutes manual control mechanisms with automated AI-based decision-making, enabling optimal coal consumption through learned patterns from process data while maintaining computational complexity rather than mechanical complexity
Solution Approach 2:
The patent changes the control parameter from fixed mechanical settings to dynamic, learned parameters. The reinforcement learning model adapts coal feeding parameters based on real-time process state and historical data, transforming static control parameters into dynamic, optimized values that minimize coal consumption while maintaining product quality
2Measurement precision
If reinforcement learning model is constructed with multiple simulation models and prediction models, then coal feed management accuracy is improved, but model construction complexity increases
Solution Approach 1:
The patent segments the complex prediction task into multiple specialized models: a first simulation model for calciner temperature prediction, a second simulation model for kiln head parameters, and a prediction model for free calcium content. This segmentation allows each model to focus on specific relationships, improving overall prediction accuracy while making the complex system more manageable through modular architecture
Solution Approach 2:
The patent introduces intermediate simulation models as mediators between the control system and the final prediction. These simulation models act as virtual laboratories that simulate process responses before actual control actions are taken, enabling accurate predictions without directly modeling the entire complex cement production process
3Productivity
If coal feed amount is optimized to reduce costs, then energy efficiency is improved, but control difficulty increases
Solution Approach 1:
The reinforcement learning system performs self-service by automatically learning optimal control strategies from process data without requiring manual intervention. The model continuously improves its own performance through experience with process data, eliminating the need for operators to manually optimize coal feeding while achieving high energy efficiency
Solution Approach 2:
The patent implements feedback mechanisms where the reinforcement learning model continuously monitors process outcomes and adjusts coal feeding decisions accordingly. The system learns from the results of previous control actions and refines its strategy, making the control process more intuitive and easier to manage while optimizing energy efficiency
Data Source
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AI summary
Embodiments of the present disclosure disclose a method, apparatus and electronic device for constructing a reinforcement learning model, and a computer readable storage medium, relate to the field of big data and deep learning technology. An implementation of the method includes: establishing a first simulation model between a calciner coal feed amount and a calciner temperature; establishing a second simulation model among a kiln head coal feed amount, a kiln current, a secondary air temperature, and a smoke chamber temperature; establishing a prediction model among: an under-grate pressure; the calciner temperature output by the first simulation model; the kiln current, the secondary air temperature, and the smoke chamber temperature content output by the second simulation model; and a free calcium; and constructing a reinforcement learning model according to a preset reinforcement learning model architecture, using the first simulation model, the second simulation model, and the prediction model.