Coal Initial Deformation Temperature Prediction Model
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Solution Overview
Problem
Coal-fired power plants face inefficiencies and increased slagging due to blending multiple types of coal, which complicates the determination of initial deformation temperature (IDT), requiring time-consuming and costly offline tests for updating coal blends.
Innovation Solution
An apparatus and method that derive an IDT predictive model using parameter extraction, analysis, and machine learning to predict the initial deformation temperature of coal without a separate test, by analyzing characteristics through ash component, elementary, and industrial analyses, and generating derivative parameters for accurate prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If multiple types of coal are blended for cost reduction, then operational cost is reduced, but determining initial deformation temperature becomes more complex and requires time-consuming offline tests
Solution Approach 1:
The patent creates a virtual copy of the offline testing process through a predictive model that replicates IDT determination. Instead of performing physical heating tests on blended coal samples, the system uses a digital model that copies the essential functionality of offline testing, providing IDT predictions without the time-consuming physical experiments.
Solution Approach 2:
The patent replaces the mechanical/physical offline testing system with a computational prediction system. The predictive model substitutes the physical heating and observation process with algorithm-based calculations using coal characteristic parameters, eliminating the need for time-consuming laboratory tests while maintaining prediction accuracy.
2Measurement precision
If offline tests are conducted to determine IDT of blended coal, then accurate IDT data is obtained, but the process is time-consuming and costly
Solution Approach 1:
The patent performs preliminary analysis by extracting key characteristic parameters from coal samples before blending. By pre-processing and storing essential coal properties (ash content, elemental composition, etc.), the system prepares data in advance that can be quickly processed by the predictive model, eliminating the need for time-consuming tests after blending occurs.
Solution Approach 2:
The patent transforms the IDT measurement problem from a physical temperature determination into a parameter-based prediction problem. By identifying and using key coal characteristic parameters (ash content, SiO2, Al2O3, CaO, MgO, Na2O, K2O, TiO2, SO3) as inputs to the predictive model, the system changes the approach from direct measurement to indirect prediction, significantly improving efficiency while maintaining accuracy.
3Measurement precision
If traditional offline testing is used to predict coal IDT, then accurate results are obtained, but the process requires separate testing for each coal blend
Solution Approach 1:
The patent creates a universal predictive model that can handle multiple coal types and blends with a single system. The model accepts various coal characteristic parameters as inputs and provides IDT predictions for any blended coal composition without requiring separate calibration or testing procedures. This multi-functional approach simplifies the overall process while maintaining accuracy across different coal scenarios.
Data Source
AI summary
An apparatus and method predict an initial deformation temperature of coal without an additional test by using a predictive model. The apparatus includes a parameter extractor configured to analyze characteristics of test coal and to extract parameters of the test coal based on the test coal characteristic analysis; a temperature analyzer configured to analyze an initial deformation temperature (IDT) of the test coal; a modeler configured to derive an IDT predictive model for predicting the test coal IDT using the extracted parameters of the test coal and the test coal IDT; and a predictor configured to predict an initial deformation temperature (IDT) of target coal to be supplied to the coal-fired power plant by substituting parameters of the target coal into the IDT predictive model. The test coal characteristics are analyzed by ash component analysis, elementary analysis, industrial analysis, or calorific value analysis.