Neural Network Cement Quality Prediction
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Solution Overview
Problem
Current methods for predicting cement quality and manufacturing conditions are time-consuming and lack accuracy, failing to consider various factors involved in the cement manufacturing process, particularly the complex interactions between clinker raw materials, burning conditions, and grinding processes.
Innovation Solution
A neural network-based method is employed, involving an input layer for observation data and an output layer for estimating cement quality or manufacturing conditions, with iterative learning processes to optimize the number of learning iterations and improve prediction accuracy, using specific combinations of data on clinker raw materials, burning conditions, and grinding conditions to predict physical properties of cement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods (compressive strength measurement after 28 days curing) are used to evaluate cement quality, then measurement accuracy is ensured, but the evaluation time is excessively long (28 days)
Solution Approach 1:
The patent applies preliminary action by measuring multiple parameters (XRD analysis, specific surface area, fineness, chemical composition) immediately after cement production, and using these early measurements to predict the 28-day compressive strength through regression analysis. This eliminates the need to wait 28 days for the actual strength test, as the prediction is made based on preliminary data collected right after production.
2Ease of manufacture
If industrial waste is increased as cement raw material or fuel to reduce costs, then manufacturing cost is reduced, but the quality stability of cement deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring multiple parameters (XRD patterns, specific surface area, fineness, chemical composition) and using regression analysis to predict quality outcomes. This feedback mechanism allows real-time detection of quality deviations caused by industrial waste variations, enabling及时调整 of manufacturing parameters to maintain quality stability while using cost-effective industrial waste materials.
3Measurement precision
If multiple factors (flow rate of preheater gas, hydraulic modulus of clinker raw material, etc.) are considered in cement manufacturing, then prediction accuracy of quality is improved, but the complexity of the prediction system increases
Solution Approach 1:
The patent merges multiple measurement techniques (XRD analysis, specific surface area measurement, fineness analysis, chemical composition testing) into a unified prediction system. By combining these different types of data and integrating them into a single regression analysis model, the system achieves high prediction accuracy without requiring excessively complex individual components, as the strength lies in the comprehensive integration of multiple simpler measurements.
Data Source
AI summary
Provided is a method capable of predicting the quality of cement in a short time period and with high accuracy. The method of predicting the quality or manufacturing conditions of cement through use of a neural network including an input layer and an output layer includes: performing learning of the neural network for a sufficiently large number of times of learning such that σL<σM is obtained, using learning data and monitor data; then repeating the learning of the neural network until σL≧σM is obtained while the number of times of learning is decreased; inputting specific observation data to the input layer of the neural network in which a judgment value for analysis degree obtained from the neural network after the learning is less than a preset value; and outputting an estimated value of specific evaluation data from the output layer of the neural network.