Key Factor Yield Prediction for Catalytic Activity Changes
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Solution Overview
Problem
Existing process change prediction technologies face challenges in accurately predicting catalytic activity in commercial chemical processes, particularly due to the complexity and scale of data generated in industrial processes, which affects production yield and catalyst replacement costs.
Innovation Solution
A system and method that selects key factors based on process operating conditions and applies them to a prediction model, using a key factor extraction and individual tag importance backup unit, along with a yield prediction model training and performance evaluation unit, to increase prediction accuracy and optimize catalytic activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all process data variables are used for prediction, then comprehensive analysis is achieved, but prediction accuracy decreases due to noise and redundancy
Solution Approach 1:
The patent extracts key factors from a large number of process data variables using machine learning algorithms. The system identifies and isolates the most influential variables (key factors) that affect catalytic activity, separating them from redundant and noisy data. This extraction process improves prediction accuracy by focusing only on the most relevant features while reducing the complexity of the overall data set.
Solution Approach 2:
The patent applies different processing and weighting to different data variables based on their individual importance. Instead of treating all variables uniformly, the system assigns different weights and processing methods to each variable based on its relevance to catalytic activity prediction. This local quality approach allows the model to focus computational resources on the most important variables while simplifying handling of less important ones.
2Measurement precision
If traditional linear models are used for prediction, then model simplicity is maintained, but prediction accuracy is insufficient for complex industrial processes
Solution Approach 1:
The patent transforms the prediction approach by changing the fundamental parameters of the model from traditional linear relationships to machine learning-based non-linear relationships. The system uses algorithms that can capture complex interactions between multiple process variables and catalytic activity, adapting the model structure to match the complexity of industrial processes while maintaining computational efficiency through automated feature selection.
3Reliability
If catalyst replacement is performed frequently to maintain production yield, then production reliability is improved, but operational costs and downtime increase
Solution Approach 1:
The patent implements preliminary action by predicting future catalytic activity trends before the catalyst actually degrades to unacceptable levels. The machine learning model forecasts when catalyst performance will fall below thresholds, allowing operators to plan catalyst replacement during scheduled maintenance windows rather than responding to unexpected failures. This advance prediction enables optimization of replacement timing to balance reliability with minimal operational disruption.
4Productivity
If comprehensive process monitoring is implemented to detect catalytic activity changes, then production yield is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the most critical key factors from comprehensive process data that have the strongest correlation with catalytic activity and production yield. By identifying and monitoring only these essential variables through the machine learning model, the system achieves effective production optimization without the computational burden of analyzing all available process data in real-time.
Data Source
AI summary
A system for predicting process changes by using key factors in a commercial chemical process, includes: a key factor extraction and individual tag importance backup unit that extracts yield key factors by calculating the importance of each tag, and backs up importance data for each tag; and a yield prediction model training and yield prediction performing unit that performs yield prediction model training by using the importance of each tag accumulated in the key factor extraction and individual tag importance backup unit, and performs yield prediction so as to output a yield prediction result, evaluates performance, and selects an optimal prediction model.