Chemical Process Key Factor Selection for Catalyst Yield Analysis
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Solution Overview
Problem
Existing process change monitoring technologies in commercial chemical processes struggle to efficiently select key factors affecting catalytic activity, leading to inefficiencies in operation condition optimization, increased production costs, and reduced product yield due to inadequate catalyst management.
Innovation Solution
A system and method for selecting a process key factor in commercial chemical processes that includes data extraction, outlier discrimination, derived variable generation, yield calculation, and key factor extraction, utilizing machine learning-based systems to predict catalytic activity and optimize operation conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional process monitoring methods are used, then the system is simple to operate, but it cannot efficiently select key factors affecting catalytic activity from complex data
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: data extraction unit, outlier discrimination unit, derived variable generation unit, yield calculation unit, and key factor extraction unit. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving high measurement precision in key factor selection.
Solution Approach 2:
The patent introduces intermediate processing steps between raw data and final key factor identification. The outlier discrimination unit and derived variable generation unit act as intermediaries that prepare and refine the data before key factor extraction, enabling accurate identification without directly processing raw complex data.
2Productivity
If comprehensive data analysis is performed to predict catalytic activity, then product yield increases, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing by extracting relevant tags and discriminating outliers before the actual key factor extraction. This preliminary action prepares the data in advance, reducing the computational burden during catalytic activity prediction and enabling faster processing while maintaining high product yield through comprehensive analysis.
Solution Approach 2:
The patent extracts only the most relevant features and derived variables from the comprehensive data set before performing catalytic activity prediction. By taking out and focusing on key derived variables rather than processing all raw data, the system achieves high productivity with reduced processing time.
3Measurement precision
If traditional outlier handling is used, then the processing is simple, but the yield calculation accuracy decreases due to inadequate outlier management
Solution Approach 1:
The patent implements a dynamic outlier handling approach where the system continuously identifies and discards outliers during the data processing pipeline. The outlier discrimination unit dynamically adjusts to data characteristics, and the derived variable generation unit adapts to data quality, enabling accurate yield calculation while managing processing complexity through adaptive rather than static methods.
Data Source
AI summary
A system selecting a process key factor in a commercial chemical process, includes: a data extraction unit that extracts tag data in units of a set period; an outlier discrimination unit that discriminates and aggregates outliers by tag by using an outlier extraction reference master; an outlier processing unit that generates an input mart draft excluding the outliers; a derived variable generation unit that generates derived variables for each tag, and generates an advanced input mart having the derived variable added thereto; a yield calculation unit that backs up the result of calculation of a yield by realizing a target value via exclusion and correction of the outliers; and a key factor extraction unit that extracts a yield key factor by calculating importance of each tag, and backs up importance data for each tag.