Batch Plant Monitoring Using Similar Process Parameter Matching
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Solution Overview
Problem
Monitoring and automated failure detection in batch plants are challenging due to the variability of products produced, making it difficult to optimize production processes and recognize abnormalities.
Innovation Solution
A computer-implemented method that compares sets of process parameters from multiple executed processes with a process of interest to determine similarity degrees, allowing for the identification of similar processes and detection of divergences in process parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If process monitoring is performed in batch plants with multiple products, then production optimization and abnormality detection are improved, but the complexity of monitoring increases due to product variability
Solution Approach 1:
The monitoring system uses a universal approach by comparing process parameters across different products and batches. Instead of creating product-specific monitoring models, the system treats all batches uniformly by selecting reference batches and comparing current batch parameters against them, enabling the same monitoring mechanism to handle product variability without increasing complexity
Solution Approach 2:
The system transforms the monitoring approach by changing from fixed threshold monitoring to dynamic parameter comparison. Process parameters are monitored by comparing their values and trends against corresponding parameters from reference batches, allowing the system to adapt to different products through parameter selection rather than through complex product-specific configurations
2Measurement precision
If process parameters are compared across multiple batches to detect abnormalities, then detection accuracy is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-selecting and storing reference batches with normal process parameters. When monitoring a current batch, the system quickly compares parameters against these pre-established references without needing to analyze all historical batches, significantly reducing analysis time while maintaining detection accuracy
Solution Approach 2:
The system extracts only the necessary process parameters from multiple batches for comparison, rather than analyzing complete batch data. By identifying and comparing only the critical parameters that indicate abnormalities, the system achieves accurate detection with reduced computational time and data processing requirements
Data Source
AI summary
Disclosed herein is a method for monitoring a plant capable of:receiving one or more educts; andexecuting multiple processes in which the educts are processed, where each of the processes is characterized by an associated set of process parameters;the method including:comparing a set of process parameters of interest and the remaining sets of process parameters associated with the remaining multiple executed processes to determine a similarity degree between the set of process parameters of interest and the remaining sets of process parameters;determining at least one similar process from the remaining multiple executed processes, the similar process having a similarity degree that is equal to or greater than a similarity threshold; andoutputting the set of process parameters of interest together with the set of process parameters associated with the determined at least one similar process.