Chemical Production Zone Routing for Predictive Quality Control
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Solution Overview
Problem
Industrial chemical production processes face challenges in maintaining consistent and predictable quality due to complex dependencies on production parameters, leading to costly quality control measures and inefficiencies in data integration across the value chain.
Innovation Solution
A method involving computing units that utilize real-time process data and historical data to determine zone-specific performance parameters, enabling the diversion of input materials and products to target equipment zones based on predefined quality criteria, thereby enhancing quality control and reducing tolerance ranges without requiring expensive equipment or materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality control methods are used with regular sampling and analysis, then product quality consistency can be maintained, but production time increases and production cost increases
Solution Approach 1:
The system performs preliminary quality assessment by computing predicted performance parameters from process data before actual product completion. This allows early identification of quality issues and proactive adjustments, eliminating the need for time-consuming post-production sampling and analysis while maintaining quality consistency.
Solution Approach 2:
The patent replaces traditional mechanical quality control methods (physical sampling, laboratory analysis) with a computational system that uses machine learning models and process data to predict product performance. This substitution eliminates the time delay inherent in physical testing while maintaining or improving quality assessment accuracy.
2Reliability
If traditional quality control methods are used with regular sampling and analysis, then product quality consistency can be maintained, but production cost increases
Solution Approach 1:
The system replaces expensive physical quality control infrastructure (sampling equipment, laboratory facilities, analyst time) with a computational model that leverages existing process data. This substitution significantly reduces quality control costs while maintaining product quality consistency through predictive analytics.
Solution Approach 2:
The system enables self-service quality control by automatically computing predicted performance parameters and identifying quality issues without requiring external laboratory analysis or specialized quality control personnel. The process data itself serves as the input for quality assessment, eliminating the need for separate testing resources.
3Loss of information
If machine learning via traditional time series approaches is used, then data analysis can be performed, but data integration across the value chain becomes difficult and standardization poses major problems
Solution Approach 1:
The system segments the value chain into distinct equipment zones, each with its own performance parameters and data characteristics. This segmentation allows for zone-specific machine learning models that are easier to train and maintain, while the standardized interface (upstream object identifier) enables seamless integration across zones without requiring complex end-to-end data synchronization.
Solution Approach 2:
The patent transforms diverse process data from different equipment zones into standardized predicted performance parameters that represent product quality attributes. This parameter transformation creates a common language for quality assessment across the value chain, enabling easy data integration and comparison without requiring standardization of underlying process data formats or collection methods.
Data Source
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AI summary
The present teachings relate to a method for improving a production process for manufacturing a chemical product using at least one input material at an industrial plant, the industrial plant comprising a plurality of physically separated equipment zones, the method comprising: providing, via an interface, an upstream object identifier comprising input material data; receiving, at a computing unit, real-time process data from one or more of the equipment zones; determining, via the computing unit, a subset of the real-time process data based on the upstream object identifier and a zone presence signal; computing, via the computing unit, at least one zone-specific performance parameter of the chemical product related to the upstream object identifier based on the subset of the real-time process data and historical data; determining, in response to at least one of the performance parameters, a target equipment zone where the input mate-rial and/or chemical product is to be sent. The present teachings also relate to a system, and a software program.