Chemical Production Data Tracking for Real-Time Quality Control
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Solution Overview
Problem
In industrial chemical production, maintaining consistent and predictable quality of chemical products is challenging due to complex dependencies on production parameters, especially in multi-stage processes, leading to costly and time-consuming quality control and difficulties in integrating vast amounts of time series data from continuous or batch processes.
Innovation Solution
A method utilizing a computing unit to monitor and control production processes by receiving real-time data, determining relevant process parameters, and providing output data that tracks the input material's progression through equipment zones, using zone presence signals and object identifiers to enhance data relevance and integration for quality control and machine learning applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quality control is performed by collecting and analyzing samples regularly, then product quality consistency is improved, but production time and costs increase
Solution Approach 1:
The patent replaces physical sampling and laboratory analysis with a virtual sensing system that uses machine learning models to predict product quality parameters in real-time based on process data, eliminating the need for time-consuming physical quality control operations
Solution Approach 2:
The system performs preliminary quality assessment by continuously monitoring and predicting quality parameters during the production process, allowing corrective actions to be taken before actual quality deviations occur, rather than waiting for post-production sampling
2Quantity of substance
If vast amounts of time series data are collected from continuous or batch processes, then data completeness is improved, but data integration difficulty increases
Solution Approach 1:
The patent extracts only the most relevant features and parameters from the vast time series data using machine learning models, separating essential quality-indicative information from redundant data, thereby simplifying integration while maintaining data completeness
Solution Approach 2:
The system creates a universal data integration platform that handles multiple data sources (process data, sensor data, historical data) through a common machine learning framework, enabling the same infrastructure to process diverse data types without increasing complexity
3Quantity of substance
If all real-time process data are provided as output, then data completeness is improved, but data relevance for quality control decreases
Solution Approach 1:
The patent applies local quality by providing different output data subsets to different users or systems based on their specific needs - quality control systems receive quality-critical parameters while other systems receive their respective relevant data, ensuring each receives high-relevance information without compromising overall data completeness
Data Source
AI summary
The present teachings relate to a method for monitoring and/or controlling a production process for manufacturing at least one industrial product at an industrial plant comprising at least one equipment by processing at least one input, the method comprising: receiving, via an input inter-face, real-time process data from the equipment; determining, via the computing unit, a subset of the real-time process data; providing as output data the subset of the real-time process data. The present teachings also relate to a system, a use, and a software product.