Chemical Product Quality Prediction Using Sensor Time Shifts
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Solution Overview
Problem
Chemical production plants face challenges in accurately predicting product quality due to complex, non-linear relationships between process parameters and limited availability of extensive training data, leading to imprecise or specialized prediction models.
Innovation Solution
A method for training a machine learning module using sensor-specific time shifts and priori information about the production plant, integrating engineering expertise and chronological sequence data to enhance prediction accuracy with limited historical data, allowing for real-time product quality prediction in chemical production plants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning methods are used for prediction, then prediction capability is provided, but prediction accuracy is insufficient due to limited training data
Solution Approach 1:
The patent applies preliminary action by determining sensor-specific time shifts before the actual prediction process. This pre-processing step aligns process parameter values with product quality parameter values in time, creating properly synchronized training data that enables accurate predictions even with limited data availability. The time shift determination is performed once during model setup, then reused for all subsequent predictions.
Solution Approach 2:
The patent introduces sensor-specific time shifts as an intermediary element that mediates between process parameters and product quality parameters. This time shift acts as a synchronization mechanism that bridges the temporal gap between when process parameters are measured and when they actually influence product quality, enabling the machine learning model to accurately correlate cause and effect.
2Measurement precision
If extensive test runs are conducted to collect training data, then data quality improves, but production time and cost increase
Solution Approach 1:
The patent determines sensor-specific time shifts in advance during a setup phase, then uses these pre-determined shifts for all subsequent training and prediction operations. This eliminates the need for repeated time synchronization analysis and allows training data to be collected and processed more efficiently without sacrificing alignment accuracy.
Solution Approach 2:
The system uses its own operational data and a priori information about the production process to automatically determine time shifts, eliminating the need for external calibration services or manual synchronization procedures. The machine learning model itself benefits from this self-determined temporal alignment.
3Measurement precision
If sensor-specific time shifts are determined and applied, then assignment accuracy improves, but computational complexity increases
Solution Approach 1:
The patent divides the time synchronization problem into sensor-specific segments, determining a separate time shift for each individual sensor based on its specific characteristics and position in the production process. This segmentation allows each sensor's data to be accurately aligned without requiring complex global synchronization algorithms.
Solution Approach 2:
The patent transforms the raw process parameter values by applying sensor-specific time shifts, changing the temporal parameter of each data point. This parameter transformation aligns the timing of process parameters with their corresponding product quality outcomes, improving model accuracy without fundamentally changing the data structure or requiring complex computational frameworks.
Data Source
AI summary
A method for training a machine-learning module of a computer-implemented prediction model for predicting product quality parameter values for one or more quality parameters of a chemical product produced by a chemical production plant. The production plant includes a plurality of sensors, each of which is configured to acquire process parameter values for one or more process parameters of a chemical process carried out by the production plant for producing the chemical product during operation of the production plant. A priori information about the production plant and the process carried out by the production plant is used, including chronological sequence information about a chronological sequence of the process carried out within the production plant, for which sensors sensor-specific time shifts between an acquisition time of training process parameter values and a production time of a product unit, during the production of which the corresponding training process parameter value was acquired.