Virtual Batch Analytics for Continuous Process Quality Prediction
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Solution Overview
Problem
Current batch analytics tools, such as PCA and PLS, are unsuitable for continuous process control systems as they cannot account for dynamic behavior and time-dependent changes, making it difficult to provide predictive analytics for continuous processes.
Innovation Solution
Implementing a virtual batch unit and sampling batch analyzer to divide continuous processes into discrete segments, allowing the application of batch-like analytic techniques, such as those defined by the ISA-88 standard, to generate predictive analytic information by mimicking a batch process within the continuous process control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If batch analytics tools (PCA and PLS) are used for continuous process control, then manufacturing precision can be improved, but the system cannot account for dynamic behavior and time-dependent changes
Solution Approach 1:
The continuous process is segmented into virtual batches of fixed duration, allowing batch analytics tools to be applied to each segment. This segmentation enables the use of PCA and PLS for quality prediction while capturing dynamic behavior through the sequential analysis of multiple segments, thereby resolving the contradiction between manufacturing precision and adaptability to dynamic changes.
2Productivity
If continuous process control is implemented, then productivity increases, but it becomes difficult to provide predictive analytics compared to batch processes
Solution Approach 1:
A virtual batch unit controller is introduced as an intermediary layer between the continuous process control system and the batch analytics tools. This intermediary segments the continuous process into virtual batches, enabling predictive analytics to be applied to continuous processes without disrupting the high productivity of continuous operation, thus resolving the contradiction between productivity and predictive analytics capability.
3Difficulty of detecting and measuring
If batch processing is used, then predictive analytics can be easily implemented, but productivity is reduced due to discrete processing periods
Solution Approach 1:
The system dynamically creates virtual batches from continuous process data, allowing the batch duration and segmentation to be adjusted based on process characteristics and analytical requirements. This dynamic approach maintains the ease of implementing predictive analytics while maximizing productivity by optimizing the virtual batch configuration for continuous operation, resolving the contradiction between analytics implementation ease and productivity.
Data Source
AI summary
Methods and apparatus to implement predictive analytics for continuous processes are disclosed. An example apparatus includes a virtual batch unit controller to implement a sampling batch on a virtual batch unit. The sampling batch corresponds to a discrete period of time of a continuous control system process. The virtual batch unit includes input and output parameters corresponding to parameters associated with the continuous control system process. The example apparatus further includes a sampling batch analyzer to generate predictive analytic information indicative of a predicted quality of an output of the continuous control system process at an end of the discrete period of time based on an analysis of the sampling batch relative to an analytical model.