Batch Process Trajectory Segmentation for Alignment
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Solution Overview
Problem
Batch processes are challenging to model and diagnose due to their non-steady state nature, requiring alignment of trajectories and often necessitating significant manual data pre-processing, which existing methods fail to address effectively.
Innovation Solution
A method that identifies and aligns segments of batch process trajectories from historical data to create an ad hoc statistical model, allowing for real-time prediction and fault detection using techniques like Principal Components Analysis (PCA) and regression, enabling operators to optimize batch processes and prevent failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional batch process modeling methods are used to align trajectories, then trajectory alignment can be achieved, but significant manual data pre-processing is required
Solution Approach 1:
The patent divides the batch process trajectory into multiple segments based on process states or grades. Each segment is modeled independently using local multivariate statistics, avoiding the need to process the entire trajectory as one object. This segmentation eliminates complex manual pre-processing while maintaining alignment accuracy through state-based boundaries.
Solution Approach 2:
Instead of requiring complete manual alignment of entire trajectories, the patent applies partial action by modeling only relevant segments based on process states. The method automatically identifies and models segments where anomalies may occur, reducing manual intervention while achieving sufficient alignment precision for diagnostic purposes.
2Reliability
If whole synchronized trajectory is processed as one object, then comprehensive modeling is achieved, but significant manual data pre-processing is required
Solution Approach 1:
The trajectory is segmented into multiple state-based segments rather than processing as one object. Each segment captures specific process states (e.g., heating, reaction, cooling phases), allowing comprehensive modeling of different operational conditions while automatically handling transitions between states without manual pre-processing.
Solution Approach 2:
The patent uses dynamic segmentation where segment boundaries are determined by process state transitions rather than fixed time intervals. This dynamic approach adapts to varying batch durations and conditions, maintaining comprehensive coverage of all process states while eliminating the need for manual synchronization and alignment pre-processing.
3Measurement precision
If trajectories are aligned using a unifying factor, then trajectory alignment is achieved, but constraints are put on unifying factor values
Solution Approach 1:
Instead of using a single unifying factor for entire trajectories, the patent applies segmentation with state-based boundaries. Each segment uses local multivariate statistics adapted to its specific process state, allowing different alignment approaches for different phases (e.g., heating vs. reaction vs. cooling) without constraining a global unifying factor.
Solution Approach 2:
The patent implements local quality by applying different statistical models and alignment approaches to different segments based on their specific characteristics. Each segment's model is tailored to its process state, allowing flexible adaptation to local conditions while maintaining overall trajectory coherence through state-based segmentation.
Data Source
AI summary
A system and method include determining a state of a batch process. Historical segments are retrieved from a historical database of trajectories of the batch process as a function of the state of the batch process. A model is created as a function of the retrieved historical segments. The model is used to provide state information about the batch process and may then be discarded.