Hypergraph Data Store for Process Variation Stabilization
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Solution Overview
Problem
Current systems fail to adequately stabilize multi-stage processes due to dominant sources of variation, leading to inefficiencies and increased costs, particularly in industries like biologics and pharmaceuticals, as they lack effective data aggregation and analytics capabilities to identify and address these variations in real-time.
Innovation Solution
The implementation of a hypergraph data store and run data store system that maintains process versions with parameterized resource inputs and outputs, allowing for data acquisition, processing, and analysis to identify and correct variations, thereby stabilizing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional systems store files and data without structure, then data storage is simple, but real-time analytics and feedback capabilities are insufficient
Solution Approach 1:
The system segments data into structured schemas with defined fields, types, and relationships. Each data element is organized according to a predefined structure that enables automated analysis, separating the complexity of data organization from the simplicity of data storage and retrieval operations.
Solution Approach 2:
The system implements automated feedback mechanisms that continuously monitor process data, compare it against predefined criteria, and trigger alerts or corrections. This enables real-time analytics by automatically processing structured data flows and providing immediate feedback on process stability and variation.
2Adaptability or versatility
If processes combine many different functional components with different data forms, then process versatility is improved, but data aggregation and contextualizing becomes difficult
Solution Approach 1:
The system employs a universal data schema framework that can accommodate multiple data forms and types from diverse process components. The schema defines standardized fields and relationships that work across different functional components, enabling a single system to aggregate and contextualize data from chemistry, biology, fermentation, and equipment systems without requiring component-specific handling.
3Productivity
If dominant sources of variation are not addressed, then process operation is simple, but efficiency and viability are adversely affected
Solution Approach 1:
The system continuously monitors process data and provides feedback on variation sources by comparing actual measurements against predefined specifications and historical data. This enables identification of dominant variation sources through statistical analysis of the feedback data, allowing targeted correction without requiring complex manual investigation of all process parameters.
Data Source
AI summary
Systems and methods for process design and analysis of processes that result in products or analytical information are provided. A hypergraph data store is maintained and comprises versions of each process. A version comprises a hypergraph with nodes, for stages of the process, and edges. Stages have parameterized resource inputs associated with stage input properties, and input specification limits. Stages have resource outputs with output properties and output specification limits. Edges link the outputs of nodes to the inputs of other nodes. A run data store is maintained with a plurality of process runs, each run identifying a process version, values for the inputs of nodes in the corresponding hypergraph, their input properties, resource outputs of the nodes, and obtained values of output properties of the resource outputs.