Process Automation Facility Layout Using Hierarchical Design Embeddings
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Solution Overview
Problem
Designing process automation facilities from scratch is costly and time-consuming due to the lack of transferable knowledge from existing facilities, requiring significant expertise and experience.
Innovation Solution
Implementations leverage federated information models generated from reference process automation facilities to automate aspects of designing new facilities by using machine learning techniques to compare and map embeddings of design inputs to template design documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If knowledge from reference process automation facilities is not transferred, then design expertise must be accumulated manually through experience, but this increases design time and cost for new facilities
Solution Approach 1:
The patent creates federated information models that are copies or representations of reference process automation facilities. These models capture design knowledge, configurations, and parameters from existing facilities and make them reusable for new designs, eliminating the need to manually accumulate experience and reducing design time while preserving design knowledge.
Solution Approach 2:
The system performs preliminary actions by pre-processing reference facilities into structured federated information models before they are needed for new designs. This advance preparation of design knowledge in reusable formats allows rapid retrieval and application during new facility design, reducing both time loss and knowledge transfer barriers.
2Reliability
If design processes are manual and experience-based, then expertise can be applied to complex designs, but the process becomes costly and time-consuming
Solution Approach 1:
The patent replaces manual, experience-based design processes with an automated machine learning system. The system uses trained models to process design inputs, retrieve relevant knowledge from federated information models, and generate design outputs automatically. This substitution maintains design quality through consistent application of learned patterns while dramatically improving productivity by eliminating manual repetition.
Solution Approach 2:
The system enables self-service design capabilities where the automated machine learning model independently processes design requests, retrieves appropriate knowledge from stored federated information models, and generates design documents without requiring constant human intervention. This maintains reliability through consistent automated processes while boosting productivity through parallel processing and elimination of manual bottlenecks.
3Adaptability or versatility
If design knowledge is captured in reference facilities, then it can be reused, but the complexity of processing and mapping design hierarchies increases
Solution Approach 1:
The patent segments the design knowledge into federated information models organized by hierarchical levels corresponding to different design document types. This segmentation allows the system to process and retrieve only the specific level of knowledge needed for each design task, improving adaptability and knowledge reusability while managing complexity through modular, level-based organization rather than requiring processing of entire facility designs.
Data Source
AI summary
Implementations herein leverage knowledge about historical process automation facilities to automate designing a new process automation facility. A first level design input may be processed to generate a first embedding that encodes design aspect(s) of the requested process automation facility with a degree of detail commensurate with a first level of a hierarchy reflected by design documents typically used to design a process automation facility. The first embedding may be used to find first level reference embeddings that encode design aspects of reference process automation facilities. Second level reference embedding(s) may be identified based on mapping(s) from the selected first level reference embedding(s). Each second level reference embedding may encode design aspect(s) of a respective reference process automation facility with a degree of detail that is commensurate with a second level of the design document hierarchy. Based on the second level reference embedding(s), template design document(s) may be provided.