Modular Plant Pipeline Assembly Using Semantic Module Selection
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Solution Overview
Problem
Current modular plants require significant engineering effort for module integration, involving compatibility, calibration, and resource management, which is time-consuming and prone to errors.
Innovation Solution
A computer-implemented resource management system that uses a database of semantic modules with optimization algorithms to select and assemble modules based on predetermined criteria, including capacity, energy consumption, and time-to-service, utilizing machine learning and simulation to optimize module pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If modules are integrated manually by plant engineers, then flexibility in module selection is maintained, but engineering effort and time consumption increase significantly
Solution Approach 1:
The system enables self-service by automatically selecting and integrating modules based on process requirements and module attributes. The automated module selection system performs compatibility checks, calibration parameter matching, and pipeline assembly without requiring manual plant engineer intervention for each integration task.
Solution Approach 2:
The patent replaces manual mechanical integration processes with automated computational systems. The automated module selection system uses algorithms to evaluate module compatibility, match calibration parameters, and assemble pipelines, substituting the manual mechanical integration process with an automated information-processing system.
2Reliability
If comprehensive module compatibility checks are performed, then integration reliability is improved, but engineering complexity increases
Solution Approach 1:
The system implements a universal compatibility checking mechanism that handles multiple integration aspects (input/output flow compatibility, calibration parameter matching, alarm management) through a single automated framework. This multi-functional approach consolidates what would otherwise require separate manual checks into one integrated system.
Solution Approach 2:
The automated module selection system manages complexity by systematically evaluating and matching calibration parameters, process parameters, and operational attributes. The system transforms the complex compatibility assessment into a parameter-based evaluation process, comparing module attributes against process requirements to determine integration suitability.
3Productivity
If optimization algorithms are used to select modules, then resource management efficiency is improved, but computational resources and time are consumed
Solution Approach 1:
The system performs preliminary action by pre-evaluating module attributes, calibration parameters, and compatibility characteristics before actual integration is needed. The automated module selection system maintains a database of module specifications and pre-computes compatibility information, so that when integration is required, the optimization process can proceed more quickly using pre-prepared data.
Data Source
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AI summary
There is a need for more effective resource planning for modular plants. There is therefore provided a computer-implemented resource management method for modular plants, the method comprising: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline comprising one or more modules; and executing an optimization algorithm to select, from a plurality of modules having the required module type, a module for inclusion in the module pipeline on the basis of one or more predetermined optimization criteria.