Modular Plant Module Selection for Compatibility and Integration Time
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Solution Overview
Problem
Current modular plants face significant challenges in efficiently integrating and managing modules, requiring extensive engineering effort and resource allocation to ensure compatibility and optimal performance.
Innovation Solution
A computer-implemented resource management method that receives data on required module types and executes an optimization algorithm to select the best modules for inclusion in a module pipeline based on predetermined optimization criteria, utilizing machine learning models and semantic data models to enhance decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modules are integrated manually by plant engineers, then compatibility and performance can be ensured, but engineering effort and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical integration processes with an automated computer-implemented optimization algorithm. The system automatically selects modules from a pool based on compatibility data and optimization criteria, eliminating the need for manual engineer intervention in the selection process while maintaining compatibility through systematic evaluation of input-output flow compatibility and process parameter calibration.
Solution Approach 2:
The system enables self-service by allowing the optimization algorithm to autonomously perform module selection and integration without requiring manual engineering effort. The algorithm independently evaluates modules against compatibility requirements and optimization criteria, automatically configuring the module pipeline to achieve both compatibility and performance optimization.
2Productivity
If extensive engineering effort is invested in module integration, then optimal performance can be achieved, but resource allocation becomes inefficient
Solution Approach 1:
The patent changes the approach from manual parameter evaluation to automated optimization by introducing an algorithm that systematically evaluates modules based on multiple parameters including capacity, energy consumption, and time-to-service. The optimization algorithm processes compatibility data and performance parameters automatically, achieving optimal plant performance without requiring extensive manual engineering resources.
Solution Approach 2:
The complex manual management process is replaced by a computer-implemented optimization system that automatically handles module selection and configuration. The system manages the complexity through automated evaluation of compatibility data and optimization criteria, reducing the burden on plant engineers while maintaining high productivity through systematic optimization.
3Adaptability or versatility
If manual integration methods are used, then flexibility in module selection exists, but selection efficiency and optimization capability are reduced
Solution Approach 1:
The optimization algorithm serves multiple functions: it evaluates compatibility, optimizes performance, and selects modules from a diverse pool. The system maintains flexibility by considering various module types and configurations while simultaneously improving selection efficiency through automated processing of compatibility data and optimization criteria across multiple dimensions.
Data Source
AI summary
A computer-implemented resource management method for modular plants may include: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline including 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.