Modular Plant Program Optimization for Resource Bottlenecks
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Solution Overview
Problem
Modular industrial plants face inefficiencies in resource management due to the lack of systematic optimization of process module states and resource interactions, leading to suboptimal resource usage and increased costs.
Innovation Solution
A computer-implemented method that analyzes the states of each service in modular industrial plants, providing resource indicators to determine optimal sequences of states for process modules, optimizing resource consumption by coupling modules to share resources and adjust based on availability and costs, using energy models and historical data to predict and improve resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple configurations of modules are used to produce a particular desired product, then flexibility and adaptability are improved, but device complexity and difficulty of optimization increase
Solution Approach 1:
The patent changes the parameter of module configuration from fixed to variable, allowing the same pool of modules to be arranged in different configurations for different products. This enables flexibility while managing complexity through systematic optimization methods that evaluate multiple configurations against defined objectives.
Solution Approach 2:
The patent introduces dynamic reconfiguration capability where the modular plant can switch between different module arrangements based on production requirements. The system dynamically selects optimal configurations from available modules to produce different products, transforming a static system into a dynamic one that adapts to changing demands.
2Loss of energy
If module re-use across multiple plants is implemented, then resource efficiency is improved, but resource management complexity and coordination requirements increase
Solution Approach 1:
The patent makes process modules universal by designing them to perform multiple functions across different product plants. The same module can be allocated to different products and configurations, reducing the total number of modules needed and improving resource efficiency while requiring systematic management to coordinate their multi-purpose use.
Solution Approach 2:
The patent implements feedback mechanisms that track module usage, availability, and performance across multiple product plants. This feedback system enables centralized coordination and optimization of module allocation, managing the complexity of re-use by providing real-time information on resource status and enabling data-driven allocation decisions.
3Productivity
If systematic optimization of process module states is implemented, then resource usage efficiency is improved, but computational requirements and analysis time increase
Solution Approach 1:
The patent performs preliminary analysis by pre-defining objective functions and constraints for module optimization. By establishing the optimization framework and criteria in advance, the system reduces the computational burden during actual operation, allowing faster optimization runs while still achieving efficient resource usage through pre-planned analytical structures.
Data Source
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AI summary
A computer-implemented method (100) for optimizing the execution of a given industrial process (1a) on a modular industrial plant (1) comprising a plurality of process modules (21-25) selected from a pool (2) of available process modules, wherein each process module (21-25) is configured to provide one or more services (31-33), the method (100) comprising the steps of: • providing (110), for each state (41-43) of each service (31-33) provided by each of the available process modules (21-25), a set of resource indicators (51-53), wherein each resource indicator (51-53) is indicative of an amount of a resource that is required and/or produced by virtue of this particular service (31-33) being in this particular state (41-43); • determining (120), based at least in part on the given industrial process (1a) and on the pool (2) of available process modules, a set of candidate programs (61-69) for execution of the process (1a), wherein each program (61-69) comprises a sequence of states (41-43) of services (31-33) of process modules (21-25) such that: ∘ this sequence accomplishes the given industrial process (1a); and ∘ the resource requirements (51-53) of all services (31-33) in the sequence are fulfilled; • determining (130), based on the sequence of states (41-43) of services (31-33) and their respective resource indicators (51-53), a set of overall resource requirements (61a-69a) for executing the given industrial process (1a) according to the candidate program (61-69); • determining (140), based at least in part on said overall resource requirements (61a-69a), a figure of merit (61b-69b) for the candidate program (61-69); and • determining (150) a candidate program (61-69) with the best figure of merit (61b-69b) as the optimal program (7) for executing the given industrial process (1a).