Modular Parameterization for Manufacturing Plant Optimization
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Solution Overview
Problem
Optimizing manufacturing facility operations is challenging due to the computationally intensive and complex nature of steady-state control models, which require extensive recalibration and optimization efforts, especially when process conditions change, leading to increased operating costs and troubleshooting difficulties.
Innovation Solution
The manufacturing environment is segmented into individual modules with fundamental principles-based models, allowing for independent parameterization and linearization, resulting in a reduced-order model that can be assembled to provide improved operating conditions without requiring the entire facility to be at steady state, facilitating faster and more frequent optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a steady-state fundamental-principles control model is used for the entire facility, then the model provides comprehensive control coverage, but the parameterization can only be performed when the entire facility is at steady state, reducing flexibility and increasing optimization time
Solution Approach 1:
The facility is divided into multiple modules, each with its own fundamental-principles control model. This segmentation allows each module to be parameterized independently when it reaches steady state, rather than requiring the entire facility to be at steady state. The modular approach maintains comprehensive control coverage while enabling more frequent and flexible optimization iterations.
2Reliability
If the entire facility is modeled as a single control model, then the model represents the complete system, but the large number of unknown variables and equations makes the model difficult to understand and troubleshoot
Solution Approach 1:
By segmenting the facility into modules, each module's control model contains a manageable number of variables and equations relevant to that specific section. This makes each module's model easier to understand, validate, and troubleshoot independently, while the collection of module models collectively represents the complete facility system.
Solution Approach 2:
Inter-module data flow acts as an intermediary mechanism that connects the individual module models. This allows data to be exchanged between modules in a structured way, maintaining system-wide representation while keeping each module's internal complexity manageable and tractable.
3Productivity
If the fundamental-principles control model is iterated frequently to optimize operating conditions, then the optimization responsiveness improves, but the computational intensity and time required for each iteration increases
Solution Approach 1:
Segmenting the facility into modules allows each module's control model to be iterated and parameterized independently and more frequently. This reduces the computational burden of each iteration compared to solving the entire facility model at once, enabling more responsive optimization without excessive computation time per iteration.
Solution Approach 2:
Each module's control model is pre-parameterized when the module reaches steady state, preparing the model for rapid optimization iterations. This preliminary parameterization reduces the computational work required during subsequent optimization iterations, allowing for faster and more frequent optimization cycles.
Data Source
AI summary
Methods and systems for substantially optimizing plant operations within a manufacturing environment. The method can include separating the manufacturing environment into two or more individual modules, wherein each individual module contains a fundamental principles-based model, and wherein the totality of the individual modules represents the entire manufacturing environment. Each individual module can be independently parameterized upon said module reaching steady state, wherein inter-module data flow can be provided to at least one of the individual modules during parameterization, and wherein an output of the parameterization comprises an individual, calibrated steady-state model of each individual module. A reduced order model can be derived from each parameterized module, and the reduced order models can be assembled to provide a facility reduced order model. The facility reduced order model can then be solved to provide improved or new operating conditions or operating condition targets.