Parametric Multifaceted Models for Plant Optimization
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Solution Overview
Problem
Integrated optimization and control of process plants face challenges such as inconsistencies between optimization and control solutions, computational complexity, and scalability issues, particularly in complex process plants, leading to inefficiencies and infeasible solutions.
Innovation Solution
The use of parametric multifaceted models that map low-level, real-time control parameters to high-level economic parameters, enabling consistent decision-making and efficient optimization and control through parametric non-linear dynamic approximator (PUNDA) models and a hybrid modeling framework, which accommodates different levels of detail and dynamic process conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If integrated optimization and control solutions are implemented in complex process plants, then economic benefits and optimization performance are improved, but computational complexity and scalability issues worsen
Solution Approach 1:
The patent segments the complex process plant into multiple modular functional units, each with its own parametric multifaceted model. This modular architecture divides the overall optimization and control problem into smaller, computationally manageable sub-problems that can be solved independently and then integrated, reducing the computational complexity while maintaining optimization performance.
Solution Approach 2:
The patent employs parametric multifaceted models that use parameter estimation and adaptation to simplify complex plant dynamics. By changing parameters based on operating conditions and using reduced-order models, the system achieves accurate optimization results with lower computational complexity compared to full-order first-principles models.
2Manufacturing precision
If detailed dynamic models are used for control, then control precision is improved, but inconsistencies with economic optimization models worsen
Solution Approach 1:
The patent creates a universal parametric multifaceted model framework that serves both control and economic optimization functions. This multi-functional model uses the same underlying plant representation for both detailed control applications and high-level economic optimization, ensuring consistency between the two previously inconsistent modeling approaches while maintaining control precision.
Solution Approach 2:
The patent uses parameter estimation techniques to adapt the dynamic model parameters based on actual plant operation data. This allows the same model structure to accurately represent plant behavior across different operating conditions, maintaining consistency between control and optimization models while preserving control precision through accurate parameter values.
3Productivity
If comprehensive integrated solutions are deployed, then economic benefits are improved, but scalability and maintainability worsen
Solution Approach 1:
The patent implements a segmented modular architecture where the process plant is divided into independent functional units (e.g., reactors, separators, heat exchangers), each with its own parametric model. This segmentation enables scalable deployment where models can be added, removed, or modified for individual units without affecting the entire system, improving scalability and maintainability while preserving economic optimization benefits.
Solution Approach 2:
The patent employs dynamic parametric models that can adapt to changing operating conditions and plant configurations. The models can be dynamically adjusted to accommodate new process units, changing operating ranges, or plant modifications, enabling the system to scale effectively while maintaining economic optimization performance.
Data Source
AI summary
The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time. In particular, the parametric multifaceted models may convert the first set of parameters into the second set of parameters in a time horizon allowing for control of the process plant by a control system based on the second set of parameters.