Modular Analytics Engine for Real-Time Industrial Process Control
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Solution Overview
Problem
Industrial monitoring and control systems face challenges in achieving real-time optimization due to the need for complex and specific models, which often result in simplified models that compromise performance and require significant manual adaptation and refinement, limiting their flexibility and adaptability across different applications and environments.
Innovation Solution
A modular analytics engine system that uses sensors and actuators to collect and process data, allowing for the instantiation of agnostic, data-driven modules for modeling, optimization, classification, and control operations, which can adapt to various applications and processes without requiring specific knowledge of the system or physics, and can operate in parallel or series to address combined modeling and optimization problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive control (MPC) system uses complex physics-based models for process optimization, then controller performance and model quality are improved, but computational complexity increases and real-time optimization efficiency deteriorates
Solution Approach 1:
The patent segments the modeling and control architecture into multiple hierarchical levels (e.g., detailed physics-based models at lower levels, simplified aggregate models at higher levels). This allows complex physics-based models to be used where needed while using simplified models for real-time optimization, resolving the contradiction between model quality and computational efficiency.
Solution Approach 2:
The patent introduces intermediary components such as model reduction techniques, surrogate models, or pre-computed lookup tables that mediate between complex physics-based models and the real-time optimization algorithm. These intermediaries preserve essential system dynamics while reducing computational burden, enabling both high controller performance and real-time efficiency.
2Productivity
If MPC system significantly simplifies the process model for computational efficiency, then real-time optimization speed is improved, but model quality and controller performance deteriorate
Solution Approach 1:
The patent employs dynamic model simplification where the level of model detail adapts based on operating conditions and control needs. During critical transitions or when high precision is needed, more detailed models are used; during steady-state operation, simplified models suffice. This dynamic approach maintains both real-time speed and model quality where necessary.
Solution Approach 2:
The patent changes model parameters dynamically, switching between different model fidelities or activating/deactivating specific model components based on the current operating regime. This allows the system to use computationally intensive models only when their added value is warranted, maintaining optimization speed while preserving model quality in critical scenarios.
3Manufacturing precision
If dedicated programming and application-specific models are developed for each machine, then control accuracy for that specific application is improved, but adaptability to other applications and ease of deployment worsen
Solution Approach 1:
The patent develops a universal control framework with standardized interfaces and modular components that can be applied across different machines and applications. The architecture separates application-specific parameters from the core control logic, allowing the same framework to achieve high control accuracy for diverse applications without requiring dedicated programming for each case.
Solution Approach 2:
The patent applies local quality by allowing application-specific customization only where necessary (in local parameter settings, boundary conditions, or specific model parameters) while maintaining a standardized core architecture. This enables high control accuracy for each specific application through localized adaptations without sacrificing the overall adaptability and reusability of the control system.
4Measurement precision
If manual adaptation and refinement of models is performed over time, then model accuracy for the specific application is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent incorporates automated feedback mechanisms that continuously monitor system performance and automatically adjust model parameters to maintain or improve accuracy. This closed-loop approach eliminates or reduces the need for manual model refinement, achieving high model accuracy while minimizing the time and operational complexity associated with manual adaptation.
Solution Approach 2:
The patent implements self-service capabilities where the control system automatically performs model calibration, parameter tuning, and validation using operational data. This automation of model refinement processes reduces dependency on manual intervention, achieving improved model accuracy without the time loss and operational complexity of manual adaptation.
Data Source
AI summary
An analytics engine is provided for industrial automation applications. The engine may be modular, and may be instantiated upon receipt of a data structure, such as containing annotated data from or relating to a monitored and/or controlled machine or process. The module may be data-driven so that it is instantiated only as needed, upon receipt of the input data structure. The module then carries out analysis on the data, and outputs a data structure that can be used for further analysis, or directly by other modules for modeling, classification, optimization and/or control.