Differentiable Digital Twin for Nonlinear Process Optimization
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Solution Overview
Problem
Current industrial process optimization methods rely on gradient-free linear methods that fail to accurately capture non-linearities, limiting the effectiveness of control parameter optimization in industrial processes.
Innovation Solution
A two-stage ML-based framework using a causality learning engine and a machine learning engine to create an end-to-end differentiable digital twin model, which captures non-linearities by training on input and control parameters with gradient flows for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If gradient-free linear methods are used for optimization, then the optimization process is simple to implement, but the accuracy of capturing non-linearities is poor
Solution Approach 1:
The patent replaces traditional gradient-free linear optimization methods with a gradient-based neural network approach. The neural network learns the non-linear mapping between control parameters and process outcomes, enabling accurate capture of non-linearities while maintaining computational efficiency through gradient-based optimization.
Solution Approach 2:
The patent transforms the optimization approach by changing from linear parameter adjustments to non-linear parameter transformations through neural network activation functions. The neural network learns optimal parameter transformations that accurately represent non-linear process behavior while remaining computationally tractable.
2Measurement precision
If non-linear optimization methods are used, then the accuracy of capturing non-linearities is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary training of the neural network offline using historical process data. This pre-computation phase captures the non-linear relationships in advance, allowing the optimized model to make rapid predictions during actual process optimization without real-time computational burden.
Solution Approach 2:
The patent creates a digital twin - a virtual copy of the industrial process - using the trained neural network. This digital twin accurately replicates non-linear process behavior and can be used for optimization simulations without affecting the actual process, reducing computational risks and enabling extensive what-if analysis.
3Device complexity
If traditional digital twin models are used, then the model structure is simple, but the optimization capability is limited due to gradient-free methods
Solution Approach 1:
The patent replaces gradient-free optimization mechanisms with gradient-based neural network optimization. The neural network's differentiable architecture enables efficient gradient computation, allowing the digital twin to perform sophisticated non-linear optimization while maintaining a relatively simple feedforward network structure.
Data Source
AI summary
The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for facilitating creation of a digital twin of a process unit which can perform constrained optimization of control parameters to minimize or maximize an objective function. The system can capture non-linearities of the industrial process while the current Industrial Process models try to approximate non-linear process using linear approximation, which are not as accurate as Neural Networks. The proposed system can further create an end-to-end differentiable digital twin model of a process unit, and uses gradient flows for optimization as compared to other digital twin models that are gradient-free.