Adaptive Deep Learning Prediction for Industrial Systems
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Solution Overview
Problem
Complex industrial systems, such as alumina production systems, face challenges in achieving accurate and real-time prediction of production indicators and key process parameters due to their dynamic and nonlinear nature, which existing deep learning technologies cannot effectively address, especially with limited training datasets and short decision-making cycles.
Innovation Solution
An adaptive deep learning-based method is introduced, involving the establishment of dynamic models, offline and online deep learning prediction models, and a deep learning correction model using LSTM networks, with self-correction mechanisms to ensure real-time prediction accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing deep learning technology is applied to complex industrial systems, then prediction capability can be achieved, but prediction accuracy deteriorates due to strong nonlinearity and coupling of variables
Solution Approach 1:
The patent segments the complex industrial system into multiple subsystems, each with its own prediction model. Instead of attempting to model all variables simultaneously, the system divides the prediction task into smaller, manageable segments that can be handled individually, thereby improving overall prediction accuracy in nonlinear and coupled environments.
Solution Approach 2:
The patent introduces an intermediary correction model that adjusts the outputs of base prediction models. This correction model acts as a mediator to compensate for errors and improve accuracy, particularly addressing the strong nonlinearity and coupling issues by learning the residual errors and applying corrections iteratively.
2Measurement precision
If complete information space deep learning technology is used, then model accuracy can be improved, but adaptability to changing production conditions deteriorates
Solution Approach 1:
The patent implements dynamic adaptation mechanisms where the prediction models are continuously updated and corrected based on incoming data and changing production conditions. The correction model dynamically adjusts its parameters to adapt to new scenarios, maintaining both accuracy and versatility in response to varying raw materials, process conditions, and operational modes.
Solution Approach 2:
The patent employs feedback loops where prediction errors are continuously monitored and fed back into the correction model. This feedback mechanism enables the system to learn from past performance and automatically adjust to changing conditions, thereby maintaining high adaptability while preserving model accuracy through iterative refinement.
3Measurement precision
If large training datasets are used to improve prediction accuracy, then model precision increases, but training time and decision-making cycle duration increase
Solution Approach 1:
The patent segments the training process into multiple stages, where base models are trained on smaller datasets and then progressively refined through correction models. This segmented approach allows the system to achieve high prediction accuracy without requiring extremely large training datasets, thereby reducing training time and enabling faster decision-making cycles.
Solution Approach 2:
The patent applies partial training strategies where the correction model is trained on a subset of data or uses transfer learning from pre-trained base models. This partial action approach achieves sufficient prediction accuracy without the need for exhaustive training on complete datasets, significantly reducing training time while maintaining acceptable precision levels.
4Measurement precision
If complex model structures are used to capture nonlinear relationships, then prediction accuracy improves, but model complexity and computational burden increase
Solution Approach 1:
The patent segments the complex prediction task into multiple simpler models rather than using a single highly complex model. Each segment handles specific aspects of the nonlinear relationships, making individual models more manageable and interpretable while collectively achieving high prediction accuracy through their combined output.
Solution Approach 2:
The patent introduces a correction model as an intermediary layer that simplifies the overall system architecture. Instead of requiring one extremely complex model to capture all nonlinear relationships, the correction model acts as a mediator that refines the outputs of simpler base models, thereby achieving high accuracy with reduced overall model complexity and lower computational burden.
Data Source
AI summary
Disclosed are an adaptive deep learning-based intelligent prediction method, apparatus, and device for a complex industrial system, and a storage medium. The method includes establishing a dynamic model for a complex industrial system; establishing an offline deep learning prediction model using the dynamic model; establishing an online deep learning prediction model using the offline deep learning prediction model; establishing a deep learning correction model based on a structure that is the same as a structure of the online deep learning prediction model; and correcting the online deep learning prediction model using the deep learning correction model; where the online deep learning prediction model predicts a parameter of the complex industrial system in real time. The offline deep learning prediction model, the online deep learning prediction model, the deep learning correction model, and a self-correction mechanism are established to achieve accurate real-time prediction of the complex industrial system.