Dynamic Process Control Using Short-Term and Terminal Predictions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional production process control techniques face challenges in accuracy as predictions are made over longer timeframes, leading to compounded inaccuracies when aggregating short-term predictions, and optimizations using short-term predictions do not accurately reflect long-term predictions.
Innovation Solution
A method involving a process model that determines a variable state, using a short-term prediction module and a terminal value prediction module to generate predictions, and a control optimization module to generate control parameters based on these predictions for optimizing and controlling the production process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If cascading short-term predictions are aggregated over time, then predictions can be made for longer timeframes, but inaccuracies are compounded and amplified
Solution Approach 1:
The system segments the prediction task into two distinct modules: a short-term prediction module for immediate future states and a terminal value prediction module for long-term outcomes. This segmentation allows each module to specialize in its respective timeframe, preventing the compounding of errors that occurs when short-term predictions are cascaded over long periods. The short-term module handles near-term dynamics with high frequency data, while the terminal value module captures long-term trends, and both contribute to the overall control optimization without one amplifying the inaccuracies of the other.
2Productivity
If optimizations are made using short-term predictions, then immediate control adjustments can be implemented, but the optimizations do not accurately reflect long-term prediction results
Solution Approach 1:
The system merges the outputs of both the short-term prediction module and the terminal value prediction module into a unified control optimization process. The control optimization module simultaneously considers both the immediate future states (from short-term predictions) and the terminal outcomes (from long-term predictions) when determining optimal control actions. This merging ensures that control optimizations are both immediately actionable and aligned with long-term objectives, resolving the contradiction between rapid implementation and accurate reflection of long-term effects.
Data Source
AI summary
Techniques are provided for dynamic prediction-based regression optimization. In one embodiment, the techniques involve determining, via a process model, a variable state of the process model, wherein the variable state includes a first input state variable, a first output state variable, and a first control parameter, generating, via a short-term prediction module, a first prediction of a first update of the variable state, generating, via a terminal value prediction module, a second prediction of a second update to the variable state, generating, via a control optimization module, a second control parameter based on the first prediction and the second prediction, and controlling, via a processor, a production process of the process model based on the second control parameter.