MPC Corrective Response Modeling for Uncertain Scheduled Disturbances
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Solution Overview
Problem
Current optimization algorithms for processing plants, particularly those with discontinuous processes, fail to account for uncertain scheduled disturbances, leading to prediction errors and violations of constraints due to assumptions about constant future operating conditions.
Innovation Solution
A computer-implemented method and apparatus that utilize a corrective response model incorporating a constraint failsafe parameter to generate optimization adjustment data, accounting for potential delays in scheduled events by adjusting the input vector and setting a constraint failsafe parameter based on predicted worst-case scenarios, ensuring compliance with inventory level limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If optimization algorithms assume constant future operating conditions, then computational simplicity is maintained, but prediction accuracy deteriorates due to uncertain scheduled disturbances
Solution Approach 1:
The patent applies preliminary action by pre-calculating adjustment values for scheduled events before they occur. The system identifies scheduled events in advance, computes their potential impact on constraints, and prepares corrective actions that are then applied when the events actually occur, improving prediction accuracy without requiring complex real-time calculations.
Solution Approach 2:
The patent changes parameters by introducing adjustment values that modify the input vector and constraint values based on scheduled events. Instead of assuming constant operating conditions, the system dynamically adjusts parameters like inventory levels and production rates to account for known future disturbances, maintaining computational efficiency while improving accuracy.
2Reliability
If adjustment values are incorporated into the corrective response model, then constraint compliance is improved, but computational load increases
Solution Approach 1:
The system performs preliminary calculations to determine adjustment values for scheduled events before the optimization run. By pre-computing these values and storing them, the system reduces the computational load during the actual optimization process while still achieving improved constraint compliance through the incorporation of these pre-calculated adjustments.
3Measurement precision
If the input vector is adjusted to account for scheduled event delays, then optimization accuracy is improved, but model complexity increases
Solution Approach 1:
The patent applies parameter changes by modifying the input vector parameters to include adjustment values that account for scheduled event timing uncertainties. This approach improves optimization accuracy by incorporating realistic timing variations while maintaining a relatively simple model structure that builds upon the existing corrective response model framework.
Data Source
AI summary
Embodiments provide for improved optimization associated with a dynamic control scheme (e.g., an MPC architecture). Some embodiments identify at a first timestamp prior to a planned start time of a scheduled event associated with a processing unit, an adjustment value associated with the scheduled event. The adjustment value may be defined based on a predicted possible worst scenario of starting the scheduled event at a start time that differs from the planned start time. Some embodiments update a corrective response model to include the adjustment value. The corrective response model may comprise a step response matrix, an input vector parameter, and a constraint failsafe parameter. Some embodiments generate, using the corrective response model and based at least in part on operating condition data associated with the scheduled event, an optimization adjustment data, and generate updated optimization data by applying the optimization adjustment data to offset initial optimization data.