Advanced Batch Control Using Predictive Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Batch process control systems face challenges due to difficulty in measuring product properties until the end of the batch, leading to variability in final product specifications and inability to proactively compensate for disturbances, resulting in inefficient process control and product quality issues.
Innovation Solution
An advanced batch control method that uses empirical or semi-empirical models to predict final product quality attributes, applying control algorithms in real-time at decision points within the batch process to adjust process variables, thereby reducing variability and improving process efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If basic automation systems adhere to defined recipe conditions, then process repeatability is improved, but product quality variability remains high due to inability to compensate for disturbances
Solution Approach 1:
The system performs preliminary actions by predicting final product quality attributes at intermediate decision points during the batch process. Using empirical or semi-empirical models, the controller forecasts quality outcomes and proactively adjusts process variables before the batch completes, rather than waiting for end-point measurements to guide adjustments in subsequent batches.
Solution Approach 2:
The system implements feedback by continuously measuring process variables during the batch process, comparing actual values against model predictions, and using this information to adjust process conditions in real-time. The controller uses measured process variable trajectories to update predictions of final product quality and makes corrective adjustments to manipulated variables to ensure quality targets are met.
2Manufacturing precision
If control adjustments are made at batch completion based on actual measurements, then product quality can be corrected for future batches, but real-time compensation for disturbances during the batch process is not possible
Solution Approach 1:
The system performs preliminary quality assessment and control adjustments at intermediate decision points during the batch process, rather than waiting until batch completion. By using predictive models to estimate final product quality based on current process variable trajectories, the controller can make timely adjustments to process conditions to ensure quality targets will be achieved by batch end.
Solution Approach 2:
The system transitions from static, batch-to-batch correction to dynamic, real-time control within batches. The controller continuously updates its predictions of final product quality as the batch progresses and process variables evolve, making adaptive adjustments to manipulated variables in real-time based on current process state and predicted outcomes.
3Manufacturing precision
If complex nonlinear dynamic models are used for control, then control accuracy is improved, but model availability and system complexity become problematic
Solution Approach 1:
The system changes the nature of the models used, transitioning from complex first-principles nonlinear dynamic models to empirical or semi-empirical models based on data-driven approaches. These models correlate measured process variables with final product quality attributes using statistical or machine learning techniques, achieving comparable or superior predictive accuracy without requiring detailed mechanistic understanding of the complex batch process dynamics.
Data Source
AI summary
A method for advanced batch control of a batch process. The method discloses completing at least one cycle of the batch process and collecting data on at least one process variable and at least one product property. A model is created based on the data, wherein the model comprises inputs. Thereafter the batch process is initiated. At one or more decision points the model is utilized to obtain outputs. A controller utilizes the model outputs to control the batch in accordance with the model outputs. Final product properties are reached at decreased variability compared to the prior art.