First Principles Model for Batch Process Control
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Solution Overview
Problem
Batch processes often face challenges in controlling process variables due to unmeasured conditions and the inability to timely adjust parameters, leading to variations in final results, as measurements may not be available in time for process adjustments, and existing control methods like single input/single output loops can cause oscillations and fail to reach steady states.
Innovation Solution
A first principles model is used to simulate batch processes and configure multiple-input/multiple-output control routines, enabling the estimation of unmeasurable parameters and facilitating control strategies for predicting end times and product yields, while also detecting abnormal situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single input/single output control loops are used, then the control system is simple to implement, but the process variables oscillate and fail to reach steady state
Solution Approach 1:
The patent combines multiple input variables and multiple output variables into a unified control framework. Instead of treating each control loop independently, the system integrates them to account for interactions between variables, thereby eliminating oscillations and achieving steady state while maintaining reasonable complexity.
Solution Approach 2:
The control system is designed to handle multiple inputs and multiple outputs simultaneously, making it universally applicable to complex batch processes where variables interact. This multi-functional approach allows the system to manage temperature, pressure, flow rate, and other parameters in an integrated manner rather than through separate single-purpose loops.
2Measurement precision
If measurements are taken during batch processes, then process monitoring is achieved, but measurements are not available in time for process adjustments
Solution Approach 1:
The system performs preliminary actions by using measured parameters to predict future process states and making adjustments before deviations occur. The control system proactively adjusts process variables based on current measurements and predicted trends, rather than waiting for measurements to indicate problems have already developed.
Solution Approach 2:
The patent implements a feedback mechanism where measurements of process parameters are continuously fed back into the control system. This feedback loop enables real-time monitoring and automatic adjustment of process variables, ensuring that measurements are effectively used for timely process control despite inherent measurement delays.
3Ease of manufacture
If conventional control methods are used, then implementation is straightforward, but unmeasured conditions cause variations in final results
Solution Approach 1:
The patent introduces an intermediary computational layer that processes measured parameters and predicts the influence of unmeasured conditions. This intermediary system uses mathematical models and algorithms to estimate the effects of variables that cannot be directly measured, thereby compensating for their impact on batch consistency while maintaining straightforward implementation through software-based control.
Data Source
AI summary
A first principles model may be used to simulate a batch process, and the first principles model may be used to configure a multiple-input/multiple-output control routine for controlling the batch process. The first principles model may generate estimates of batch parameters that cannot, or are not, measured during operation of the actual batch process. An example of such a parameter may be a rate of change of a component (e.g., a production rate, a cell growth rate, etc.) of the batch process. The first principles model and the configured multiple-input/multiple-output control routine may be used to facilitate control of the batch process.