Nonlinear MPC for Batch Reaction Yield and Energy
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Solution Overview
Problem
Batch reaction processes in industries such as chemical, biochemical, and pharmaceutical face challenges in maintaining optimal operating conditions due to variability in feedstock and process phases, leading to sub-optimal performance and the need for frequent adjustments, which human operators cannot efficiently manage.
Innovation Solution
The implementation of model predictive control (MPC) systems using dynamic multivariate predictive models and nonlinear control models to optimize batch reaction processes by adjusting manipulated variables and controlling key parameters like temperature and feedstock concentrations, enabling real-time optimization and improved yield and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If classical model predictive control is used for batch reaction systems, then the control design is simpler, but the control performance is sub-optimal due to inability to handle time-variant nonlinear relationships
Solution Approach 1:
The patent applies dynamics by transitioning from static linear MPC models to dynamic nonlinear models that adapt to changing batch phases. The control model evolves from fixed parameter relationships to time-variant models that capture the nonlinear behavior at different stages of batch processing, enabling the system to respond appropriately to phase transitions while maintaining manageable complexity through structured model development.
Solution Approach 2:
The patent implements parameter changes by modifying the control model from linear to nonlinear representations. This involves changing the mathematical parameters and relationships in the predictive model to accurately reflect the nonlinear process behavior at different batch phases, thereby improving control performance without sacrificing excessive design complexity.
2Productivity
If frequent adjustments are made to maintain optimal conditions, then process performance can be improved, but operational complexity increases beyond human capability
Solution Approach 1:
The patent applies self-service by implementing an automated control system that continuously monitors batch phase transitions and autonomously adjusts control parameters. The nonlinear MPC system serves itself by automatically detecting phase changes and computing optimal control actions without human intervention, thereby maintaining high process performance while eliminating the need for complex manual operations.
Solution Approach 2:
The patent implements feedback mechanisms where the control system continuously monitors process variables, detects phase transitions, and adjusts control parameters in real-time. This closed-loop feedback approach enables frequent adjustments to maintain optimal performance automatically, reducing operational complexity by replacing manual monitoring and adjustment with automated feedback-driven control.
3Manufacturing precision
If batch processing phases are recognized and handled differently, then control precision is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the batch process into distinct phases (filling, initiation, lag, production, exhaustion, termination) and developing specific control strategies for each phase. This segmentation enables precise control tailored to each phase's characteristics while managing system complexity through a structured, modular approach to phase recognition and control.
Solution Approach 2:
The patent implements local quality by applying phase-specific control parameters and models to different batch phases. Each phase receives customized control treatment based on its unique characteristics, improving control precision locally at each stage while maintaining overall system manageability through the systematic organization of phase-specific control strategies.
Data Source
AI summary
The present invention provides novel techniques for controlling batch reaction processes. In particular, a parametric hybrid model may be used to parameterize inputs and outputs of batch reaction processes. The parametric hybrid model may include an empirical model, a parameter model, and a dynamic model. Critical quality parameters, which are correlated with, but not the same as, end-of-batch quality values for the batch reaction processes may be monitored during cycles of the batch reaction processes. The quality parameters may be used to generate desired batch trajectories, which may be used to control the batch reaction processes during the cycles of the batch reaction processes.