Fixed-Bed Reactor Temperature Control Under Feed Composition Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies for controlling the temperature profile in fixed bed reactors do not effectively account for feed composition changes, leading to inefficiencies in reactor operation, conversion, and product quality.
Innovation Solution
A system and method that utilize interstage model error inputs to infer feed composition changes, integrating this information into a Model Predictive Control (MPC) algorithm to adjust the temperature profile across multiple fixed beds, including adjustments to feed temperature and intercooling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional PID loop control or MPC is used without feed composition inference, then the control system is simpler and easier to operate, but the temperature profile control accuracy deteriorates when feed composition changes occur
Solution Approach 1:
An external model is introduced as an intermediary component that infers feed composition changes from temperature measurements. This model acts as a mediator between the process measurements and the MPC controller, providing composition estimates without requiring direct composition sensors. The model error from this intermediary is then fed into the MPC algorithm to improve temperature profile control accuracy while maintaining relative system simplicity.
Solution Approach 2:
The system implements feedback by using the external model to continuously estimate feed composition changes based on temperature measurements. This inferred composition information is fed back into the MPC controller, which adjusts control actions accordingly. The model error feedback loop allows the system to adapt to composition changes dynamically, improving control accuracy without requiring complex direct composition measurement systems.
2Productivity
If feed composition changes are not inferred, then the control algorithm is simpler and faster to execute, but conversion and product yield deteriorate under varying feed conditions
Solution Approach 1:
The external model serves as an intermediary that automatically infers feed composition changes from readily available temperature measurements. This approach maintains high productivity by providing composition estimates that enable the MPC controller to optimize conversion and product yield under varying feed conditions, without requiring complex direct composition analysis or manual intervention.
Solution Approach 2:
The system replaces direct mechanical or analytical composition measurement systems with a software-based external model that infers composition from temperature data. This substitution maintains computational efficiency and execution speed while providing the composition information needed to maximize conversion and product yield through automated MPC control adjustments.
3Reliability
If model error from external model is not integrated into MPC, then the control system is easier to implement, but the ability to compensate for feed composition disturbances is lost
Solution Approach 1:
The model error from the external composition inference model is fed back into the MPC algorithm as an additional input. This feedback mechanism enables the MPC controller to compensate for feed composition disturbances by adjusting control actions based on the inferred composition changes. The relatively simple integration of model error feedback provides robust disturbance compensation without requiring complex multi-variable control architectures.
Data Source
AI summary
Disclosed are systems, servers and methods for improving temperature profile control in a reactor with at least three fixed beds, exothermic reactions and interstage cooling. A model of the temperature differential across the first bed is developed and its error is used to infer unmeasured feed composition disturbances, which are used in the control of the downstream fixed beds for faster response to unmeasured feed composition changes and improved control of the temperature profile throughout the reactor. The first bed model error is then used as an input into an overall model that predicts reactor temperature profiles, which provides advanced notice of reactions in downstream beds, and enables efficient adjustment and compensation to a feed composition change. A Model Predictive Control (MPC) algorithm is applied to adjust the bed intercooling and first bed feed temperature so that the reactor temperature profile can be more precisely controlled.