Hot Strip Mill Temperature Control Using Predictive Setpoints
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Solution Overview
Problem
Current control systems for hot strip mills struggle to optimize setpoint specifications across various units, leading to suboptimal material quality and increased energy and manufacturing costs, especially when producing steel strips with high demands on material quality, due to complex interactions between time, temperature, and structure development.
Innovation Solution
A higher-level process model on a data processing system optimizes setpoint specifications for individual units by exchanging and storing target and actual values, including temperatures, speeds, and cooling/heating rates, using optimization algorithms and subordinate process models to maintain setpoint specifications and improve product properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If individual unit control systems are used with aggregate-specific process models, then unit-specific process control is simplified, but interactions between units are not reflected leading to suboptimal overall process control
Solution Approach 1:
The patent merges individual unit control systems into a centralized control system that manages the entire hot strip mill process. This centralized system integrates process models for multiple units (furnace, rolling stands, cooling section) and optimizes setpoints across all units simultaneously, ensuring that interactions between units are properly accounted for while maintaining comprehensive process control.
Solution Approach 2:
The centralized control system performs multiple functions: it manages individual unit operations, optimizes overall process parameters, predicts steel strip temperature across different locations and times, and adjusts setpoints for multiple units based on global process objectives. This multi-functional approach resolves the contradiction by providing both unit-specific control capability and overall process optimization.
2Device complexity
If static target values are used for control, then control system complexity is reduced, but dynamic optimization of product properties and energy consumption is limited
Solution Approach 1:
The patent implements dynamic setpoint adjustment where target values for furnace temperature, rolling stand parameters, and cooling rates are continuously optimized based on real-time process conditions. The system uses process models to predict future steel strip temperature and microstructure development, then dynamically adjusts setpoints to achieve optimal product properties while minimizing energy consumption, rather than relying on fixed static targets.
Solution Approach 2:
The control system performs preliminary calculations using process models to predict the future state of the steel strip (temperature, microstructure) before actual processing occurs. This allows the system to pre-optimize setpoints for upcoming operations, enabling dynamic optimization without requiring complex real-time adjustments during critical processing phases.
3Ease of manufacture
If empirical knowledge and previous process analyses are used to define target values, then control implementation is simplified, but complex interactions between time, temperature, and microstructure development cannot be optimized
Solution Approach 1:
The patent implements a feedback mechanism where actual process measurements (temperature, speed) are continuously compared with predicted values from process models. This feedback loop allows the system to validate and refine its process models, ensuring they accurately represent the complex interactions between time, temperature, and microstructure development. The system uses this feedback to continuously optimize setpoints while maintaining reliable product quality.
Solution Approach 2:
The system transitions from using fixed empirical target values to dynamically changing parameters based on process conditions. Process models calculate optimal temperature profiles, heating rates, and cooling rates that account for complex interactions between parameters. This allows the system to maintain simplified control implementation while achieving reliable optimization of product properties through adaptive parameter adjustment.
Data Source
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AI summary
the invention relates to a method for the open-loop or closed-loop control of the temperature of a steel strip during hot working in a hot strip mill. A higher-level open-loop or closed-loop control involves a process model which predicts the temperature development of the hot strip. The target values of the individual units are adjusted on the basis of this predicted temperature development.