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Article summary:
1. The laminar cooling process (LCP) is an important procedure in hot steel strip rolling, used to cool the steel strip from an initial temperature of roughly 800–950 °C down to a coiling temperature (CT) of roughly 550–700 °C.
2. Various models have been developed for modeling and monitoring the LCP, such as exponential model, statistical model, differential model, neural networks and support vector machines.
3. Distributed parameter systems (DPSs) are used to accurately monitor the temperature variation of the steel strip in the spatial domain and meet the requirements of the temperature uniformity in a large spatial range.
Article analysis:
This article provides a comprehensive overview of modeling and monitoring for laminar cooling process of hot steel strip rolling with time-space nature. It presents various models that have been developed for this purpose, such as exponential model, statistical model, differential model, neural networks and support vector machines. It also discusses distributed parameter systems (DPSs), which can accurately monitor the temperature variation of the steel strip in the spatial domain and meet the requirements of the temperature uniformity in a large spatial range.
The article is generally reliable and trustworthy as it provides detailed information on various models that have been developed for modeling and monitoring laminar cooling process of hot steel strip rolling with time-space nature. The article also cites relevant research papers to back up its claims. However, there are some potential biases that should be noted. For example, while discussing distributed parameter systems (DPSs), it does not mention any potential risks associated with using these systems or any possible counterarguments against their use. Additionally, while discussing various models that have been developed for this purpose, it does not provide any evidence or data to back up its claims about their effectiveness or accuracy. Furthermore, it does not present both sides equally when discussing distributed parameter systems (DPSs).
In conclusion, this article provides a comprehensive overview of modeling and monitoring for laminar cooling process of hot steel strip rolling with time-space nature but there are some potential biases that should be noted when evaluating its trustworthiness and reliability.
Topics for further research:
Risks associated with distributed parameter systems
Accuracy of different models for laminar cooling process
Advantages and disadvantages of using distributed parameter systems
Comparison of different models for laminar cooling process
Data-driven modeling for laminar cooling process
Real-time monitoring of laminar cooling process