Abstract
Predicting the temperature of the steel strip in the annealing process in a hot-dip galvanizing line (HDGL) is important to ensure the physical properties of the processed material. The development of an accurate model that is capable of predicting the temperature the strip will reach according to the furnace’s variations in temperature and speed, its dimensions and the steel’s chemical properties, is a requirement that is being increasingly called for by industrial plants of this nature. This paper presents a comparative study made between several types of algorithms of Data Mining and Artificial Intelligence for the design of an efficient and overall prediction model that will allow determining the strip’s variation in temperature according to the physico-chemical specifications of the coils to be processed, and fluctuations in temperature and speed that are recorded within the annealing process. The ultimate goal is to find a model that is effectively applicable to coils of new types of steel or sizes that are being processed for the first time. This model renders it possible to fine-tune the control model in order to standardise the treatment in areas of the strip in which there is a transition between coils of different sizes or types of steel.
Highlights
The commissioning of new production plants, the processing of new types of products or the readjustment of the original production conditions tend to require a large amount of human effort and a lot of time and money
The result of the training and validation process is shown in table V
It can be seen that the validation Root Mean Squared Error (RMSE), performed with 30 % of cases not used in the creation of the models, is close to 1.0 % for the models based on K-nearest-neighbours (IBk)
Summary
The commissioning of new production plants, the processing of new types of products or the readjustment of the original production conditions tend to require a large amount of human effort and a lot of time and money. In these cases, having robust models that are capable of responding correctly to the requirements of the products that have already been processed and of new ones is a need that is being increasingly called for in today’s industry. The challenge lies in designing overall models that learn from the past yet which are capable of still dealing efficiently with any new operating conditions that may arise in the future
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