System for Prediction and Prevention of Defects in the Process of Metal Annealing in Lingering Tower Furnace
Abstract
Due to a number of economic and ecological parameters, hot galvanizing of steel strips is one of the optimal methods of protecting metals against corrosion. Continuous hot-dip galvanizing lines are the most efficient units implementing this manufacturing process.
One of the main components of a continuous hot-dip galvanizing unit is the lingering furnace providing annealing of the drawn strip in the protective gas atmosphere.
As the unit is enclosed, the annealing process cannot be controlled along the strip length, and parameters directly characterizing the annealing process are: the strip temperatures measured by pyrometers located at the end of each section of the furnace (heating, holding and cooling sections).
This feature of the information support of the control system of thermal conditions of the lingering furnace as well as significant transport delays impose serious constraints on the strip annealing process control. One of the constraints is the inability to provide strip temperature control along the furnace zones, which results in the violation of the annealing technology and inferior quality products.
One of the most promising ways to solve the problem of control for such an object is to develop an intelligent control system, in which control actions on the thermal load of various zones could be chosen by indirect process parameters taking into account past experience of heating.
As the main parameter of the galvanizing process is production of quality galvanizing coating, one of the most important elements of the control system is the subsystem of predicting the final product quality. On the basis of the data from this subsystem one can introduce timely adjustment of the thermal conditions of heating and holding zones.
The paper describes the structure of the developed prediction module of inferior products manufacture. The module is based on the mathematical model implemented by means of the artificial neural network, which is capable of making adequate real-time description of processes taking place in the furnace and in the galvanizing unit; the network can also form the value of probability ratio of inferior quality product.
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DOI: http://dx.doi.org/10.24892/RIJIE/20160307
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