Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

[EN] Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.This work is supported by a Project (AA-ELACERO, P211LH021-023) of the National Key Research and Development Program of Automatic, Robotic and Artificial Intelligence of Cuba.González-Yero, G.; Ramírez Leyva, R.; Ramírez Mendoza, M.; Albertos, P.; Crespo, A.; Reyes Alonso, JM. (2021). Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control. Metals. 11(1):1-21. https://doi.org/10.3390/met1101005612111

17. Simpozij „Materijali i metalurgija“ – dopuna „Zbornik sažetaka”

In Metalurgija 63 (2024) 2,303-320 published „ Book of Abstracts “ (224). Deadline for received of Abstracts was November, 30,2023 y. Many authors have request new deadline by March, 25, 2024 y. Organizing committee have accept new deadline. Now it published supplements of 103 Abstracts.U Metalurgiji 63 (2024) 2,303-320 objavljen je Zbornik sažetaka (224). Rok za primitak sažetke je bio 30. studeni 2023. god. Mnogi autori zatražili novi rok do 25.03.2024. Organizacijski odbor Simpozija je prihvatio novi termin. Objavljuje se sada dodatnih još 160 sažetaka

Refining and Casting of Steel

Steel has become the most requested material all over the world during the rapid technological evolution of recent centuries. As our civilization grows and its technological development becomes connected with more demanding processes, it is more and more challenging to fit the required physical and mechanical properties for steel in its huge portfolio of grades for each steel producer. It is necessary to improve the refining and casting processes continuously to meet customer requirements and to lower the production costs to remain competitive. New challenges related to both the precise design of steel properties and reduction in production costs are combined with paying special attention to environmental protection. These contradictory demands are the theme of this book

Energy Savings in EAF Steelmaking by Process Simulation and Data-Science Modeling on the Reproduced Results

Electric-Arc-Furnace (EAF)-based process route in modern steelmaking for the production of plates and special quality bars requires a series of stations for the secondary metallurgy treatment (Ladle-Furnace, and potentially Vacuum-Degasser), till the final casting for the production of slabs and blooms in the corresponding continuous casting machines. However, since every steel grade has its own melting characteristics, the melting (liquidus) temperature per grade is generally different and plays an important role in the final casting temperature, which has to exceed by somewhat the melting temperature by an amount called superheat. The superheat is adjusted at the ladle-furnace (LF) station by the operator who decides mostly on personal experience but, since the ladle has to pass from downstream processes, the liquid steel loses temperature not only due to the duration of the processes till casting but also due to the ladle refractory history. Simulation software was developed in order to reproduce the phenomena involved in a meltshop and influence downstream superheats. Data science models were deployed in order to check the potential of controlling casting temperatures by adjusting liquid-steel exit temperatures at LF

Neural Network Models for Assessing the Financial Condition of Enterprises for Supply Chain

The paper deals with the task of assessing the financial condition of enterprises. To solve it, we prove the necessity of building a neural network model for supply chain. A set of financial ratios is defined as the input parameters of the model: the current liquidity ratio of the enterprise, the equity ratio, the equity turnover ratio, and the return on equity ratio. The output parameters were the types of the financial condition of enterprises: an unstable state (regression), a normal state (stable) and an absolutely stable state (progression). The volume of input data for building neural network models for assessing the financial condition of enterprises amounted to 210 records. The construction and evaluation of the effectiveness of neural network models are based on the analytical platform Deductor. There have been built 32 modifications of neural network models with different architectures and trained with different samples formed randomly from the source data. To assess the effectiveness of the models built, a technique has been developed, which includes the stages of testing neural networks, evaluating their accuracy and average classification error taking into account weighting factors assigned by an expert. The results of calculations of errors of the first and second type for each financial condition, as well as the average total classification error,  are presented. The best model with a minimum average classification error, which is a single-layer perceptron with 10 hidden neurons, was chosen. The classification accuracy of the model was about 98%. The neural network model is adequate and can be effectively used to solve the problem of assessing the financial condition of enterprises

SHMD \u272024 – Book of abstracts

Book of abstracts of the 17th International Symposium of the Croatian Metallurgical Society - SHMD \u272024 - Materials and metallurgy, Zagreb, Croatia, April 18-19 2024