Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

Abstract

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity

“…This model is around 80% accurate [46]. Accuracy can be determined by one measurement or experiment, but to determine accuracy requires measurement to assess precision [47]- [49]. Accuracy measurements can be accurate but not necessarily exact [50], [51].…”

Modeling of artificial neural networks for silicon prediction in the cast iron production process

“…From the technical point of view, we can mention that the silicon content in pig iron is an important quality parameter to be monitored, as it acts as an indicator of the thermal condition and its decrease indicates the cooling of the blast furnace, which requires countermeasures to avoid serious problems in operation [27]. Since the silicon in the process comes from the raw materials, especially from the coke ash and gangue of the metallic charge, the use of raw materials with small variations in composition is one of the ways to control the content obtained in production and keep it as constant as possible with respect to its optimum level, which is intended to minimize the cost of secondary refining in the converters of steel mills [28]. It is also worth noting that the excess of silicon in pig iron requires a larger amount of calcium oxide (CaO) in the steel mill to perform refining, resulting in a larger slag volume and higher cost.…”