Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace\u2019s thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125, 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson\u2019s correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values and those calculated by the neural network

The superhydrophobicity of polymer surfaces: Recent developments

Superhydrophobicity is the extreme water repellence of highly textured surfaces. The field of superhydrophobicity research has reached a stage where huge numbers of candidate treatments have been proposed and jumps have been made in theoretically describing them. There now seems to be a move to more practical concerns and to considering the demands of individual applications instead of more general cases. With these developments, polymeric surfaces with their huge variety of properties have come to the fore and are fast becoming the material of choice for designing, developing, and producing superhydrophobic surfaces. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 49: 1203–1217, 201