Artificial Neural Network and Its Application in Steel Industry

Abstract

The recent developments in computational intelligence has enhances the applicability of empirical modelling in different areas particularly in the area of machine learning. These new approaches are based on analysing the data about a system, in particular finding connections between the system state variables (input, internal and output variables) without having precise knowledge about the physical behaviour of the system. These data driven methods explain advances on conventional empirical modelling and include contributions from many overlapping fields like Artificial Intelligence (AI), Computational Intelligence (CI), Soft Computing (SC), Machine Learning (ML), Intelligent Data Analysis (IDA), and Data Mining (DM). The most popular computational intelligence techniques used in process modelling of steel industry includes neural networks, fuzzy rule-based systems, genetic algorithms as well as approaches to model integration. This chapter describes mainly the application of Artificial Neural Network (ANN) in steel industry. ANN has extensively used in improving and controlling different processes of steel industry like steel making, casting and rolling which lead to indirect energy savings through reduced product rejects, improved productivity and reduced down time. The efficiency of artificial neural network tool in handling steel plant processes has been discussed in detail. ANN based models are found to be very potential to handle very complex, dynamic and non-linear problems.

Settlement Forecast of Marine Soft Soil Ground Improved with Prefabricated Vertical Drain-Assisted Staged Riprap Filling

“…In BPANNs, every four adjacent consecutive settlements with equal time interval (∆t = 5 d) were taken as an input sample sequence (a 1 , a 2 , a 3 , and a 4 ), and the immediately adjacent fifth settlement was considered as the target sample (y 1 ), as shown in Figure 5. In this study, the number of input layer units for each set of training samples is n = 4, the number of output layer units is q = 1, and the formula for determining the number of hidden layer units p can be calculated by Equation (17) [58]:…”

“…where p, n, and q are annotated in Figure 5. And parameter x ∈ (1, 10) [58], thus p ∈ (3,12), and the optimal value of p can be judged by the BPANN training error. Taking Point B as an example, when p = 8, the neural network and weight matrices (W) of BPLM and BPGD are presented in Figure 6.…”

Prediction of properties over the length of the coil during thermo-mechanical processing using DNN

“…It has also helped in improving the understanding about various metallurgical phenomena takes place during rolling. Mohanty and Bhattacharjee [20] explained in detail the success of ANN in steel industry in predicting and controlling processes by understanding the physics behind it. Mohanty et al [21] has shown ANN could be used in estimating and optimizing properties of microalloyed steel,, the result has been validated by thermodynamic calculation and plant trial.…”

Prediction of electrical resistivity of steel using artificial neural network

“…Using an artificial neural network (ANN) model, it is possible to predict electrical resistivity of steels with better accuracy compared to the aforementioned methods. ANN is a tool which can help analyse non-linear relationships in complex systems within a data framework [15][16][17][18]. Erzin et al [19] had developed an ANN-based model for prediction of electrical resistivity of soil from its thermal resistivity.…”