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Traditionally, mechanical property estimation is carried out by destructive testing, which is costly and time consuming. Sometimes, the time schedule in the mill is so tight that coils are dispatched, while the samples are still under investigation; thus, knowledge of the strip quality immediately after rolling without mechanical testing can save a lot of time and money. As the rolling process is complex and final mechanical properties of steel depend on many parameters, it is almost impossible to develop an accurate first principle based mathematical model, so an artificial neural network based model to predict the mechanical properties of hot rolled steel strip has been developed. This paper describes the neural network based online system that helps in predicting mechanical properties of interstitial free (IF) steel strip and also elaborates how this models can help in capturing various metallurgical phenomena during rolling.

Strict monitoring and prediction of endpoints in a Basic Oxygen Furnace (BOF) are essential for end-product quality and overall process efficiency. Existing control models are mostly developed based on thermodynamic principles or by deploying advanced sensors. This article aims to propose a novel hybrid algorithm for endpoint temperature, carbon, and phosphorus, based on heat and mass balance and a data-driven technique. Three types of static models were established in this study: firstly, theoretical models, based on user-specified inputs, were formulated based on mass and energy balance; secondly, artificial neural networks (ANN) were developed for endpoints predictions; finally, the proposed hybrid model was established, based upon exchanging outputs among theoretical models and ANNs. Data of steelmaking production details collected from 28,000 heats from Tata Steel India were used for this article. Machine learning model validation was carried out with five-fold cross-validation to ensure generalizations in model predictions. ANNs are found to achieve better predictive accuracies than theoretical models in all three endpoints. However, they cannot be directly applied in any steelmaking plants, due to possible variations in the production setting. After applying the hybrid algorithm, normalized root mean squared errors are reduced for endpoint carbon and phosphorus by 3.7% and 9.77%.

Electrical resistivity of commercially produced plain carbon manganese steel has been experimentally measured at room temperature (28-30°C) using four-probe method. Resulting data were used to generate both regression based and artificial neural network-based models for prediction of electrical resistivity from the chemical composition of steel. It was found that both models were capable of predicting the resistivity within ±5% error band. Analysis of data also indicated carbon to be the most influential element to increase resistivity followed by manganese and silicon. A comprehensive literature review indicates no such advanced resistivity prediction model is available in the public literature for commercially produced steel with wide variation in carbon content (0.03 0.85 wt-%), manganese content (0.35-1.50 wt-%) and silicon content (0.015-0.90 wt-%).

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.

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