Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms

Md Obaidullah Ansari; Somnath Chattopadhyaya; Joyjeet Ghose; Shubham Sharma; Drazan Kozak; Changhe Li; Szymon Wojciechowski; Shashi Prakash Dwivedi; Huseyin Cagan Kilinc; Jolanta B Królczyk; Dominik Walczak

doi:10.3390/ma15020670

Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms

Materials (Basel). 2022 Jan 17;15(2):670. doi: 10.3390/ma15020670.

Authors

Affiliations

¹ Department of Mechanical Engineering, Indian Institute of Technology, Dhanbad 826004, India.
² Department of Production and Industrial Engineering, Birla Institute of Technology Mesra, Ranchi 835215, India.
³ Department of Mechanical Engineering, IK Gujral Punjab Technical University, Kapurthala 144603, India.
⁴ Department of Mechanical Engineering, University Centre for Research and Development, Chandigarh University, Punjab 140413, India.
⁵ Mechanical Engineering Faculty, University of Slavonski Brod, 35000 Slavonski Brod, Croatia.
⁶ School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.
⁷ Faculty of Mechanical Engineering and Management, Poznan University of Technology, 60-965 Poznan, Poland.
⁸ GL Bajaj Institute of Technology & Management, Greater Noida, Gautam Buddha Nagar, Uttar Pradesh 201310, India.
⁹ Department of Civil Engineering, Istanbul Esenyurt University, Istanbul 34510, Turkey.
¹⁰ Department of Manufacturing and Materials Engineering, Faculty of Mechanical Engineering, Opole University of Technology, Mikolajczyka 5, 45-271 Opole, Poland.

Abstract

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

Keywords: artificial neural network; breakout prediction system; continuous casting; mold breakout; steel slab.