Optimization of Continuous Steel Annealing Operations Using Model Predictive Control at Tata Steel, India

Sujit A. Jagnade (), Sachin C. Patwardhan (), Kunal Kumar (), Arup K. Dey (), Sai K. Gudimetla (), Manish K. Singh (), Ajay K. Jha (), Gyan Prakash () and Jose M. Korath ()
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Sujit A. Jagnade: Automation Division, Tata Steel Limited, Jamshedpur 831001, India
Sachin C. Patwardhan: Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
Kunal Kumar: Systems and Control Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
Arup K. Dey: Cold Rolling Mill Operation, Tata Steel Limited, Jamshedpur 831001, India
Sai K. Gudimetla: Cold Rolling Mill Operation, Tata Steel Limited, Jamshedpur 831001, India
Manish K. Singh: Design and Engineering Division, Tata Steel Limited, Jamshedpur 831001, India
Ajay K. Jha: Cold Rolling Mill Operation, Tata Steel Limited, Jamshedpur 831001, India
Gyan Prakash: Automation Division, Tata Steel Limited, Jamshedpur 831001, India
Jose M. Korath: Automation Division, Tata Steel Limited, Jamshedpur 831001, India

Interfaces, 2025, vol. 55, issue 1, 48-65

Abstract: In steel manufacturing, continuous annealing is a crucial heat treatment applied to cold-rolled steel strips to achieve a prescribed temperature, which will ensure quality in terms of mechanical properties. However, controlling this process is challenging because of slow furnace temperature dynamics, significant time delays, frequent changes in the steel mass flow rate, target annealing temperature changes induced by steel grade transitions, and multivariable interactions within the furnace zones. To address these challenges, Tata Steel, India, with consultancy support from the Indian Institute of Technology Bombay, developed a novel model predictive control (MPC) technology-based solution for a continuous annealing furnace, which produces automotive grade steels. The dynamic model was developed using data from both perturbation trials and scraping historical processes. We then converted the model to a discrete-time state-space form and used it to formulate an optimal control problem over a moving time window. The solution generates optimal furnace setpoints by solving this finite-horizon optimal control problem each minute, ensuring smooth temperature transitions during steel grade changes while avoiding operational constraint violations. Tata Steel successfully implemented an MPC-based real-time supervisory optimal control solution, which became fully operational in January 2023. The implementation of the solution has led to a significant improvement in the proportion of annealed products meeting the premium quality band (±5°C of the target temperature), increasing from 30% (manually operated) to 50% (MPC operated), thereby ensuring better uniformity of properties. Furthermore, an 8% reduction in products outside the widest band (±15°C) has prevented the reprocessing of 13,000 tons of material from one line alone, annually. We have seen a consistent 8% reduction in specific fuel consumption per ton of steel. When considering Tata Steel’s current installations and those under commissioning, these improvements translate to savings of US$2.5 million and a reduction of 10,000 tons of CO 2 emissions annually.

Keywords: continuous annealing furnace; optimization; data-driven modelling; Kalman filter; model predictive control; Edelman Award; optimal control (search for similar items in EconPapers)
Date: 2025
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