Real-Time Lime Quality Control through Process Automation
Vipul Kumar Tiwari¹, Abhishek Choudhary², Umesh Kr. Singh³, Anil Kumar Kothari⁴, Manish Kr. Singh^5
1Vipul Kumar Tiwari*, Technologist, Automation Division, Tata Steel, Jamshedpur, India.
²Abhishek Choudhary, Sr. Manager, Lime plant, Tata Steel, Jamshedpur, India.
³Umesh Kr. Singh, Principal Technologist, Automation Division, Tata Steel, Jamshedpur, India.
⁴Anil Kumar Kothari, Chief (SM&C), Automation Division, Tata Steel, Jamshedpur, India.
⁵Manish Kr. Singh, Chief (One IT), Automation Division, Tata Steel, Jamshedpur, India.
Manuscript received on May 03 , 2021. | Revised Manuscript received on May 08, 2021. | Manuscript published on May 30, 2021. | PP: 1-10 | Volume-7 Issue-2, May 2021. | Retrieval Number: 100.1/ijese.B2502057221 | DOI: 10.35940/ijese.B2502.057221
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In the steel industry – Tata steel, India, most of the lime produced in the lime plant is used in the steel-making process at LD shops. The quality of steel produced at LD shops depends on the quality of lime used. Moreover, the lime also helps in the crucial dephosphorization process during steel-making. The calcined lime produced in the lime plant goes to the laboratory for testing its final quality (CaO%), which is very difficult to control. To predict, control and enhance the quality of lime during lime making process, five machine-learning-based models such as multivariate linear regression, support vector machine, decision tree, random forest and extreme gradient boosting have been developed using different algorithms. Python has been used as a tool to integrate the algorithms in the models. Each model has been trained on the past 14 months’ data of process parameters, collected from level 1 sensor devices, to predict the future quality of lime. To boost the model’s prediction performance, hyper-parameter tuning has been performed using grid-search algorithm. A comparative study has been done among all the models to select a final model with the least root mean square error in predicting and control future lime quality. After the comparison, results show that the model incorporating support vector machine algorithm has least value of root mean square error of 1.23 in predicting future lime quality. In addition to this, a self-learning approach has also been incorporated into support vector machine model to enhance its performance further in realtime. The result shows that the performance has been boosted from 85% strike-rate in +/-2 error range to 90% of strike-rate in +/-1 error range in real-time. Further, the above predictive model has been extended to build a control model which gives prescriptions as output to control the future quality of lime. For this purpose, a golden batch of good data has been fetched which has shown the best quality of lime (≥ 94% of CaO%). A good range of process parameters has been extracted in the form of upper control limit and lower control limit to tune the set-points and to give the prescriptions to the user. The integration of these two models (Predictive model and control model) helps in controlling the quality of lime 12 hours before its final production of lime in lime plant. Results show that both models (Predictive model and control model) have 90% of strike-rate within +/-1 of error in real-time. Finally, a human machine interface has been developed to facilitate the user to take action based on control model’s output. Eventually this work is deployed as a lime making process automation to predict and control the lime quality.
Keywords: Steel-Making, Quality Control, Process Automation, Machine-Learning, Human-Machine-Interface.