Predicting and Explaining Pig Iron Production on Charcoal Blast Furnaces: A Machine Learning Approach

Marcio Salles Melo Lima (), Enes Eryarsoy () and Dursun Delen ()
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Marcio Salles Melo Lima: Metalsider, Betim 32602-335, Brazil; Spears School of Business, Oklahoma State University, Tulsa, Oklahoma 74106
Enes Eryarsoy: Sabanci Business School, Sabanci University, Istanbul 34956, Turkey
Dursun Delen: Spears School of Business, Oklahoma State University, Tulsa, Oklahoma 74106; School of Management, Halic University, Istanbul 34445, Turkey

Interfaces, 2021, vol. 51, issue 3, 213-235

Abstract: Pig iron, the source for a variety of iron-based products, is traded in commodity markets. Therefore, enhanced productivity has significant economic implications for the producers. Pig iron is mainly produced inside of tall, vertical, thermodynamic reactors called blast furnaces that run 24 hours a day. The blast furnaces are too complex to model explicitly and are generally regarded as black boxes . In this study, we design, develop, and deploy novel machine learning models on a rich data sample covering more than 20 production variables spanning nine years of actual operational period, collected at one of the largest pig iron production plants in Brazil. We show that, given the blast furnace parameters, machine learning models are capable of unveiling novel insights by illuminating the black box and successfully predicting production levels at different configurations. These prediction models can be used as decision aids to improve production efficiencies. We also perform a sensitivity analysis of the trained models to identify and rank the input variables according to their relative importance. We present our findings, which are largely in line with the existing literature, and confirm their validity, practicality, and usefulness through consultations with subject matter experts.

Keywords: machine learning; pig iron production; blast furnace; predictive analytics; gradient boosting trees; sensitivity analysis; neural networks; regression (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:51:y:2021:i:3:p:213-235

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