Title: Application of ANN in Six Sigma for CO modelling and energy efficiency of blast furnace: a case study of an Indian pig iron manufacturing organisation

Authors: Parag Sen

Addresses: Department of Chemical Engineering, National Institute of Technology Durgapur, Mahatma Gandhi Rd, A-Zone, Durgapur, West Bengal 713209, India

Abstract: The current paper presents a case study of an Indian pig iron manufacturing organisation to model the CO emission from the blast furnace by applying Six Sigma. The problem for the current organisation is the variation of CO from blast furnace which often exceeds the prescribed limit. Hence, the main objective of the paper is to identify significant process parameters responsible for CO emission by applying define-measure-analyse-improve-control (DMAIC) methodology of Six Sigma because of its structured approach to problem solving. However, this paper also shows a new technique by using artificial neural network (ANN) as a tool in Six Sigma. Results suggest that coke consumption is the most important parameter to influence CO emission from the perspective of cost. Frequent high concentration of CO implies that heat is leaving the furnace in the form of coke consumption, which needs to be improved using best available technologies (BAT). After implementing BAT, the process capability improves from 0.80 to 1.18, resulting in satisfactory reduction of CO concentration and energy consumption. Application of Six Sigma may help the organisation to save around Rs. 5 crore (50 million) annually.

Keywords: carbon monoxide modelling; six sigma; ANNs; artificial neural networks; process capability; blast furnaces; pig iron manufacturing; energy efficiency; case study; India; process parameters; carbon emissions; define measure analyse improve control; DMAIC; coke consumption; air pollution.

DOI: 10.1504/IJSSCA.2015.074957

International Journal of Six Sigma and Competitive Advantage, 2015 Vol.9 No.2/3/4, pp.109 - 125

Received: 30 Sep 2014
Accepted: 28 Apr 2015
Published online: 27 Feb 2016 *

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