Identification of environmental bottleneck using Bayesian Networks: a case study of an Indian pig iron manufacturing organization
Department of Chemical Engineering, National Institute of Technology, Durgapur
713209, West Bengal
* e-mail: firstname.lastname@example.org
Accepted: 22 August 2015
Environmentally conscious manufacturing has become a global attention for the iron and steel manufacturers to prevent global warming and climate change while making money. Iron and steel sector is considered as one of the most polluting sectors in the world. It is also one of the most energy intensive industries. During pig iron manufacturing, there is a number of steps that affect the environment emitting different pollutants. While some step(s) may be considered critical to damage the environment among all the steps, some pollutant(s) may be considered critical to affect the environment among all the pollutants. This paper proposes environmental bottleneck to consider critical step and critical pollutant simultaneously. Unless environmental bottleneck is improved, environmental performance of the entire manufacturing process may not improve significantly even if other processes (i.e. other than environmental bottleneck) are taken care of. Thus, environmental bottleneck must be taken care of properly by the manufacturing organization to enable environmentally conscious manufacturing. Hence, a method should be developed to identify environmental bottleneck. Current research work uses Bayesian Networks (BN) to identify environmental bottleneck. The contribution of the paper is to identify the environmental bottleneck for an Indian pig iron manufacturing organization. Results suggest that carbon monoxide (CO) emission from the blast furnace is the environmental bottleneck for the current pig iron manufacturing organization. Hence, proper precautions should be considered to control the CO emission from the blast furnace.
Key words: Environmental conscious manufacturing / Pig iron manufacturing / Environmental bottleneck / Bayesian Networks
© P. Sen, Published by EDP Sciences, 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.