Open Access
Manufacturing Rev.
Volume 2, 2015
Article Number 17
Number of page(s) 7
Published online 26 October 2015
  1. L. Wang, D. Wang, J. Zhu, X. Zhao, A new model based on improved ACA and BP to predict Silicon content in hot metal, Computer Modeling and Simulation ICCMS ‘10 (2010) 364–368. [CrossRef]
  2. P. Bennett, T. Fukushima, Impact of PCI coal quality on blast furnace operations. CoalTech Pty Ltd, F-TeCon Pty Ltd. (accessed on dated 25th June, 2014).
  3. M. Kundak, L. Lazic, J. Crnko, CO2 Emissions in the steel industry, Metalurgija 48 (2009) 193–197.
  4. D.T. Allen, D.R. Shonnard, Sustainability in chemical engineering education: identifying a core body of knowledge, American Institute of Chemical Engineers (AlChE) Journal. (accessed on dated 29th May, 2014).
  5. A. Swain, Climate Change Connection, Winnipeg, Manitoba, Canada 1 (July 2006) (accessed on 5th May, 2014).
  6. A.D. Jayal, F. Badurdeen, O.W. Dillon Jr., I.S. Jawahir, Sustainable manufacturing: modeling and optimization challenges at the product, process and system levels, CIRP Journal of Manufacturing Science and Technology 2 (2010) 144–152. [CrossRef]
  7. J. Sarkis, Manufacturing strategy and environmental consciousness, Technovation 15 (1995) 79–97. [CrossRef]
  8. B. Bieda, Life cycle inventory processes of the Mittal Steel Poland (MSP) S.A. in Krakow, Poland – blast furnace pig iron production – a case study, International Journal of Life Cycle Assessment 17 (2012) 787–794. [CrossRef]
  9. P. Cavaliere, A. Perrone, Optimization of blast furnace productivity coupled with CO2 emissions reduction, Journal of Iron and Steel Research, International 85 (2014) 89–98. [CrossRef]
  10. S.S. Krishnan, V. Vunnam, P.S. Sunder, J.V. Sunil, A.M. Ramakrishnan, A study of energy efficiency in the Indian iron and steel industry and steel industry, Center for Study of Science, Technology and Policy Bangalore, India(December, 2013). (accessed on dated 26th June, 2014).
  11. J.A. Burgo, Chapter 10, The Manufacture of Pig Iron in the Blast Furnace, U.S. Steel Technical Center. (accessed on dated 24th July, 2014).
  12. Chapter 6, Iron and Steel Vision – Vision 2020, (accessed on dated 14th July, 2014).
  13. S.K. Bag, ANN based prediction of blast furnace parameters, Journal – The Institution of Engineers 68 (2007) 37–42.
  14. D.L. Doushanov, Control of pollution in the iron and steel industry, Pollution Control Technologies vol. 3. (accessed on dated 26th June, 2014).
  15. J.S. Fuglestvedt, I.S.A. Isaken, W.C. Wang, Direct and indirect global warming potential of source gases, Report 1 (1994) (accessed on dated 15th June, 2014).
  16. J.M. Reilly, K.R. Richards, Climate change damage and the trace gas index issue, Environmental and Resource Economics 3 (1993) 41–61. [CrossRef]
  17. S.M. Bernard, J.M. Samet, A. Grambsch, K.L. Ebi, I. Romieu, The potential impacts of climate variability and change on air pollution-related health effects in the United States, Environmental Health Perspectives 109 (2001) 199–209. [CrossRef]
  18. C. Baukal, Everything you need to know about NOx: controlling and minimizing pollutant emissions is critical for meeting air quality regulations, Metal Finishing 103 (2005) 18–24. [CrossRef]
  19. S. Lewis, C. Mason, J. Srna, Carbon monoxide exposure in blast furnace workers, Australian Journal of Public Health 16 (1992) 262–268. [CrossRef]
  20. J.M. Freeman, Everything you wanted to know about carbon monoxide but didn’t know who to ask. (accessed on dated 15th June, 2014).
  21. Y. Tunckaya, E. Koklukaya, Comparative performance evaluation of blast furnace flame temperature prediction using artificial intelligence and statistical methods (2013) (accessed on dated 26th June, 2014).
  22. A. Kumar, K.P. Mrunmaya, S. Maharana, S.K. Chowdhury, R. Sah, M. Kaza, A real time model for prediction of blast furnace hot metal temperature through neural network, Proceeding of the International conference on science and technology of iron making and steel making, December 16–18, 2013, CSIR-NML Jamshedpur (2013). (accessed on dated 15th June, 2014).
  23. J. Angstenberger, Blast Furnace Analysis with Neural Networks, Cybernetics and Systems 37 (2006) 509–531. [CrossRef]
  24. M. Langer, B. Vogel, Synthesis of plantwide quality prediction system for a sintering plant, 15th Triennial World Congress, Barcelona, Spain. (accessed on dated 24th July, 2014).
  25. E. Worrell, L. Price, N. Martin, Energy efficiency and carbon dioxide emissions reduction opportunities in the US iron and steel sector, Energy 26 (2001) 513–536. [CrossRef]
  26. Available and Emerging Technologies for Reducing Greenhouse Gas Emissions from the Iron and Steel Industry, Sector Policies and Programs Division, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency (September, 2012). (accessed on dated 24th July, 2014)
  27. E. Worrell, N. Martin, L. Price, Energy efficiency and carbon dioxide emissions reduction opportunities in the U.S. iron and steel sector, Environmental Energy Technologies Division, Ernest Orlando Lawrence Berkeley National Laboratory (July, 1999)
  28. Tracking Industrial Energy Efficiency and CO2 Emissions, France, International Energy Agency (IEA), (accessed on dated 16th July, 2014).
  29. I.v. Ion, F. Popescu, L. Georgescu, Prediction of the pollutants generation in natural gas/residual steel gases co-combustion, International Journal of Energy & Environment 1 (2007) 79–84.
  30. G.V. Korshikov, V.N. Titov, V.G. Mikhailov, Energy expenditures and carbon dioxide emissions at blast furnaces, Steel in Translation 43 (2013) 465–470. [CrossRef]
  31. T. Miyakawa, N. Takegawa, Y. Kondo, Removal of sulfur dioxide and formation of sulfate aerosol in Tokyo, Journal of Geophysical Research 112 (2007) 1–13. (accessed on dated 16th July, 2014). [CrossRef]
  32. W. Wang, Z. Zhao, F. Liu, S. Wang, Study of NO/NOx removal from flue gas contained fly ash and water vapor by pulsed corona discharge, Journal of Electrostatics 63 (2005) 155–164. [CrossRef]
  33. Carbon monoxide in the workplace, Industrial Accident Prevention Association 2008. (accessed on dated 16th July, 2014).
  34. G. Xiang, W. Zuliang, S. Xu, L. Zhongyang, N. Mingjiang, C. Kefa, Multi-pollutants simultaneous removals from flue gas, 11th International Conference on Electrostatic Precipitation 1 (2009) 12–18.
  35. I. Dogan, Analysis of facility location model using Bayesian Networks, Expert Systems with Applications 39 (2012) 1092–1104. [CrossRef]
  36. Y.Y. Wee, W.P. Cheah, S.C. Tan, K.K. Wee, A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map, Expert Systems with Applications 42 (2015) 468–487. [CrossRef]
  37. B.G. Marcot, Metrics for evaluating performance and uncertainty of Bayesian network models, Ecological Modelling 230 (2012) 50–62. [CrossRef]
  38. A.M. Ellison, An Introduction to bayesian inference for ecological research and environmental decision-making, Ecological Applications 6 (1996) 1036–1046. [CrossRef]
  39. J.Y. Zhu, A. Deshmukh, Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing, Engineering Applications of Artificial Intelligence 16 (2003) 91–103. [CrossRef]
  40. E. Pérez-Miñana, P.J. Krause, J. Thornton, Bayesian Networks for the management of greenhouse gas emissions in the British agricultural sector, Environmental Modelling & Software 35 (2012) 132–148. [CrossRef]
  41. K.L. Webster, J.W. McLaughlin, Application of a Bayesian belief network for assessing the vulnerability of permafrost to thaw and implications for greenhouse gas production and climate feedback, Environmental Science & Policy 38 (2014) 28–44. [CrossRef]
  42. V. Delcroix, K. Sedki, F.X. Lepoutre, A Bayesian network for recurrent multi-criteria and multi-attribute decision problems: choosing a manual wheelchair, Expert Systems with Applications 40 (2013) 2541–2551. [CrossRef]
  43. E.J.M. Lauría, P.J. Duchessi, A Bayesian Belief Network for IT implementation decision support, Decision Support Systems 42 (2006) 1573–1588. [CrossRef]
  44. D.L. Kelly, C.D. Kolstad, Bayesian learning, growth, and pollution, Journal of Economic Dynamics and Control 23 (1999) 491–518. [CrossRef]
  45. W. Wang, Application of Bayesian Network to tendency prediction of blast furnace silicon content in hot metal, Bio-Inspired Computational Intelligence and Applications, Lecture Notes in Computer Science 4688 (2007) 590–597. [CrossRef]
  46. T.A. Zheldak, V.V. Slesarev, D.O. Volovenko, Knowledge-based intellectual DSS of steel deoxidation in BOF production process, American Journal of Mining and Metallurgy 1 (2013) 7–10.
  47. P.A. Leicester, C.I. Goodier, P. Rowley, Using a Bayesian Network to evaluate the social, economic and environmental impacts of community renewable energy, Proceedings of CISBAT, Clean Technology for Smart Cities and Buildings, Lausanne, 4–6 September 2013. (accessed on dated 24th July, 2014).
  48. E. Celio, T. Koellner, A. Grêt-Regamey, Modeling land use decisions with Bayesian networks: spatially explicit analysis of driving forces on land use change, Environmental Modelling & Software 52 (2014) 222–233. [CrossRef]
  49. D.P. Ames, B.T. Neilson, D.K. Stevens, U. Lall, Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study, Journal of Hydroinformatics 7 (2005) 267–282.
  50. M. Tighe, C.A. Pollino, S.C. Wilson, Bayesian Networks as a screening tool for exposure assessment, Journal of Environmental Management 123 (2013) 68–76. [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.