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dc.contributor.authorPoonia A.K.-
dc.contributor.authorKhanam, Shabina-
dc.identifier.citationProceedings of 2013 International Workshop on Computer Science and Engineering, WCSE 2013, (2013), 95- 98-
dc.description.abstractIn the present study, estimation of actual output parameters is carried out for a sponge iron production process by designing a Multilayer Perceptron model that uses a momentum learning algorithm. For this purpose data of temperature profile of rotary kiln are collected from typical sponge iron plant, which correlate four air inlets and twelve temperatures measured at different lengths of the kiln. Four different topologies are proposed for this data set to optimize the regression coefficients (R2). Firstly, these topologies are used to identify optimum value of output parameters based on value of R2. These values of output parameters meet the process requirements. Further, a better option is found to compute actual input parameters correspond to desired output. The analysis shows that to get desired output the input parameters are varied maximum by 38.9% in comparison to input parameters used in the industry. Copyright © 2008-2019. International Journal of Engineering and Technology. All rights reserved.-
dc.publisherInternational Journal of Engineering and Technology (IJET)-
dc.relation.ispartofProceedings of 2013 International Workshop on Computer Science and Engineering, WCSE 2013-
dc.titleSimulation of rotary kiln used in sponge iron process using ANN-
dc.typeConference Paper-
dc.affiliationPoonia, A.K., Dept. of Chemical Mechanical Engineering, National Institute of Technology, Raipur, Chhattisgarh, 492010, India-
dc.affiliationKhanam, S., Dept. of Chemical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India-
dc.identifier.conferencedetails2013 International Workshop on Computer Science and Engineering, WCSE 2013, 24-25 August 2013-
Appears in Collections:Conference Publications [CH]

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