TY - GEN
T1 - Neural network model for profile temperature of nickel kiln
AU - Purnomo, Leo Agung Arie
AU - Biyanto, Totok Ruki
N1 - Publisher Copyright:
© 2019 American Institute of Physics Inc. All rights reserved.
PY - 2019/3/29
Y1 - 2019/3/29
N2 - Reduction Kiln plays a very important role in the nickel smelting process. As multi-variable processing and nonlinear system, nickel kiln is difficult to be maintain in the best performance. While research on this area is very rare for this industry is such specific and unique. In this paper, it is proposed time-series neural network model for nickel kiln, with an approach based on Nonlinear Auto Regressive with eXogenous input (NARX). This model provides prediction of input-output data set to achieve certain operating condition, especially profile temperature alongside the kiln. Input-output data set of real plant was collected from Kiln 5 in PT Vale Indonesia for along 2018. The data set was reviewed and chosen for the neural network model. It was achieved that regression of the model is 0.999 which is proving that there is strong relationship between input and output. While mean squared error (MSE) is 16.4077, which means that the model is able to capture the characteristic of the plant and temperature of 6 points alongside the kiln, yet open for future research.
AB - Reduction Kiln plays a very important role in the nickel smelting process. As multi-variable processing and nonlinear system, nickel kiln is difficult to be maintain in the best performance. While research on this area is very rare for this industry is such specific and unique. In this paper, it is proposed time-series neural network model for nickel kiln, with an approach based on Nonlinear Auto Regressive with eXogenous input (NARX). This model provides prediction of input-output data set to achieve certain operating condition, especially profile temperature alongside the kiln. Input-output data set of real plant was collected from Kiln 5 in PT Vale Indonesia for along 2018. The data set was reviewed and chosen for the neural network model. It was achieved that regression of the model is 0.999 which is proving that there is strong relationship between input and output. While mean squared error (MSE) is 16.4077, which means that the model is able to capture the characteristic of the plant and temperature of 6 points alongside the kiln, yet open for future research.
UR - http://www.scopus.com/inward/record.url?scp=85064410555&partnerID=8YFLogxK
U2 - 10.1063/1.5095267
DO - 10.1063/1.5095267
M3 - Conference contribution
AN - SCOPUS:85064410555
T3 - AIP Conference Proceedings
BT - Advanced Industrial Technology in Engineering Physics
A2 - Hatta, Agus Muhamad
A2 - Indriawati, Katherin
A2 - Nugroho, Gunawan
A2 - Biyanto, Totok Ruki
A2 - Arifianto, Dhany
A2 - Risanti, Doty Dewi
A2 - Irawan, Sonny
PB - American Institute of Physics Inc.
T2 - 2nd Engineering Physics International Conference 2018, EPIC 2018
Y2 - 31 October 2018 through 2 November 2018
ER -