TY - GEN
T1 - Characterization of Speed Based on Fuel Input Ratio on Generator Set Dual Fuel (Gasoline - Biogas) Using Artificial Neural Networks
AU - Abdurrakhman, Aroef
AU - Kurniawan, Dhirga
AU - Toriki, Mohammad Berel
AU - Hadi, Herry Sufyan
AU - Patrialova, Sefi Novendra
AU - Nugroho, Dwi Oktavianto W.
AU - Soehartanto, Totok
AU - Widjiantoro, Bambang Lelono
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Energy consumption in Indonesia has increased, it makes the role of renewable energy more developed with one of the renewable energy sources being intensified is biogas, especially for household scale. The gasoline-biogas dual fuel generator set saves the use of gasoline as fuel and reduces production costs. The gasoline-biogas mixture ratio affects engine performance, one of which is the rotational speed, for this reason it is necessary to have an artificial neural network software to find the best ratio in order to get the generator set rotational speed characterization with the best engine performance value. A total of 300 variations of data were processed using 75% ANN for training with the number of hidden nodes 100 net.trainParam.goal value = 0.0001, net.trainParam.lr = 0.01, and net.trainParam.epochs = 1000, and 25% for the test, producing values RMSE training 10.4812 at node 55 and RMSE value of test 5.8301 with the results of 3445.87 rotational speed get the best ratio at 0.012 L / minute gasoline and Biogas 5 L / minute.
AB - Energy consumption in Indonesia has increased, it makes the role of renewable energy more developed with one of the renewable energy sources being intensified is biogas, especially for household scale. The gasoline-biogas dual fuel generator set saves the use of gasoline as fuel and reduces production costs. The gasoline-biogas mixture ratio affects engine performance, one of which is the rotational speed, for this reason it is necessary to have an artificial neural network software to find the best ratio in order to get the generator set rotational speed characterization with the best engine performance value. A total of 300 variations of data were processed using 75% ANN for training with the number of hidden nodes 100 net.trainParam.goal value = 0.0001, net.trainParam.lr = 0.01, and net.trainParam.epochs = 1000, and 25% for the test, producing values RMSE training 10.4812 at node 55 and RMSE value of test 5.8301 with the results of 3445.87 rotational speed get the best ratio at 0.012 L / minute gasoline and Biogas 5 L / minute.
KW - Artificial Neural Network
KW - Gasoline-Biogas
UR - http://www.scopus.com/inward/record.url?scp=85095803913&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA47173.2019.9223390
DO - 10.1109/ICAMIMIA47173.2019.9223390
M3 - Conference contribution
AN - SCOPUS:85095803913
T3 - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding
SP - 215
EP - 219
BT - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019
Y2 - 9 October 2019 through 10 October 2019
ER -
