Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Nidlom Muddin
PT. Pertamina (Persero)
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler Shafanda Nabil Sembodo; Nazrul Effendy; Kenny Dwiantoro; Nidlom Muddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3044-3050

Abstract

The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.
The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest Nazrul Effendy; Eko David Kurniawan; Kenny Dwiantoro; Agus Arif; Nidlom Muddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp923-929

Abstract

The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.
Co-Authors Agus Arif Eko David Kurniawan Kenny Dwiantoro Nazrul Effendy Shafanda Nabil Sembodo