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All Journal Formosa Journal of Science and Technology (FJST) Enrichment: Journal of Multidisciplinary Research and Development
Nengkoda, Ardian
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Techno-Economic Risk Analysis Study of Co2 and Hydrogen Utilization From Refinery as Raw Material for Production of Dimethyl Ether Suriperdana, Puan Chairunnisa; Sommeng, Andy Noorsaman; Nengkoda, Ardian
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 4 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i4.409

Abstract

The existence of carbon footprint trade regulations and advances in carbon capture, utilization, and storage (CCUS) technology have led to the urgency of CCUS installations at all refineries globally. Captured CO2 can be used as a new economic opportunity by being reprocessed as a raw material for the production process. CO2 can be processed into DME through dry methane reforming, methanol synthesis, and methanol dehydration processes. The Indonesian government plans to replace LPG with DME. Thus, a process simulation using Aspen Plus was carried out to see the effectiveness of production along with an investment feasibility analysis in terms of NPV, IRR, PBP, and PI values and a probability review using Monte Carlo simulation. From the simulation on Aspen Plus, DME was produced as much as 868.04 tons/day. Furthermore, the economic parameters were calculated with a DME selling price of USD 1,300/ton and obtained an NPV value of USD 1,783,715,566.19, IRR 58.44%, PBP 2.041 years, and PI 3.675 so that the plant can be said to be feasible. From 1000 iterations carried out in the simulation, the four economic parameters show positive values so that the financial risk of the plant is relatively safe.
Model of Machine Learning for Prediction and Optimization of Oil and Gas Operating Costs in Indonesia Bagaskoro, Adhanto; Nengkoda, Ardian; Sommeng, Andy Noorsaman
Formosa Journal of Science and Technology Vol. 3 No. 6 (2024): June 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v3i6.9687

Abstract

This study leverages machine learning techniques to predict and optimize operational expenditures (OPEX) in Indonesia's oil and gas industry. By analyzing historical data from Work Plan and Budget (WP&B) reports from 2017, the research identifies key factors influencing OPEX, such as production location, oil characteristics, and development stages. The Random Forest model demonstrated the highest predictive accuracy with an R-squared value of 0.92 and Mean Squared Error (MSE) of 4.5. The findings highlight significant cost-saving opportunities, particularly in Kalimantan and Papua. These insights support strategic planning and decision-making, emphasizing the transformative potential of machine learning in enhancing operational efficiency and sustainability in the oil and gas sector.
Co-Authors Bagaskoro, Adhanto Sommeng, Andy Noorsaman Suriperdana, Puan Chairunnisa