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Scientometric Analysis of Biofilm Research in Microbial Fuel Cells: Insights into Key Research Areas and Emerging Trends Christwardana, Marcelinus; Suedy, Sri Widodo Agung; Harmoko, Udi
Eksergi Vol 21, No 2 (2024)
Publisher : Prodi Teknik Kimia, Fakultas Teknologi Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v21i2.11960

A scientometric investigation mapped the literature on biofilm development in Microbial Fuel Cells (MFCs), revealing promising renewable energy prospects and waste treatment solutions. The analysis encompassed 16898 sources, predominantly research articles (12571), along with review papers, conference papers, books, and other publications. Network analysis highlighted key research clusters and subtopics, including biofilm characterization, electrode optimization, and monitoring/control technologies. Insights from biofilm research have led to innovative approaches like biofilm engineering and advanced analytical techniques, enhancing real-world applications. Integration of MFCs into sustainable development underscores biofilms' potential as eco-friendly and economically viable components of energy production systems.

Use of Machine Learning-Based Health Index With K-Nearest Neighbors Method to Maintain Desalination Plant Performance Gas and Steam Power Plants Applications Harmoko, Udi; Christwardana, Marcelinus; Rizkan, Muhammad
Asian Journal of Social and Humanities Vol. 3 No. 7 (2025): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v3i7.549

This study presents the implementation of a Machine Learning-Based Health Index utilizing the K-Nearest Neighbors (K-NN) algorithm for predictive maintenance in desalination plants within gas and steam power plants. The research focuses on optimizing the maintenance schedule of the Block 3 Priok Desalination Plant, which is critical for providing high-quality distilled water for power generation. This study aims to develop and integrate a predictive maintenance framework into PLN’s digitization system, allowing for automated monitoring and optimized servicing schedules. Unlike the previous application of K-NN in Block 4, which utilized five health indices for performance classification, Block 3 requires an expanded model incorporating at least seven input parameters due to its multi-effect desalination process. By refining the predictive model and increasing data parameterization, this study seeks to enhance maintenance accuracy, minimize operational downtime, and improve overall desalination efficiency. By leveraging historical operational data and real-time monitoring, the K-NN model predicts the health index of desalination components with 98% accuracy. Implementing this approach minimizes downtime, optimizes maintenance schedules, and enhances energy efficiency. The results demonstrate that AI-driven predictive maintenance significantly improves reliability, reduces costs, and supports energy sustainability goals.

PEMBANGKIT PANAS BUMI KAPASITAS 2 KW MEMANFAATKAN SISTEM ENTHALPI RENDAH UNTUK APLIKASI SISTEM OFF-GRID Herlambang, Yusuf Dewantoro; Harmoko, Udi; Widodo, Achmad; Yulianto, Gatot; Widada, Sugeng; Sahid, Sahid
Eksergi Vol. 17 No. 1 (2021): JANUARI 2021
Publisher : Politeknik Negeri Semarang

PLTP siklus biner merupakan salah satu solusi alternatif pemanfaatan panas bumi yang memanfaatkan sistem entalpi rendah. Skala percobaan pembangkit listrik siklus biner bertujuan untuk mengembangkan kemungkinan penggunaan tidak langsung dari sistem panas bumi entalpi rendah di situs Diwak. Unit ini adalah sistem siklus biner 2 kW yang terdiri dari empat komponen utama: evaporator, turbin, kondensor, dan sistem pendingin. Refrigeran R134a dipilih sebagai fluida biner karena kinerja yang diinginkan dan kemudahan ketersediaan di area lokal. Panas ditransfer ke fluida biner di sistem evaporator dengan loop ekstraksi termal. Pendinginan disuplai ke pipa ganda kondensor kemudian diedarkan sebagai loop pada sistem menara pendingin. Turbin putar digunakan sebagai ekspander untuk mengekstraksi pekerjaan dari sistem dan diumpankan oleh evaporator bertekanan tinggi. Sistem telah dirancang dan dibangun dan sekarang siap untuk komisioning dan pengujian.

Implementation of Town and School Watching for Disaster Education to the Communities in Sidomulyo Village, Pronojiwo, Lumajang Maryanto, Sukir; Setyowati, Alvira Galihputri; Aprilla, Arga Nanda; Sari, Rendi Pradila Hab; Ramadhani, Naufal Helmi; Tolle, Herman; Nurjannah, Nurjannah; Sujarwo, Sujarwo; Harmoko, Udi
International Journal of Disaster Management Vol 5, No 2 (2022)
Publisher : TDMRC, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/ijdm.v5i2.29175

The eruption of Mount Semeru on December 4, 2021, caused a fairly high death toll of up to 50 people. The high number of fatalities is caused by the lack of mitigation capacity and the absence of community involvement in mitigation efforts. Increased mitigation capacity can be done by presenting disaster education to the communities. Thus, we applied the concepts of school watching and town watching in Pronojiwo District, Lumajang Regency. School watching was implemented in eight schools located at Sidomulyo Village. We also carried out town watching in Sidomulyo Village and brought out 33 residents to the Sidomulyo village hall. The implementation of school watching and town watching in Pronojiwo District can increase community involvement in disaster mitigation efforts and public awareness of mitigation efforts.

Use of Machine Learning-Based Health Index With K-Nearest Neighbors Method to Maintain Desalination Plant Performance Gas and Steam Power Plants Applications Harmoko, Udi; Christwardana, Marcelinus; Rizkan, Muhammad
Asian Journal of Social and Humanities Vol. 3 No. 7 (2025): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v3i7.549

This study presents the implementation of a Machine Learning-Based Health Index utilizing the K-Nearest Neighbors (K-NN) algorithm for predictive maintenance in desalination plants within gas and steam power plants. The research focuses on optimizing the maintenance schedule of the Block 3 Priok Desalination Plant, which is critical for providing high-quality distilled water for power generation. This study aims to develop and integrate a predictive maintenance framework into PLN’s digitization system, allowing for automated monitoring and optimized servicing schedules. Unlike the previous application of K-NN in Block 4, which utilized five health indices for performance classification, Block 3 requires an expanded model incorporating at least seven input parameters due to its multi-effect desalination process. By refining the predictive model and increasing data parameterization, this study seeks to enhance maintenance accuracy, minimize operational downtime, and improve overall desalination efficiency. By leveraging historical operational data and real-time monitoring, the K-NN model predicts the health index of desalination components with 98% accuracy. Implementing this approach minimizes downtime, optimizes maintenance schedules, and enhances energy efficiency. The results demonstrate that AI-driven predictive maintenance significantly improves reliability, reduces costs, and supports energy sustainability goals.

Analysis of Potential Utilization of Sarulla Geothermal Combined Cycle Residual Fluids for Direct Use in The Coffee Industry Harefa, Jonius Christian; Hadiyanto; Harmoko, Udi
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 13 No. 2 (2022): November
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21771/jrtppi.2022.v13.no2.p37-50

The Geothermal Power Plant is one of the new renewable energy power plants. In Indonesia, the realization has reached 2%. Sarulla Operations Limited is the first geothermal power plant in Indonesia, located in North Tapanuli Regency, that utilizes combined cycle technology. Coffee is the leading commodity in the North Tapanuli district, with a plant area of 17,586 hectares. Coffee is dried in the traditional way (open field drying) so that it is still constrained by rain and cloudiness and can only be done during the day. The reinjection well fluid has a temperature of 103°C with a flow rate of 4978 t/h and a pressure of 6–14 Bar. This study analyses the residual fluid energy for coffee drying purposes. Energy and exergy calculations are done manually and using DWSIM software with a total of 24 data points 24 hours a day to represent the availability of dryers both day and night. The results showed that the most energy needed to raise the drying air temperature at night from 15°C to 60°C was 125.62 kW, while the lowest energy needed to raise the drying air temperature during the day from 30°C to 40°C was 27.92 kW. The results of research calculations show the energy potential for residual fluid from geothermal plants to be used for drying coffee for 24 hours, both day and night.

Scientometric Analysis of Biofilm Research in Microbial Fuel Cells: Insights into Key Research Areas and Emerging Trends Christwardana, Marcelinus; Suedy, Sri Widodo Agung; Harmoko, Udi
Eksergi Vol 21 No 2 (2024)
Publisher : Prodi Teknik Kimia, Fakultas Teknik Industri, UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/e.v21i2.11960

A scientometric investigation mapped the literature on biofilm development in Microbial Fuel Cells (MFCs), revealing promising renewable energy prospects and waste treatment solutions. The analysis encompassed 16898 sources, predominantly research articles (12571), along with review papers, conference papers, books, and other publications. Network analysis highlighted key research clusters and subtopics, including biofilm characterization, electrode optimization, and monitoring/control technologies. Insights from biofilm research have led to innovative approaches like biofilm engineering and advanced analytical techniques, enhancing real-world applications. Integration of MFCs into sustainable development underscores biofilms' potential as eco-friendly and economically viable components of energy production systems.

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