Design and Thermodynamic Modeling of a Recuperative ORC Binary System for Double Flasher Geothermal Power Plant

2026-05-26T07:29:51+07:00

This research evaluates the thermodynamic performance of a recuperative binary Organic Rankine Cycle (ORC) integrated with a double-flash geothermal power plant, modeled using Aspen HYSYS for medium-enthalpy resources (200°C) common in Indonesia, such as Lahendong or Wayang Windu fields. Configurations compared include standalone double-flash (baseline), double-flash + simple ORC, and double-flash + recuperative ORC, with n-pentane selected as the optimal working fluid for its superior net power output, thermal efficiency, and low specific investment cost among alternatives like isopentane and isobutane. Fixed geothermal brine inputs (180°C, 15 bar, ~144-146 kg/s) simulate real Indonesian conditions. Results demonstrate the recuperative design's superiority: ORC efficiency rises by ~2% (13.71% vs. 11.71%), overall plant efficiency by 3.26% (21.5% vs. 18.24%), and net power output by 168% (5229 kW vs. 1514.6 kW baseline), driven by recuperator-enabled heat recovery that cuts evaporator duty by 15-25% and condenser rejection. Geothermal segment efficiency improves to 5.62% from 4.55%. Recuperator design features shell-and-tube units (e.g., 5181 mm tube length, 2100 mm shell diameter, U=65 W/m²K, area ~1889 m²) with minimal pressure drops. Findings align with global benchmarks (e.g., 3-4% efficiency gains, 35% power boosts) and support Indonesia's 40 GW geothermal potential via modular ORC for brine utilization. Recommendations emphasize economic optimization and scaling for Lahendong expansions.

Deteksi Dini Kebocoran Refrigeran Menggunakan Sensor Murah dan Klasifikasi Machine Learning

2026-06-30T13:55:38+07:00

Refrigerant leaks are one of the main causes of decreased cooling system efficiency and increased greenhouse gas emissions. This study aims to develop a low-cost sensor-based refrigerant leak early detection system and a machine learning classification algorithm to improve diagnostic accuracy in small-scale cooling systems. Data were obtained through pressure, temperature, electric current, and humidity measurements using analog-digital sensors such as MQ-135, DS18B20, and ACS712. The machine learning model was tested with the K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM) algorithms to classify normal system conditions, light leaks, and heavy leaks. The test results showed that the Random Forest model provided the highest accuracy of 96.7%, with a detection response time of <2 seconds. This system has proven to be efficient and economical, potentially applicable to household and small industrial cooling systems.

Mesin

Design and Thermodynamic Modeling of a Recuperative ORC Binary System for Double Flasher Geothermal Power Plant

Deteksi Dini Kebocoran Refrigeran Menggunakan Sensor Murah dan Klasifikasi Machine Learning