Wiboonrat, Montri
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Item type:Publication, Feasibility study of a combined system of electricity generation and cooling from liquefied natural gas to reduce the electricity cost of data centres(2022-09-01); ; Global data centre power demands would expand from 286 TWh in 2016 to around 321 TWh in 2030. The cooling system represents electricity consumption of approximately 40–50% of the total energy used. The global LNG trade has reached 356.1 MTPA. Cold energy equal to 89025 GW is released into the ocean. Therefore, this study focused on the technical and economic feasibility of an LNG receiving terminal combined with a data centre using a direct expansion cycle (DEC), a Rankine cycle (RC) and a combination of Rankine cycle and direct expansion cycle (RC + DEC) under different natural gas distribution pressures to produce a supply of cooling water and electricity to reduce electricity consumption and greenhouse gas emissions. According to the study, the RC + DEC produces the maximum cold water at 7 °C, with the total cold energy of 44.23 MW which is sufficient for cooling a data centre with a capacity of 5345 racks, to reduce the electricity for conventional cooling system is 13521 kWh and generated electricity form turbine is 9968 kWh. This research has the potential to reduce the operating costs of data centres by more than USD 23.87 million per annum as well as CO<inf>2</inf> emissions by 83859 t per annum with exergy efficiency of 78.94%. In an economic study, indicated a payback period of 1.60 years with an IRR of 62%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Feasibility of integrating small-scale liquefied natural gas (LNG) terminal with combined cycle power plant to reduce carbon emissions and costs for data centers(2025-06-01); ; Global data centers are projected to consume 2–3% of global electricity and contribute 8% of carbon emissions by 2030, driven by the rising demand for 5G. In Southeast Asia's tropical climate, cooling data centers presents a challenge, as power plant efficiency drops by 10–20% during summer, when ambient temperatures reach 35–40°C. This study introduces a novel system that integrates a small-scale liquefied natural gas (LNG) receiving terminal with a combined cycle power plant (CCPP) and a data center, designed specifically for tropical climates. The system harnesses LNG cold energy through three configurations: intermediate fluid vaporizer (IFV), Rankine cycle (RC), and direct expansion cycle (DEC), to optimize electricity generation and chilled water production. By reducing the gas turbine inlet temperature from 35°C to 22°C, the system boosts power output by 12.22% and thermal efficiency by 3.84%. Nighttime cooling supports a 3,048-rack data center, resulting in annual savings of $5.50 million and a reduction of 20,304 tons of CO₂ emissions. Switching to gas power plants during summer further increases savings to $7.75 million and cuts emissions by 29,104 tons. An economic analysis shows a payback period of 2.30 years and an internal rate of return (IRR) of 69%. This integrated approach offers an efficient, cost-effective, and environmentally sustainable solution for power generation and data center operation in hot climates. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ENERGY LOSS MODEL FOR TIER 2 AND TIER 3 DATA CENTERS(2022-01-01)Demand for energy for data centers has soared during the COVID-19 pandemic. In 2020, the demand for energy for data centers is around 1% of worldwide electricity consumption and will be 3-13% in 2030. Energy loss in data centers is the main focus of this research. An energy loss model has been constructed to investigate data center power distribution systems (PDS). The model found that the highest energy loss is from the power supply unit (PSU) of IT equipment and the uninterruptible power supply (UPS). A server upgrade to PSU 80 PLUS Titanium reduces energy loss by around 2.7 times when compared with a normal PSU. These amounts of energy loss affect long-term operatingexpenses over the course of the data center’s lifetime. Moreover, this research compares PDS of Tier II and Tier III data centers to measure energy loss. The research results demonstrate that Tier III data centers lost approximately 1.782 times more energy through PDS than Tier II data centers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Utilising cold energy from liquefied natural gas (LNG) to reduce the electricity cost of data centres(2021-10-01); ; The Office of the National Broadcasting and Telecommunications Commission has reported that, from 2014 to 2018, Thailand’s internet usage has grown six‐fold to 3.3 million terabytes per annum. This market trend highlights one of the policies of Thailand 4.0, with the aim of making Thailand a hub for information transfer in ASEAN. As a result, there will be a massive demand growth for data storage facilities in the near future. Data centres are regarded as the brain and heart of the digital industry and are essential for facilitating businesses in organising, processing, storing and disseminating large amounts of data. As the energy demand for equipment cooling contributes to over 37% of the total energy consumption, the data centres of the world’s leading companies, such as Amazon, Google, Microsoft and Facebook, are generally located in cold climate zones, such as Iceland, in order to reduce operating costs for cooling. Due to this reason, the possibility of data centres in Thailand is limited. Beneficially, PTTLNG, as the first liquified natural gas (LNG) terminal in Thailand, has processed the import, receiving, storage and regasification of LNG. The high abundance of cold energy inherently presented in LNG is normally lost to the surroundings during regasification. Presently, PTTLNG’s LNG receiving terminal utilises a heat exchanger with propane as an intermediate fluid to transfer cold energy from LNG to water. This cold energy, in the form of cold water, is then used in several projects within the LNG receiving terminal: (1) production of electricity via an organic Rankine cycle capacity of 5 MWh; (2) cooling the air inlet of gas turbine generators to increase the generator efficiency; (3) replacing refrigerant heating, ventilation and air conditioning systems within buildings; (4) development of winter plantations with precision agriculture to replace imported products. Therefore, this study focuses on the potential and future use for LNG cold energy by performing a thermodynamic and economic analysis of the use of LNG cold energy as a source to produce cold water at 7 °C, with the total cold energy of 27.77 to 34.15 MW or 7934 t to 9757 t of refrigeration depending on the target pressure of the natural gas to replace the conventional cooling system of data centres. This research has the potential to reduce the cooling operation costs of data centres by more than USD 9.87 million per annum as well as CO2 emissions by 34,772 t per annum. In an economic study, this research could lead to a payback period of 7 years with IRR 13% for the LNG receiving terminal and a payback period of 2.21 years with IRR 45% for digital companies. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Advanced conceptual design for the regasification process to minimize carbon emissions, water use, and reduce operating costs for AI data centers, airport hubs, smart cities and initial zero-emission LNG receiving terminals(2026-06-01); ; Achieving net-zero greenhouse gas emissions by 2050 requires improving energy efficiency in transitional fuels such as liquefied natural gas (LNG). In 2023, global LNG imports reached 401.5 MTPA; however, most of the 180 LNG receiving terminals worldwide still discharge large amounts of recoverable cold energy into the ocean. Meanwhile, the rapid growth of artificial intelligence (AI) data centers is driving unprecedented electricity demand, with projected global electricity consumption exceeding 3000 TWh by 2030. This study proposes an advanced LNG regasification system integrating Intermediate Fluid Vaporization (IFV), Rankine Cycle (RC), and Direct Expansion Cycle (DEC) to recover both temperature and pressure exergy. The system is evaluated for LNG terminals with capacities of 10–30 MTPA under pressure levels of 70, 30, and 6 bar across eight operational models and two infrastructure configurations. Results indicate that terminal electricity imports are reduced by 52–78 % compared with conventional open rack vaporizer (ORV) systems. Net electricity generation ranges from 12.09 to 197.91 MW, while chilled water cooling capacity reaches 272.72–1075.44 MW. Cooling electricity demand is reduced by approximately 79 %, lowering data center Power Usage Effectiveness (PUE) from 2.00 to 1.36. Water Usage Effectiveness (WUE) decreases from 1.9 to 3.0 to approximately 0.3 L/kWh, achieving an 89–93 % reduction in cooling-related water use. At 30 MTPA, carbon emissions are reduced by up to 2.99 million tons annually, with economic savings of USD 119.96–603.15 million per year and a maximum IRR of 37.7 %. These results demonstrate the significant potential of LNG cold energy integration for near-zero-emission terminal operation and sustainable data center cooling. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of an AIoT-Based Early Flash-Flood Warning System for Smart Rural Disaster Resilience(2026-06-01); This paper presents the development of an AIoT-based early flash-flood warning system to enhance disaster resilience in smart rural communities. The framework integrates multi-source hydrological sensors, AI-enabled edge–cloud computing, and a mobile alert application to provide real-time monitoring and short-term flood forecasting, and includes an intelligent hybrid model combines YOLOv10 for visual water-level detection from CCTV imagery with a long short-term memory (LSTM) network for hydrological time-series prediction. The system was deployed and evaluated at two sites in Thailand: the Ban Luang station in Chiang Mai and the Chumkho station in Chumphon. The experimental results show near-perfect detection performance by YOLOv10, with precision and mAP@0.5 exceeding 0.99 across varying water-level conditions. The LSTM model achieved high forecasting accuracy, with an R<sup>2</sup> of 0.987 at Ban Luang and 0.781 at Chumkho, reflecting site-specific hydrodynamic complexity. The results confirm that integrating AIoT-based visual sensing with data-driven forecasting significantly improves the reliability, responsiveness, and robustness of early flash-flood warning systems in rural environments.1