KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Wiboonrat, MontriSubject: search.filters.subject.Data center

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent 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)
    Sermsuk, Maytungkorn
    ;
    Sukjai, Yanin
    ;
    Rajoo, Srithar
    ;
    Kiatkittipong, Kunlanan
    ;
    Wiboonrat, Montri
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent 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)
    Sermsuk, Maytungkorn
    ;
    Sukjai, Yanin
    ;
    Rajoo, Srithar
    ;
    Kiatkittipong, Kunlanan
    ;
    Wiboonrat, Montri
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    ENERGY LOSS MODEL FOR TIER 2 AND TIER 3 DATA CENTERS
    (2022-01-01)
    Wiboonrat, Montri
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Data center investment vs. System reliability in power distribution systems
    (2019-05-01)
    Wiboonrat, Montri
    Power failures are the major causes of data center downtime. Reliability analysis of power distribution systems (PDS) of data center evokes representing a system as collection of subsystems to characterize the reliability of each part of the whole system. Therefore, understanding of reliability at the component level is fundamental to develop accurate estimate of system reliability and prevent system failures. Moreover, realization in the component level help to estimate overall investment of each systems topology. To improve system reliability, it can perform through redundant topology, however redundant components and systems is increasing investment and complexity of data center system. Balancing between cost of downtime and cost of reliability can be differed from business to business. This research purpose optimal method analysis (OMA) to quantify the proper investment against the cost of data center downtime.