Optimizing HVAC systems for semiconductor fabrication: A data-intensive framework for energy efficiency and sustainability

被引:0
|
作者
Ni, Hsiao-Ping [1 ]
Chong, Wai Oswald [2 ]
Chou, Jui-Sheng [3 ]
机构
[1] Arizona State Univ, Sch Mfg Syst & Networks, Tempe, AZ 85287 USA
[2] Arizona State Univ, Sch Sustainable Engn & Built Environm, Del E Webb Sch Construct, Tempe, AZ USA
[3] Natl Taiwan Univ Sci & Technol, Dept Civil & Construct Engn, Taipei, Taiwan
来源
关键词
Semiconductor fabrication plants; HVAC system optimization; Energy efficiency; Data -intensive design framework; Energy consumption patterns; Regulatory codes and standards; Determinants of performance; AIR-CONDITIONING SYSTEM; COMMERCIAL BUILDINGS; CLIMATE-CHANGE; CONSUMPTION; SIMULATION; DESIGN; CONSERVATION; TECHNOLOGIES; IMPROVEMENT; OPTIMIZATION;
D O I
10.1016/j.jobe.2024.109397
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper presents a detailed comparative analysis of the performance and energy consumption of heating, ventilation, and air conditioning (HVAC) systems as standalone units and integrated systems, specifically focusing on semiconductor manufacturing facilities (SMFs). Highlighting the urgent need for HVAC system optimization to minimize energy waste and reduce greenhouse gas emissions, the study uncovers distinct energy consumption patterns across residential, commercial, and industrial sectors, emphasizing the unique needs of SMFs. It reveals the need for more current energy codes and standards in addressing the specialized energy demands of semiconductor fabrication plants (fabs), thereby advocating for a customized HVAC energy-design framework to enhance energy efficiency within these facilities. The research is divided into three main segments: (1) identifying the key factors that drive HVAC systems' energy consumption, (2) examining the factors that influence system performance, and (3) analyzing how these factors impact the optimization of HVAC systems in SMFs. By developing a framework that integrates design, engineering, and energy consumption data, the paper lays the groundwork for a dataintensive design approach tailor-made to meet the energy efficiency requirements of semiconductor fabs. The study's pivotal findings highlight the deficiencies of existing energy codes and standards for SMFs and propose a bespoke HVAC energy design framework. This strategy identifies critical energy consumption and performance factors unique to SMFs, recommending a datadriven design method for enhanced energy efficiency. This forward-thinking approach aims to significantly reduce energy waste and greenhouse gas emissions, establishing a new benchmark for sustainable practices in semiconductor manufacturing.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Optimizing Data-intensive Systems in Disaggregated Data Centers with TELEPORT
    Zhang, Qizhen
    Chen, Xinyi
    Sankhe, Sidharth
    Zheng, Zhilei
    Zhong, Ke
    Angel, Sebastian
    Chen, Ang
    Liu, Vincent
    Loo, Boon Thau
    [J]. PROCEEDINGS OF THE 2022 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA (SIGMOD '22), 2022, : 1345 - 1359
  • [2] Optimizing Distributed Data-Intensive Workflows
    Friese, Ryan D.
    Tallent, Nathan R.
    Schram, Malachi
    Halappanavar, Mahantesh
    Barker, Kevin J.
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2018, : 279 - 289
  • [3] Improving the energy efficiency of data-intensive applications running on clusters
    Liu, Weifeng
    Zhou, Jie
    Gong, Bin
    Dai, Hongjun
    Guo, Meng
    [J]. INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2020, 35 (03) : 246 - 259
  • [4] Optimizing Interactive Development of Data-Intensive Applications
    Interlandi, Matteo
    Tetali, Sai Deep
    Gulzar, Muhammad Ali
    Noor, Joseph
    Condie, Tyson
    Kim, Miryung
    Millstein, Todd
    [J]. PROCEEDINGS OF THE SEVENTH ACM SYMPOSIUM ON CLOUD COMPUTING (SOCC 2016), 2016, : 510 - 522
  • [5] A Framework for Optimizing Energy Efficiency in Data Centers
    Gizli, Volkan
    Gomez, Jorge Marx
    [J]. FROM SCIENCE TO SOCIETY: NEW TRENDS IN ENVIRONMENTAL INFORMATICS, 2018, : 275 - 282
  • [6] Improving the energy efficiency and performance of data-intensive workflows in virtualized clouds
    Xilong Qu
    Peng Xiao
    Lirong Huang
    [J]. The Journal of Supercomputing, 2018, 74 : 2935 - 2955
  • [7] Improving the energy efficiency and performance of data-intensive workflows in virtualized clouds
    Qu, Xilong
    Xiao, Peng
    Huang, Lirong
    [J]. JOURNAL OF SUPERCOMPUTING, 2018, 74 (07): : 2935 - 2955
  • [8] A Framework for Data-Intensive Computing with Cloud Bursting
    Bicer, Tekin
    Chiu, David
    Agrawal, Gagan
    [J]. 2011 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2011, : 169 - 177
  • [9] Parallel Framework for Data-Intensive Computing with XSEDE
    Subramanian, Ranjini
    Zhang, Hui
    [J]. PEARC '19: PROCEEDINGS OF THE PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING ON RISE OF THE MACHINES (LEARNING), 2019,
  • [10] A framework for the internationalization of data-intensive Web applications
    Belussi, A
    Posenato, R
    [J]. WEB ENGINEERING, PROCEEDINGS, 2004, 3140 : 478 - 482