Fuzzy optimization of the automotive ammonia fuel cycle

被引:19
|
作者
Angeles, Donna A. [1 ]
Tan, Raymond R. [1 ]
Aviso, Kathleen B. [1 ]
Are, Kristian Ray Angelo G. [1 ]
Razon, Luis F. [1 ]
机构
[1] De La Salle Univ, Dept Chem Engn, 2401 Taft Ave, Manila 0922, Philippines
关键词
Ammonia; Fuel; Life-cycle optimization; Carbon footprint; Nitrogen footprint; SENSITIVITY-ANALYSIS; IGNITION ENGINE; VALUE CHAIN; DESIGN; ENERGY; CARBON; BIODIESEL; BIOMASS; SYSTEMS;
D O I
10.1016/j.jclepro.2018.03.143
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ammonia has favorable properties for use in automotive fuel applications, such as good storage properties and mature production and distribution infrastructure. However, the sustainability of ammonia as an alternative automotive fuel remains in question, due to the significant environmental impact of conventional production technology and the need for a secondary hydrocarbon fuel to promote combustion when used in internal combustion engines. The two commercially implemented processes for ammonia: steam reforming and partial oxidation, a wood-based syngas process and a cyanobacterial process combined are considered in the life cycle optimization of the ammonia-based fuel system. It is assumed that the functional unit is 1 km travelled by a representative light-duty internal combustion engine vehicle. Two conventional fuels are also considered as secondary fuel in this study, namely, gasoline and diesel. Fuzzy linear programming is applied using carbon and nitrogen footprint as environmental objectives. The cyanobacteria-based process combined with gasoline as the secondary fuel was identified as the optimal solution. The most significant parameter was the end-user vehicle fuel economy. Hence, more attention must be given to the improvement of vehicle technology to enable the sustainable use of ammonia for transportation. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:877 / 882
页数:6
相关论文
共 50 条
  • [1] Optimization of the Automotive Ammonia Fuel Cycle Using P-Graphs
    Angeles, Donna A.
    Are, Kristian Ray Angelo G.
    Aviso, Kathleen B.
    Tan, Raymond R.
    Razon, Luis F.
    ACS SUSTAINABLE CHEMISTRY & ENGINEERING, 2017, 5 (09): : 8277 - 8283
  • [2] AMMONIA AS A GREEN FUEL FOR TRANSPORTATION
    Zamfirescu, Calin
    Dincer, Ibrahim
    ES2008: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY - 2008, VOL 1, 2009, : 507 - 515
  • [3] Noise optimization of a regenerative automotive fuel pump
    Wang, J. F.
    Feng, H. H.
    Mou, X. L.
    Huang, Y. X.
    2016 SECOND INTERNATIONAL CONFERENCE ON MECHANICAL AND AERONAUTICAL ENGINEERING (ICMAE 2016), 2017, 187
  • [4] Fractional Order Fuzzy PID Control of Automotive PEM Fuel Cell Air Feed System Using Neural Network Optimization Algorithm
    AbouOmar, Mahmoud S.
    Zhang, Hua-Jun
    Su, Yi-Xin
    ENERGIES, 2019, 12 (08)
  • [5] Environmental Life Cycle Assessment of Ammonia-Based Electricity
    Boero, Andrea J.
    Kardux, Kevin
    Kovaleva, Marina
    Salas, Daniel A.
    Mooijer, Jacco
    Mashruk, Syed
    Townsend, Michael
    Rouwenhorst, Kevin
    Valera-Medina, Agustin
    Ramirez, Angel D.
    ENERGIES, 2021, 14 (20)
  • [6] Life cycle assessment of ammonia utilization in city transportation and power generation
    Bicer, Yusuf
    Dincer, Ibrahim
    JOURNAL OF CLEANER PRODUCTION, 2018, 170 : 1594 - 1601
  • [7] Ammonia as a suitable fuel for fuel cells
    Lan, Rong
    Tao, Shanwen
    FRONTIERS IN ENERGY RESEARCH, 2014,
  • [8] Two Optimization Approaches for a Small-Scale Power-to-Ammonia Cycle
    Koschwitz, Pascal
    Ross, Leon
    Epple, Bernd
    CHEMIE INGENIEUR TECHNIK, 2025, 97 (1-2) : 71 - 82
  • [9] Life cycle assessment of the solid oxide fuel cell vehicles using ammonia fuel
    Liao, Chengfeng
    Tang, Yuting
    Liu, Yuchen
    Sun, Ziwei
    Li, Weijie
    Ma, Xiaoqian
    JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2023, 11 (05):
  • [10] Machine learning-based metaheuristic optimization of an integrated biomass gasification cycle for fuel and cooling production
    Li, Xuhao
    Zhong, Kunyu
    Feng, Li
    FUEL, 2023, 332