An experimental and ANN analysis of ammonia energy integration in biofuel powered low-temperature combustion engine to enhance cleaner combustion

被引:2
|
作者
Ramalingam, Krishnamoorthy [1 ,2 ]
Vellaiyan, Suresh [3 ]
Gupta, M. Satyanarayana [4 ]
Nagappan, Beemkumar [5 ]
Faujdar, Pramod Kumar [6 ]
Chandran, Davannendran [7 ]
Raviadaran, Revathi [8 ]
机构
[1] Univ Sains Malaysia, Sch Mech Engn, Engn Campus, Nibong Tebal 14300, Penang, Malaysia
[2] CK Coll Engn & Technol, Dept Mech Engn, Cuddalore, Tamilnadu, India
[3] Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept Sustainable Engn, Chennai, Tamilnadu, India
[4] MLR Inst Technol, Dept Aeronaut Engn, Hyderabad, Telangana, India
[5] JAIN, Fac Engn & Technol, Dept Mech Engn, Bengaluru, Karnataka, India
[6] Vivekananda Global Univ, Dept Mech Engn, Jaipur, Rajasthan, India
[7] Univ Teknol PETRONAS, Dept Mech Engn, Seri Iskandar 32610, Perak, Malaysia
[8] Univ Teknol PETRONAS, Inst Sustainable Energy, HICoE Ctr Biofuel & Biochem Res, Dept Chem Engn, Seri Iskandar 32610, Perak, Malaysia
关键词
RCCI engine; Ammonia fuel; Sustainable energy; Emission control; Hydrogen combustion; EMISSION CHARACTERISTICS; PERFORMANCE; PREDICTION; STRATEGY; FUEL;
D O I
10.1016/j.csite.2024.105284
中图分类号
O414.1 [热力学];
学科分类号
摘要
In the pursuit of global net-zero emissions targets and cleaner combustion technologies, researchers are increasingly turning to innovative solutions. Ammonia, as a carbon-free fuel, holds promise for achieving low emissions and efficient combustion. This study explores the potential synergies of various ammonia shares (20 %, 40 %, and 60 %) combined with Citronella biofuel in a Low-Temperature Combustion (LTC) Reactivity Controlled Compression Ignition (RCCI) engine. Comparative analysis between a 40 % ammonia blend Citronella biofuel (A40) and a Standard Diesel (SDL) in an RCCI engine demonstrates significant reductions in oxides of nitrogen (by 17 %), carbon dioxide (by 6.6 %), and smoke (by 9.8 %), along with a notable enhancement in Brake Thermal Efficiency (BTE) by 5.4 % for A40. The validation of results using an Artificial Neural Network (ANN) model shows robust coefficients of determination (97 %), high R values (0.9945-0.9995), and low Root Mean Square Error values (ranging from 0.0219 to 0.0483). These findings strongly support the efficacy of the A40 blend, positioning it as a promising and viable alternative fuel for achieving cleaner combustion in LTC-RCCI engines.
引用
收藏
页数:15
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