Optimization of energy production from biogas fuel in a closed landfill using artificial neural networks: A case study of Al Ghabawi Landfill, Jordan

被引:24
作者
Alrbai, Mohammad [1 ]
Abubaker, Ahmad M. [2 ]
Ahmad, Adnan Darwish [3 ]
Al-Dahidi, Sameer [4 ]
Ayadi, Osama [1 ]
Hjouj, Dirar [5 ]
Al-Ghussain, Loiy [6 ]
机构
[1] Univ Jordan, Sch Engn, Dept Mech Engn, Amman 11942, Jordan
[2] Villanova Univ, Mech Engn Dept, Villanova, PA 19085 USA
[3] Univ Kentucky, Inst Res Technol Dev IR4TD, Lexington, KY 40506 USA
[4] German Jordanian Univ, Mech & Maintenance Engn Dept, Amman 11180, Jordan
[5] Greater Amman Municipal, Amman 11118, Jordan
[6] Univ Kentucky, Mech Engn Dept, Lexington, KY 40506 USA
关键词
Combustion parameters; ANN; LFG; Power generation; Renewable energy; MUNICIPAL SOLID-WASTE; GAS; MODEL; PERFORMANCE; FRACTION;
D O I
10.1016/j.wasman.2022.07.011
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Landfills have high potency as renewable energy sources by producing biogas from organic waste degradation. Landfills biogas (LFG) can be used for power plant purposes instead of allowing it to flare to the atmosphere which contributes to the global warming. The aim of this work was to introduce and examine an optimization model for maximizing the power generation of Al Ghabawi landfill in Amman city, Jordan. The optimization process focused on studying the effect of several operating parameters within the landfill power plant. To achieve this goal, a combustion model had been built and validated against a set of historical real data obtained from the landfill operator. In addition to that, an Artificial Neural Network (ANN) model had been built to perform a multi-objective optimization to obtain the optimal power generation conditions for Al Ghabawi landfill. The combustion model along with the ANN model aim to estimate the best engine operating conditions based on the actual daily data of the landfill. The engine operating parameters includes the intake pressure and temperature, the ignition time and the equivalence ratio. The results of the study indicate that the current operating parameters can be optimized to maximize the gensets power generation. Based on the daily data of the produced LFG, the optimal operating conditions for the landfill are 2.32 bar for the intake pressure, 303 K for the intake temperature, 0.9-1.0 for the equiveillance ratio and for the ignition time it is 13 degrees before the top dead center (BTDC). These optimized operating parameters can maximize the landfill power generation by at least 1 MW for each genset.
引用
收藏
页码:218 / 226
页数:9
相关论文
共 32 条
[1]   Techno-economic feasibility of thermal storage systems for the transition to 100% renewable grids [J].
Al-Ghussain, Loiy ;
Ahmad, Adnan Darwish ;
Abubaker, Ahmad M. ;
Hassan, Muhammed A. .
RENEWABLE ENERGY, 2022, 189 :800-812
[2]   Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks [J].
Almonacid, F. ;
Rus, C. ;
Perez-Higueras, P. ;
Hontoria, L. .
ENERGY, 2011, 36 (01) :375-384
[3]   Investigation of the main exhaust emissions of HCCI engine using a newly proposed chemical reaction mechanism for biogas fuel [J].
Alrbai, Mohammad ;
Al-Dahidi, Sameer ;
Abusorra, Mosa .
CASE STUDIES IN THERMAL ENGINEERING, 2021, 26
[4]   Multi Cycle Modeling, Simulating and Controlling of a Free Piston Engine with Electrical Generator under HCCI Combustion Conditions [J].
Alrbai, Mohammad ;
Robinson, Matthew ;
Clark, Nigel .
COMBUSTION SCIENCE AND TECHNOLOGY, 2020, 192 (10) :1825-1849
[5]   Artificial neural network analysis of heat pumps using refrigerant mixtures [J].
Arcaklioglu, E ;
Erisen, A ;
Yilmaz, R .
ENERGY CONVERSION AND MANAGEMENT, 2004, 45 (11-12) :1917-1929
[6]   Economic and environmental assessment of electricity generation using biogas from organic fraction of municipal solid waste for the city of Ibadan, Nigeria [J].
Ayodele, T. R. ;
Ogunjuyigbe, A. S. O. ;
Alao, M. A. .
JOURNAL OF CLEANER PRODUCTION, 2018, 203 :718-735
[7]   New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks [J].
Bechtler, H ;
Browne, MW ;
Bansal, PK ;
Kecman, V .
APPLIED THERMAL ENGINEERING, 2001, 21 (09) :941-953
[8]   Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste [J].
Behera, Shishir Kumar ;
Meher, Saroj Kumar ;
Park, Hung-Suck .
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2015, 17 (02) :443-453
[9]   The statistical modeling of potential biogas production capacity from solid waste disposal sites in Turkey [J].
Can, Ali .
JOURNAL OF CLEANER PRODUCTION, 2020, 243
[10]   Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation [J].
Fallah, Bahareh ;
Ng, Kelvin Tsun Wai ;
Hoang Lan Vu ;
Torabi, Farshid .
WASTE MANAGEMENT, 2020, 116 :66-78