Thermodynamic analysis of lithium bromide absorption chiller driven by geothermal energy
The use of geothermal energy as a heat source to drive vapor absorption chillers has been receiving more interest. Geothermal energy is sustainable, economically viable and environmentally friendly. Absorption chillers powered by geothermal energy are still in their development phase. In this work, a lithium bromide absorption chiller operated with a geothermal heat source will be thermodynamically analyzed. The focus will be on the solution heat exchanger performance, in particular, the effect of geothermal heat source fluid temperature as well as mass flow rate on the absorption chiller coefficient of performance (COP). The effectiveness of the solution heat exchanger will be also studied and evaluated. Preliminary results showed that as the pressure in geothermal water vapor separator increased from 0.23 to 0.45 MPa, the absorption chiller cooling load increased by 28% while the COP decreased by only less than 1.4% at the same condenser pressure.
An artificial intelligence approach for thermodynamic modeling of geothermal based-organic Rankine cycle equipped with solar system
Abstract Geothermal energy is a renewable resource that is constantly available. The low geothermal well operating lifetime is the main challenge in using this type of renewable energy. This problem can be covered by the aid of solar system (hybrid system). For complicated renewable energy systems, finding the optimum design parameters and operating conditions require to develop experimental apparatus or sophisticated thermodynamic models. Hence, in this study, artificial intelligence (AI) approach is proposed for modeling the geothermal organic Rankin cycle (GORC) equipped with solar thermal unit. Indeed, the current study depicts how AI methods can meticulously simulate the operation of a complicated renewable energy system. The developed intelligent methods are adaptive neuro-fuzzy inference system (ANFIS) optimized with particle swarm optimization (PSO) algorithm (ANFIS-PSO) and multilayer perceptron (MLP) neural network optimized with PSO algorithm (MLP-PSO). The models are composed based on the main design parameters of the geothermal system that are solar radiation, well temperature, working fluid mass flow rate, turbine output pressure, surface area of the solar collector and preheater inlet pressure. The intelligent models use the mentioned input variables to predict the net power output, energy efficiency, exergy efficiency and levelized cost of energy (LCOE) of the GORC. Energy, exergy and economic analyses are carried out for the low global warming potential (GWP) refrigerants. It was found out that although the intelligent models can meticulously predict the targets, ANFIS-PSO performs better than MLP-PSO for modeling the GORC with solar system. Root mean square error of this model for prediction of power generation, energy efficiency, exergy efficiency and LCOE was 12.023 (kW), 3.587 × 10 - 4 , 3.278 × 10 - 4 and 1.332 × 10 - 4 , respectively.
Impact of hydrogen onboard storage technologies on the performance of hydrogen fuelled vehicles: A techno-economic well-to-wheel assessment
Hydrogen onboard storage technologies form an important factor in the overall performance of hydrogen fuelled transportation, both energetically and economically. Particularly, advanced storage options such as metal hydrides and carbon nanotubes are often hinted favourable to conventional, liquid and pressurized, storage options. This study assesses the technical and economic performance of four onboard storage technologies in a well-to-wheel assessment. It does so for centralized and onsite hydrogen production scenarios with gaseous pipeline and liquid truck distributed hydrogen. Energetically, centralized production and liquefaction outperforms onsite production and liquefaction by 20% on the average primary energy input, despite the need of pipeline or over the road distribution. The gaseous handling on the filling station performs better with an average driving energy use of 0.11 MJp/km compared to 0.15 MJp/km for liquid handling. The advanced onboard storage technologies perform better by 22% than the conventional storage technologies on primary energy input in the chain. The average driving cost for gaseous and liquid onboard storage are 53% above the gasoline reference cost of 21 ¥ct/km and the advanced onboard storage technologies as much as 98%, almost double the reference cost. The car expenditure for a fuel cell driven car compared to an internal combustion engine driven car is for a large part responsible for this difference. Advanced onboard storage technologies thus perform better than conventional onboard storage technologies energetically albeit to higher cost. 2007 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.