It has been well recognized that driving behaviors significantly impact fuel consumption of vehicles. In this paper, we propose a FuelNet model based on Long Short-term Memory Neural Network (LSTM NN), which can predict vehicle fuel consumption in a very accurate manner. First, we take the kinetic vehicle parameters and the corresponding fuel consumption parameters to build the FuelNet model, and analyze the correlations between the prediction accuracy and different combinations of input parameters. In addition, our model exhibits the superior capability for fuel consumption prediction (FCP) at different speed, and the comparison with different deep learning models as well as other physics model and data-driven methods suggests that FuelNet can achieve the best prediction performance in terms of both accuracy and stability. Finally, the application of FCP in distinct driving trajectories and abnormal fuel consumption detection performs well, which demonstrates the FuelNet also can provide guidance for eco-driving strategies.
The boiler thermal efficiency test requires strict operating conditions. Numerical simulation, which is considered as an economic and effective method to study the boiler combustion process, was adopted on a WNS2-1.25-Q gas-fired boiler. The combustion characteristics and boiler thermal efficiency were investigated. The simulation results are reliable, compared with thermodynamic calculation values. The results show that it is feasible to calculate boiler thermal efficiency by numerical simulation. This study can provide a new method for boiler efficiency calculation, which is beneficial to economics.
Exploiting the renewable energy rather than fossilfuels has become increasingly important. Among all types of renewable energy, geothermal energy is an ideal choice . It can be used for heating and generating electricity and has advantages over wind and solar power [2, 3]. Closed-loop technology can be used to effectively extract heat from the underground formation and can avoid problems of corrosion, scaling in the wellbore, and mass flowrate loss in the reservoir . Rybach and Hopkirk, firstly proposed the concept of using coaxial heat exchanger to provide heat for buildings in 1995 . Alimonti et al. once set up a numerical model of a coaxial heat exchanger to maximize the extracted heat on Villafortuna Trecate oilfield . Bu et al. made a study on acquiring geothermal energy from an abandoned oil and gas well using coaxial heat exchanger and verified its feasibility . Noorollahi et al. carried out a numerical simulation of two vertical closed-loop heat extraction models for two oil wells in southern Iran .
However, few studies investigated the performance of closed-loop heat extraction from deviated-geothermal wells. In this paper, Closed-loop heat extraction models for a vertical geothermal well (Model A), a deviated geothermal well (Model B) and symmetrically arranged two deviated-geothermal wells (Model C) have been established. Their heat extraction performances were compared with respect to different inflow parameters.
Combined cooling, heating and power (CCHP) is one of effective approach to enhance the energy conversion performances, in order to enhance the effective utilization of the waste heat, a thermochemical recuperation based CCHP production method is evaluated by employing methanol decomposition, and two power scenarios are considered of gas turbine (GT) and internal combustion engine (ICE). The newly developed CCHP system are mathematically modeled, the thermodynamic characteristics are evaluated and compared. The results indicate that a considerable high-temperature waste heat realize favorable recovery and produce high-quality syngas, the power efficiency is improved by 7.48%-13.7%, and the adjustment range on cooling/heating-to-power ratio can be evidently extended. Both the improved energy conversion efficiency and operation flexibility are achieved, and provides an alternative way to enhance the waste heat recovery and the CCHP production.
Designing waterfront redevelopment generally focuses on amenity, hobby and beauty, resulting in various types of building and block shapes. However, increasing climate change impacts necessitates these buildings to be sustainable, resilient, and zero CO2 emissions. Here, we investigate how building morphology affects energy consumption and PV generation in the context of Shinagawa, Tokyo at waterfront for possible redevelopments. For our analyses, we utilized â€˜Rhinoceros 3Dâ€™ and its plugin â€˜Grasshopperâ€™, which is a commonly used architecture program applicable to building energy analysis. It is found that among considered scenarios high-rise buildings had the least energy demand and CO2 emission, emphasizing that building morphology is one of the critical factors, leading to low CO2 emission buildings.
The thermal degradation kinetics and flammability of three forest fuels (pine needle, pine bark, and pine branch) were studied by thermogravimetric analysis (TGA). Friedman method and Flynn-Wall-Ozawa method were used to determine the conversion dependence of apparent activation energy (E) for degradation of the fuels. The flammability of the three forest fuels were evaluated by the TGA results combined with the distribution of activation energies. Results indicate that pine needle is easier to release volatiles than pine branch and pine bark when heated. Pine branch involves the highest combustibility and pine bark shows the highest sustainability in fires.
Given the increasing role of the tourism industry in tackling climate change and air pollution, this study attempts to develop an analysis framework to investigate the effects of promoting the green and sustainable development of tourism from the perspective of developing distributed solar PV. Three typical innovation development patterns of promoting the use of distributed PV while new tourism economic growth point also has been explored. A typical tourism and distributed PV co-development project in Shenzhen which is a typical low-carbon development city is took as an example to demonstrate the application of the framework and related methods proposed to assess the benefit. The results indicate that the energy replacement effect on high-carbon coal and emission reduction effect on CAPs and GHG of this typical case is significant. On average, it results a significant reduction in coal consumption by approximately 22 thousand tonne of coal equivalent (tce), in GHG emissions by 62.9 thousand tonnes and in CAP emissions by 0.9 thousand tonnes, respectively. As the co-development of tourism and distributed PV, the effect becomes much bigger. Based on a scenario analysis, due to the use of distributed PV, the tourism industry in Shenzhen will contribute significant reductions in coal consumption, CAP and GHG emissions, ranging from 0.8 to 3.8 million tce, ranging between 0.03 and 0.2 million tonnes and in a range from 2.2 to 10.9 million tonnes, respectively.
A novel hybrid solarâ€“windâ€“bioethanol hydrogen generation system via membrane reactor was proposed in this study. The thermodynamic and economic analyses were conducted at different bioethanol reforming working conditions under the actual weather situation of a year in DunHuang based on numerical simulation. The energy efficiency and standard coal saving rate (SCSR) from 300 Â°C to 500 Â°C (reaction temperature) and 0.005 bar to 0.25 bar (separation pressure) were studied. Under the design condition, the energy efficiency and SCSR can reach 52.55% and 1760.94 kg/year at 500 Â°C, 0.1 bar. Hydrogen generation rate and levelized cost under different photovoltaic area were researched and analyzed. The results showed that the hydrogen generation rate and levelized cost can be 394.15 kg/year and 4.56 $/kg. This study shows the feasibility of bioethanol reforming hydrogen production driven by renewable energy via membrane reactor.
The design optimisation of a hybrid Stirling-organic Rankine cycle driven micro-CCHP utilising biomass fuel and exhaust waste heat to produce power, cooling and heating is presented. Four objectives have been formulated from thermodynamic and economic points of view to optimise the design of the system including the energy utilisation efficiency, exergy efficiency, primary energy savings and artificial thermal efficiency of the system. While the cooling ratio and frequency of the Stirling engine prime mover have been selected as the decision variables. The non-dominated sorting genetic algorithm II (NSGA-II) has been deployed to solve the optimisation problem and produce a Pareto frontier of the optimal solutions. Further, using the TOPSIS approach, the optimal design parameters have been selected from the Pareto set. The study constitutes the first attempt to holistically optimise such a hybrid micro-CCHP in a robust manner. The results of the study optimise the design of the proposed system and this design will be used as basis, in the future, to carry out a dynamic simulation of a scaled-up case study.
Under optimal conditions, the design parameters are found to be frequency and cooling ratio of 29.11 Hz and 0.238, respectively and the performance indicators; energy utilisation efficiency, exergy efficiency, primary energy savings and artificial thermal efficiency are 0.85, 0.57, 0.51 and 0.62, respectively. The optimum SE-ORC based micro-CCHP system will produce 3.2 times more heating than cooling.
Ideally, primary data collection is recommended for every life cycle assessment (LCA) study. However, due to limited availability or accessibility to first-hand data, related sources of secondary data can be a good alternative in practice. In this work, the uncertainty of using secondary data from the Ecoinvent Life Cycle Inventory (LCI) database is illustrated with an LCA case study on global air travel. Inside the database, both parametersâ€™ basic uncertainty from measurements and additional uncertainty from data quality criteria are considered with the pedigree approach. The effect of updated pedigree matrix coefficients is also evaluated. Furthermore, the sensitivity with respect to the choice of system boundary is studied with a hotspot analysis for air travel. Outside the database, the uncertainty associated with mapping real world processes to those available in the database is analyzed. In particular, the influence of flight specific parameters, e.g. plane type and occupancy level, is assessed by comparing the International Civil Aviation Organizations (ICAO) carbon emissions calculator with database calculations. The results show that emissions calculated by ICAO generally lie on the lower end of confidence intervals provided by uncertainty analysis of the database, especially for very long-haul flights. Finally, for the LCA case study on air travel, a two-step method combining the advantages of both the ICAO calculator and the Ecoinvent database is proposed.