This study develops hybrid renewable energy systems integrated with battery vehicles and hydrogen vehicles for application in a typical zero-energy community based on the TRNSYS platform. The load files of the community including school campus, office and residential buildings are obtained according to on-site collected energy use data and simulation data as per local surveys. Three groups of battery vehicles and hydrogen vehicles following different cruise schedules are integrated as both cruise tools and energy storage technologies. The study results find that the renewables self-consumption ratio of the zero-energy community with hydrogen vehicles is up to 94.45%, much higher than that of the battery vehicles integrated system of 75.84%. The load cover ratio of hydrogen vehicles integrated system is about 69.86%, slightly lower than that of the zero-energy community with battery vehicles of 70.21%. The lifetime net present value of the zero-energy community with battery vehicles is US$ 256.79m, smaller than that of the zero-energy community with hydrogen vehicles by 44.08%. And the net present value of the zero-energy community with battery vehicles is lower than its baseline case by about 27.54%, while the net present value of the zero-energy community with hydrogen vehicles is higher than its baseline case by 31.91%. Obvious decarbonisation potential of the zero-energy community with battery vehicles and hydrogen vehicles is achieved of about 92.71% and 75.96% respectively compared with the corresponding baseline cases. The detailed techno-economic-environmental feasibility study provides stakeholders with valuable guidance for integrating renewable supply and clean transportation in urban communities.
Staged combustion and oxy-fuel combustion are both effective technologies to control NOx emission for power plant. Besides, blending fuel is also widely used in power plants. In this paper, CHEMKIN software was used to simulate the NOx formation characteristics. The co-combustion simulation of semi-coke and bituminous coal in this paper was under oxy-fuel atmosphere and deep oxygen-staging conditions. All of these combustion conditions were considered to explore the rule and reaction path of NOx formation. The simulation results show that when the temperature of the main combustion zone is below 1400 oC, the conversion of NO to N2 is promoted, while the condition is opposite at temperature above 1400 oC. Accelerating the NH2 transforming to NH rather than HNO can promote the fuel-nitrogen transforming to N2, reducing the generation of NO. This study can provide new insight into NOx formation and reaction mechanism of boiler deep oxygen-staging oxy-fuel co-combustion.
Hydrothermal gasification is an effective and economic technology for production of combustible gases and valuable chemicals from wet wastes. In the present work, machine learning (ML), a data-driven approach, is employed to predict the composition of syngas in terms of H2, CH4, CO2, and CO). A gradient boosting regression (GBR) model with optimal hyperparameters was developed for the prediction of syngas composition with a test R2 of 0.92, 0.90, 0.95, and 0.92 for H2, CH4, CO2, and CO prediction, respectively. This ML framework provides useful model inference, to identify the correlation and causal analytics between the inputs (feedstock compositions and operational conditions of HTG) and outputs (syngas compositions) essential for our future work, and it lays a concrete foundation to devise ML-based process optimization or inverse design for experiments.
This study performs the design and performance analysis of a novel solar-borehole thermal energy storage system to supply a complete heating solution to a residential high-rise building located in Ontario, Canada. Building total heat demand is estimated based on user demand and ambient temperature, a solar-thermal collector system and a borehole thermal energy storage system (BTES) are designed to generate and store the energy. A 1 1D numerical code is developed to solve the heat transfer phenomenon in BTES and is coupled to the solar collector system. A time-dependent dynamic simulation is performed over a year with hourly weather data with a time-step of 10 minutes and the observations are recorded.