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.
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 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.
The treatment of antibiotic filter residue (AFR) is an important issue, because of its special nature, the large output, and the difficulty of combustion. Nowadays, the AFR is generally incinerated, but dioxins are produced during the process, which makes incineration technology face challenges. There is a lack of research on the influences of co-combustion conditions on temperature field and flue gas characteristics, but it plays an important role in practical applications. The present study aimed to investigate the influences of AFR mixed with biogas on furnace temperature field and emission characteristics through numerical simulation. The results showed that the opposed injection position was the best type when the proportion of mixed biogas was constant. The results can provide new insight into co-combustion and thermal utilization of AFR.
Cogeneration system in the cane sugar industry consists of boiler, steam turbine, and evaporation process. Bagasse is used as fuel in boiler. Bagasse has a high moisture content, which leads to the inefficiency of energy conversion. The integration of steam dryer in cogeneration system to reduce bagasse moisture con-tent will improve the system performance. The use of parabolic trough collector to generate additional steam for steam dryer will enhance the capacity of steam dryer. In the paper, the cogeneration system integrated with steam dyer and parabolic trough collector is proposed. Simulation results from models of cogeneration system, boiler, steam dryer, and parabolic trough collector show that this system is capable of generating more power output than the cogeneration system without steam dyer and parabolic trough collector that consumes the same amount of fuel. The estimation of the payback period for the investment in steam dyer and parabolic trough collector is also provided.
The cyclone-fired boilers are very suitable for burning high-alkali content coal due to high slag capture rate and low flue gas dust content. However, NOx generation in cyclone-fired boilers is higher than that in other boiler types. In this work, the NOx generation characteristic of cyclone combustion was studied in a 100 kW cyclone combustion test bench. The results show that a strong reducing atmosphere is formed in cyclone barrel, and NOx generation is greatly inhibited in cyclone air-staging combustion. In cyclone barrel, NOx generation is high in the near-wall zone and low in the central zone. The central zone is the core region of NOx reduction. After the over fire air (OFA) injects into the burnout furnace, an obvious increase in NOx concentration is observed, which may be due to oxidation of char-N residue in char in burnout furnace.
The effect of alkali metal sodium on the NOx formation characteristic of zhundong coal under different temperatures, combustion atmospheres and loading amounts were studied on a fixed bed experimental system. The results show that alkali metal sodium could suppress the formation of NOx during coal combustion. The peak value of volatile nitrogen decreases with the increase in the adding amount of NaCl and increase with the temperature. When the combustion temperature is 1773 K, the alkali metal sodium addition of 1% has the most significant inhibitory effect on the NOx formation. The NOx conversion ratio can be reduced by 45.8%.
Â Heat demand prediction is a notable research topic in intelligent energy networks (IENs), due to the rapid growth of heat demand in cities. Given that hourly heat demand data can be considered as a time series data recording and well analyzed by time series seasonal decomposition algorithms, we develop a variant of the recurrent neural network (RNN), namely time frequency-domain memory (TFDM). The TFDM combines fast Fourier transform (FFT) and long short-term memory (LSTM) model to preserve memory of the series in both time and frequency domains, and cascades a residual block to introduce the impact factors (e.g., weathers). In the experiments, we compare the proposed TFDM with various referred methods on a heat demand dataset. The experimental results show that the proposed TFDM has significant performance improvement in the heat demand prediction.
In this study, a two-phase liquid-immersion cooling system was developed for ICT (Information communication technology equipment) cooling. The PUE energy efficiency of the two-phase cooling system was evaluated under various IT loads, and the annual PUE was calculated dependent on the meteorological parameters in Shanghai. Then, exergy analysis of the two-phase cooling system was taken based on the second law of thermodynamics. The observations and conclusions in this study can be valuable references for the study of cooling systems in data centers.