A triple-pressure organic Rankine cycle (TPORC) using geothermal energy for power generation has been investigated in this paper. The net power output of the TPORC was analyzed by varying the evaporation pressures, pinch temperature differences and degrees of superheat to find the optimum operation conditions of the system. The thermodynamic performance of the TPORC was compared with dual-pressure ORC (DPORC) and single-pressure ORC (SPORC) respectively for the heat source (geofluid) temperature between 135°C and 200°C. The results show that the net power output of the TPORC is higher than that of the DPORC and SPORC when the heat source temperature is low, especially when it is less than 150°C. Thus the TPORC could be a choice for power generation for utilizing medium-low geothermal resources (100°C-150°C) provided that it has a sound techno-economics.
When coupled to different applications, a polymer electrolyte fuel cell (PEFC) work at a different operating temperature. In this study, the internal resistance of a PEFC is evaluated by using Electrochemical Impedance Spectroscopy (EIS) at moderate low current density, i.e., 0.5 A/cm2, in the range of 40 – 80°C. The evaluation is carried out considering frequencies between 10kHz and 0.1Hz. An equivalent Randle circuit model is considered and the Nyquist and Bode diagram were obtained. Results show that the ohmic resistance and charge transport at low frequencies increase when the operating temperature is low, while the double layer capacitance increase at high temperatures.
Molecular dynamics simulations are used to study the replacement behavior of CH4 in the hydrate with CO2 injection. The molecular configurations and microstructure properties are analyzed with the systems containing the gas layer (CO2 or CH4) and the CH4 hydrate layer. It is found that the H2O molecules arrangement of hydrate surface is changed in the replacement process. These H2O molecules of hydrate surface move and reform the solid cages, while H2O molecules of the interfacial transition zone of the gas layer and the hydrate layer present the quasi‐liquid structure. The simulation results indicate the H2O molecules arrangement is affected by the gas molecules in replacement process.
The content of bound water in the montmorillonite soaked in NaCl solution is measured by thermogravimetric analysis. The variation of NaCl solution concentration and bound water content is discussed. The results show that the bound water content of the montmorillonite decreases with the increase of salt ion concentration. The experimental drying temperature and heating rate has little effect on the bound water content. It is concluded that ions have a great influence on the water content in porous media, which provides guidance for the research on the influence of ions on the formation of hydrate in porous media.
This paper presents a Huber-M estimation of Thevenin equivalent calculation method using variable forgetting factor and projection statistics for the accuracy of Thevenin equivalent parameter identification in time-varying outliers. The method employ the Huber function based on projection statistics to suppress the influence of outliers on parameter identification to improve the robustness of the algorithm by using local measurement data. It also can trace the change of system quickly by using a variable forgetting factor. In order to improve the stability and generalization performance of the model, the paper adopt regularization technique algorithm to solve the problem of ill-conditioned matrix inversion. The simulation results of IEEE 30 node systems verify the effectiveness and accuracy of the proposed method.
Taking diesel-ignited direct injection natural gas (NG) engine as the research object, the synergistic control of intake pressure and NG injection timing on the organization of in-cylinder mixture and combustion process was analyzed under the condition of low load (1300rpm) by numerical simulation, in order to achieve low HC emission and high thermal efficiency. The results show that by improving intake pressure, the NG jet momentum, squeeze flow and turbulent motion in cylinder are enhanced, which is beneficial to the stratification of mixture, gradual ignition and combustion rate in cylinder. In addition, too prior injection results in more combustible mixture entering the cylinder clearance, leading to incomplete combustion; too late injection causes too late combustion, reducing the power capability and thermal efficiency. By optimal matching the intake pressure and NG injection timing, a higher indicated thermal efficiency (ITEg) of 49.03% can be obtained when the intake pressure is 0.2MPa and the injection timing is -10°CA ATDC with a lower NOx emission level without using EGR technology.
Organic Flash Cycles (OFCs) are preferred to convert low temperature geothermal energy to electricity. In this work, comparative studies on two kinds of systems aiming to recover the heat of the saturated liquid after flash evaporation are presented. Modified OFC (MOFC) mixes the saturated liquid after flashing with the cold working fluid to recover the heat of liquid after flashing. Regenerative OFC (ROFC) that use an internal heat exchanger to recover the part of heat of saturated liquid after flashing. The flash temperatures for Basic OFC (BOFC), ROFC and MOFC using pentane were optimized to maximize the net power outputs at various condensation temperatures. Results shows that recovering the heat of liquid after flashing leads to system performance improving, irreversible loss decrease and change of locations of pinch points.
The Central Air-Conditioning System (CACS) in subtropical region is responsible for more than 50% of total energy consumption in public buildings. Improper operating modes of CACS often lead to abnormalities in DHECM (Diurnal Hourly Energy Consumption Mode), the detection of which is of great significance for energy conservation. However, It is difficult to detect the abnormal modes effectively by conventional feature extraction and single threshold anomaly detection methods due to its complicated operational condition. Two-year hourly energy consumption data of CACS in an office building collected by CACS monitoring and control platform are divided into types of typical working conditions by decision tree and the information entropy value is used as the characteristic parameter of uncertainty for diurnal hourly energy consumption time series to reduce their dimension. Furthermore, a clustering unsupervised algorithm was used to classify normal and abnormal DHECM which solve the problem that the threshold of the abnormal mode is difficult to determine. The abnormal detection results showed the effectiveness of this method in the field of abnormal DHECM detection of public buildings.
Conventional heating, ventilation and airconditioning (HVAC) features such as use of static occupancy schedule profile to control HVAC operation or traditional controls may not be enough to cope with requirements of the next generation-built environment. This work introduces a demand-driven deep learningbased framework which can be integrated with building energy management systems (BEMS) to accurately predict occupancy’s activity for HVAC systems which can minimize unnecessary loads and produce satisfactory thermal comfort conditions for occupants. The developed framework utilises a trained deep learning algorithm and an artificial intelligence (AI)-powered camera. Tests are performed with new data fed into the framework which enables predictions of typical activities in buildings such as walking, standing, sitting and napping. To initially test and validate the framework, building energy simulation was used with various occupancy profile schedules under a modelled UK office building with 4 occupants. Initial results present occupancy heat gains were 23.5% lower when Deep Learning Influenced Profile (DLIP) was used as compared to static office occupancy profile. Further developments include; framework enhancement to increase detection accuracy and to provide automated set point adjustment for HVAC system. Initial data indicates the method could resolve occupancy related problems within buildings and enhance building performances through accurate occupancy activity prediction.
The energy transition raises a need for innovative ideas to cope with the integration of high shares of variable renewable energy sources. Across political, industrial and research audiences, the idea of electricity self-sufficiency has been gaining rising interest. It might, among others, solve questions of energy security and help to avoid short term necessary investments into the electricity grids. However, while electricity selfsufficiency can be technically feasible for a large range of user types (e.g. residential, agricultural, and industrial) and cluster sizes (e.g. individuals, districts, municipalities), it becomes rapidly unfeasible when strict regulations are considered. In this study, the feasibility of electricity self-sufficiency based on free-standing photovoltaics, wind power and storage systems was evaluated for all municipalities in Bavaria (Germany) and the Czech Republic. Main focus of this paper is the calculation of the spatially distributed electricity demand of today and the future. Methods for the technology potential evaluation and the development of an optimization model to determine necessary system sizes are shortly presented referring to previous work. Results indicate that around 20% of the German and 6% of the Czech municipalities could achieve self-sufficiency today based on the considered technologies and under current Bavarian regulatory restrictions. These figures improve enormously with milder regulations for wind power installations. Furthermore, due to an expected depopulation of rural areas, a rising trend in potentially electricity self-sufficient municipalities is visible.