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Oil shale is a kind of potential alternative energy source for petroleum and has attracted the attention of energy researchers all over the world. Borehole hydraulic mining has more prominent advantages than both conventional open-pit mining and underground mining. It is very important to attempt to use the borehole hydraulic mining method to exploit underground oil shale. The nozzle is the key component of borehole hydraulic mining and reasonable mining parameters are also crucial in exploiting underground oil shale efficiently. The straight cone nozzle and the oil shale of Huadian area will be taken as the research objects. The self-developed, multifunctional, experimental device can test both the jet’s performance as well as the breaking of oil shale by the high-pressure water jet using the straight cone nozzle and varying structural parameters. Comprehensive analysis of the results of an orthogonal experimental design, including range analysis and variance analysis, demonstrate the optimal structural parameters of a straight cone nozzle as follows: the outlet diameter is 4 mm, the length to diameter ratio is 2.5, and the contraction angle is 60°. In addition, in order to maximize the efficiency of borehole hydraulic mining for Huadian oil shale, the non-submerged jet should be placed parallel to the oil shale bedding. These results can provide scientific and valuable references for borehole hydraulic mining of oil shale.

Oil shale is a kind of potential alternative energy source for petroleum and has attracted the attention of energy researchers all over the world. Borehole hydraulic mining has more prominent advantages than both conventional open-pit mining and underground mining. It is very important to attempt to use the borehole hydraulic mining method to exploit underground oil shale. The nozzle is the key component of borehole hydraulic mining and reasonable mining parameters are also crucial in exploiting underground oil shale efficiently. The straight cone nozzle and the oil shale of Huadian area will be taken as the research objects. The self-developed, multifunctional, experimental device can test both the jet’s performance as well as the breaking of oil shale by the high-pressure water jet using the straight cone nozzle and varying structural parameters. Comprehensive analysis of the results of an orthogonal experimental design, including range analysis and variance analysis, demonstrate the optimal structural parameters of a straight cone nozzle as follows: the outlet diameter is 4 mm, the length to diameter ratio is 2.5, and the contraction angle is 60°. In addition, in order to maximize the efficiency of borehole hydraulic mining for Huadian oil shale, the non-submerged jet should be placed parallel to the oil shale bedding. These results can provide scientific and valuable references for borehole hydraulic mining of oil shale.

This paper presents a one-dimensional finite-volume model of an unglazed photovoltaic/thermal (PVT) solar collector. The unit consists of a conventional solar photovoltaic (PV) collector coupled with a suitable heat exchanger. In particular, the collector includes a roll bond heat exchanger and it is not equipped with back and frame insulation. The system is discretized along the flow direction (longitudinal) of the cogenerative collector. For each finite-volume element of the discretized computational domain, mass and energy balances are implemented. The collector geometry and materials parameters are taken from a commercially available device. An on-field experimental investigation is performed in order to validate the proposed model. The model is used to evaluate both electrical and thermodynamic parameters for each element of the domain and for fixed operating conditions. Finally, a sensitivity analysis is also performed in order to investigate the energetic performance of the cogenerative collector as a function of the main design/environmental parameters.

This paper presents a one-dimensional finite-volume model of an unglazed photovoltaic/thermal (PVT) solar collector. The unit consists of a conventional solar photovoltaic (PV) collector coupled with a suitable heat exchanger. In particular, the collector includes a roll bond heat exchanger and it is not equipped with back and frame insulation. The system is discretized along the flow direction (longitudinal) of the cogenerative collector. For each finite-volume element of the discretized computational domain, mass and energy balances are implemented. The collector geometry and materials parameters are taken from a commercially available device. An on-field experimental investigation is performed in order to validate the proposed model. The model is used to evaluate both electrical and thermodynamic parameters for each element of the domain and for fixed operating conditions. Finally, a sensitivity analysis is also performed in order to investigate the energetic performance of the cogenerative collector as a function of the main design/environmental parameters.

Recent developments in micro-grids have led to increased interest in DC distribution due to its high efficiency in distributing energy from renewable energy sources to DC loads. This paper seeks to analyse the performance of AC and DC systems in a relatively large-sized 6 kW PV installation to determine the level of improvement in efficiency provided by DC distribution and to identify methods for further improvement. Baseline annual data for the AC system were collected from a live installation on a national school in Inis Oirr, an island off the west coast of Ireland. The results indicate that usage of a DC distribution system has the potential to reduce system losses by up to 50% as well as the ability for an annual saving in grid energy of 5% compared to the existing AC system. Moreover, the analysis reveals that DC outperforms AC distribution more in spring and autumn, when power consumption is comparable to the system production, but there is less impact in summer, when PV production is significantly higher than demand. These findings provide insights into the benefits of future DC distribution systems in individual buildings and in larger-scale micro-grids.

Recent developments in micro-grids have led to increased interest in DC distribution due to its high efficiency in distributing energy from renewable energy sources to DC loads. This paper seeks to analyse the performance of AC and DC systems in a relatively large-sized 6 kW PV installation to determine the level of improvement in efficiency provided by DC distribution and to identify methods for further improvement. Baseline annual data for the AC system were collected from a live installation on a national school in Inis Oirr, an island off the west coast of Ireland. The results indicate that usage of a DC distribution system has the potential to reduce system losses by up to 50% as well as the ability for an annual saving in grid energy of 5% compared to the existing AC system. Moreover, the analysis reveals that DC outperforms AC distribution more in spring and autumn, when power consumption is comparable to the system production, but there is less impact in summer, when PV production is significantly higher than demand. These findings provide insights into the benefits of future DC distribution systems in individual buildings and in larger-scale micro-grids.

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.