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An urgent demand for recycling spent lithium-ion batteries (LIBs) is expected in the forthcoming years due to the rapid growth of electrical vehicles (EV). To address these issues, various technologies such as the pyrometallurgical and hydrometallurgical method, as well as the newly developed in-situ roasting reduction (in-situ RR) method were proposed in recent studies. This article firstly provides a brief review on these emerging approaches. Based on the overview, a life cycle impact of these methods for recovering major component from one functional unit (FU) of 1 t spent EV LIBs was estimated. Our results showed that in-situ RR exhibited the lowest energy consumption and greenhouse gas (GHG) emissions of 4833 MJ FU−1 and 1525 kg CO2-eq FU−1, respectively, which only accounts for ~23% and ~64% of those for the hydrometallurgical method with citric acid leaching. The H2O2 production in the regeneration phase mainly contributed the overall impact for in-situ RR. The transportation distance for spent EV LIBs created a great hurdle to the reduction of the life cycle impact if the feedstock was transported by a 3.5–7.5 t lorry. We therefore suggest further optimization of the spatial distribution of the recycling facilities and reduction in the utilization of chemicals.

Conventional recovery enhancement techniques are aimed at reducing the abandonment pressure, but there is an upper limit for recovery enhancement due to the energy limitation of reservoirs. Gas injection for energy supplementation has become an effective way to enhance gas recovery by reducing hydrocarbon saturation in gas reservoirs. This review systematically investigates progress in gas injection for enhanced gas recovery in three aspects: experiments, numerical simulations and field examples. It summarizes and analyzes the current research results on gas injection for EGR and explores further prospects for future research. The research results show the following: (1) Based on the differences in the physical properties of CO2, N2 and natural gas, effective cushion gas can be formed in bottom reservoirs after gas injection to achieve the effects of pressurization, energy replenishment and gravity differentiation water resistance. However, further experimental evaluation is needed for the degree of increase in penetration ability. (2) It is more beneficial to inject N2 before CO2 or the mixture of N2 and CO2 in terms of EGR effect and cost. (3) According to numerical simulation studies, water drive and condensate gas reservoirs exhibit significant recovery effects, while CO2-EGR in depleted gas reservoirs is more advantageous for burial and storage; current numerical simulations only focus on mobility mass and saturation changes and lack a mixed-phase percolation model, which leads to insufficient analysis of injection strategies and a lack of distinction among different gas extraction effects. Therefore, a mixed-phase-driven percolation model that can characterize the fluid flow path is worth studying in depth. (4) The De Wijk and Budafa Szinfelleti projects have shown that gas injection into water drive and depleted reservoirs has a large advantage for EGR, as it can enhance recovery by more than 10%. More experiments, simulation studies and demonstration projects are needed to promote the development of gas injection technology for enhanced recovery in the future.

Electro-mechanical transmission is the best choice for the transmission system of military, engineering and other heavy special vehicles. The scheme design is fundamental and key to realize the original innovation of the electro-mechanical transmission. Therefore, a novel design method of a planetary-gear scheme is proposed for electro-mechanical transmission. According to the distribution of mechanical points and the speed continuous condition of mode switching, the mode combination law of a dual-mode electro-mechanical transmission is obtained, i.e., the input split mode based on the scheme of three-degree-of-freedom (3-DOF) and the compound split mode based on the scheme of 2-DOF. Moreover, a design method for an electro-mechanical transmission scheme is proposed based on the mode combination law. Two single-mode schemes are combined to form a dual-mode scheme, and then mode jointing, control logic, isomorphism and other screening conditions are in turn used to screen schemes; therefore, two optimized schemes are obtained ultimately. Lastly, by analyzing the characteristics of speed, torque and the power split of the optimized schemes, the accuracy of the proposed design method in this paper is verified. The proposed design method can provide new ideas of designing multi-mode and multi-output electro-mechanical transmission schemes.

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

With the continuous development of large-scale wind and photovoltaic power worldwide, the net load fluctuation of systems is increasing, thereby imposing higher demands for power supply assurance and new energy consumption capacity within emerging power systems. It is imperative to establish a quantifiable and efficient model for planning new power systems, to propose an analytical approach for determining optimal evolutionary paths, and to advance research on flexible resource planning across wide areas. In this paper, based on the simplified operating characteristics of multi-type flexible resources, a source-grid-load-storage collaborative planning and evolution analysis framework is established. Secondly, the lowest total cost of the whole life cycle of the system is taken as the optimization goal, the multiple flexible resource investment decisions and production operation constraints of various flexible resources on all sides of the system are considered, and the source-grid-load-storage planning model is established. Then, through the investment decision-making strategy setting of the system in different planning level years, the evolutionary path analysis method of the whole life cycle economy and weighted environmental protection benefit of the system is given. Finally, by taking the sending-end power grid in Gansu Province as an example, a case study is carried out. Calculations of new energy, key channels within the province, energy storage capacity, and load-side response capacity requirements for 2025, 2030, and 2060 are optimized. Based on the above analysis, the optimal evolution path of the power grid is proposed. When considering the weighted benefits of economy and environmental protection, the greater the weight of environmental protection benefits, the greater the possibility of choosing a radical scheme. The model and method proposed in this paper can provide technical reference for the future development planning and evolution analysis of new power systems.

Approximately 40% of the overall energy consumption of society is consumed by buildings. Most building energy usage is due to poor envelope performance. In regions with cold winters, the corners of structures typically have the lowest interior surface temperature. In corners, condensation, frost, and mold are common. This has a substantial effect on building energy usage and residents’ comfort. In this study, the heat loss of corner envelopes is evaluated, and a suitable insulation construction of wall corners is constructed to increase the surface temperature of the envelope interior. Computational Fluid Dynamics simulation has been used to examine the heat transmission in a corner of an ultra-low energy building in this study. By comparing the indoor surface temperature to the soil temperature beneath the building, the insulation construction of wall corners has been tuned. The study results indicate that the planned insulation construction of wall corners can enhance the internal surface temperature in the corner and the soil temperature under the structure by approximately 8.5 °C, thereby decreasing the indoor–outdoor temperature differential and the heat transfer at ground level. In extremely cold places, the insulation horizontal extension belt installation can help prevent the earth beneath the building from freezing throughout the winter.

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving.

A number of new rural management models have emerged to solve the problems of economic backwardness, insufficient resource utilization, and technical shortages in rural areas in the context of poverty alleviation to the rural revitalization strategy in China. However, the influence of new rural management model under all countermeasures for rural sustainable development with a comprehensive perspective is lacking. Therefore, exploring whether the new rural management model meets the requirements of sustainable development is an urgent issue. From the theory of system metabolism and emergy accounting method, this study classified the government funds for poverty alleviation measures as import resources, and analyzed the metabolic structure, efficiency, and the rural development factors of Chehe Village before and after poverty alleviation measures are carried out (the year of 2012 and 2019) to verify whether the new model was sustainable. According to the results of this study, the new management model of Chehe Village declined the rural system sustainability with the emergy sustainability index decreasing from 1.96 in 2012 to 0.32 in 2019. With the development of economy, the system metabolic efficiency of Chehe Village promoted and the metabolic structure became more reasonable manifesting in the decline of emergy use per unit GDP and the increase of emergy exchange rate. Moreover, production and livelihood had been highly valued in Chehe Village. In conclusion, it is feasible to add countermeasures of poverty alleviation and rural revitalization into the village system metabolism. The new management model of Chehe Village needs to change exogenous force into endogenous force to meet the requirements of rural sustainable development.

The aim of this paper is to evaluate the overall life cycle environmental impact of an adiabatic compressed air energy storage (ACAES) system, which is designed to achieve the best match between the power production of a photovoltaic (PV) power plant and the power demand from the final user. The electrical energy demand of a small town, with a maximum power load of about 10 MW, is considered a case study. The ACAES system is designed with a compressor-rated power of about 10 MW and charging and discharging times of 10 and 24 h, respectively. Different sizes of the PV plant, ranging from 20 to 40 MWp, and two different solutions for the compressed air storage, an underground cavern, and a gas pipeline, are analyzed. The aim of this analysis is to compare the impacts on human health, ecosystem quality, climate change, and resource consumption of the PV power generation plant and the integrated PV-ACAES system with those of a reference scenario in which the end user demand is met entirely by the grid. The best results in terms of a reduction in environmental impact in comparison to the reference scenario are obtained for a small PV plant (20 MW) without the ACAES section, with reductions of about 85–95% depending on the category of impact. The integration of the ACAES system improves energy self-consumption but worsens the environmental impact, especially for air storage in gas pipelines. The best configuration in terms of environmental impact is based on a 30 MW PV plant integrated with an ACAES section using an underground cavern for air storage and allows for improvements in the energy self-consumption of between 38% and 61%, with a reduction in the environmental impact compared to the reference scenario of about 80–91% depending on the impact category.

The yeast Yarrowia lipolytica is an industrially relevant microorganism, which is able to convert low-value wastes into different high-value, bio-based products, such as enzymes, lipids, and other important metabolites. Waste cooking oil (WCO) represents one of the main streams generated in the food supply chain, especially from the domestic sector. The need to avoid its incorrect disposal makes this waste a resource for developing bioprocesses in the perspective of a circular bioeconomy. To this end, the strain Y. lipolytica W29 was used as a platform for the simultaneous production of intracellular lipids and extracellular lipases. Three different minimal media conditions with different pH controls were utilized in a small-scale (50 mL final volume) screening strategy, and the best condition was tested for an up-scaling procedure in higher volumes (800 mL) by selecting the best-performing possibility. The tested media were constituted by YNB media with high nitrogen restriction (1 g L−1 (NH4)2SO4) and different carbon sources (3% w v−1 glucose and 10% v v−1 WCO) with different levels of pH controls. Lipase production and SCO content were analyzed. A direct correlation was found between decreasing FFA availability in the media and increasing SCO levels and lipase activity. The simultaneous production of extracellular lipase (1.164 ± 0.025 U mL−1) and intracellular single-cell oil accumulation by Y. lipolytica W29 growing on WCO demonstrates the potential and the industrial relevance of this biorefinery model.