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• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility of using organic wastewater as renewable energy source is verified. • ＞99% COD, TOC removal rate in the experimental platform. • Up to 78.75% of the energy recovery efficiency of the SCWO system. The degradation of high concentration organic wastewater and the recovery of energy using supercritical water oxidation (SCWO) technology have the potential to become viable in the future. High concentration organic wastewater contains a significant amount of energy. This paper focuses on utilizing high concentrated wastewater as a renewable energy source, from an energy system perspective, an experimental rig was designed to determine the optimal working conditions. The experiments were conducted with an oxidation coefficient of 1.1, a flow rate of high concentration organic wastewater of 9 ml/min, a heating temperature of 400 °C, and a reaction pressure of 25 MPa. Under these conditions, the COD and TOC removal rates of the high concentration organic wastewater were found to be 99.383 % and 99.876 % respectively. Additionally, the phenomenon of "Abnormalities in pressure sensitivity" observed during high concentration organic wastewater the experiment was further explained through additional experiments. Finally, by comparing the experimental energy recovery with the simulation results, an energy recovery efficiency of 78.95 % was achieved. The feasibility of the simulated energy recovery in practice was verified, confirming the potential to convert the previous energy-consuming wastewater treatment process into a productive one. This study provides a theoretical and experimental foundation for further related research. [ABSTRACT FROM AUTHOR]

• Design of a non-intrusive device to intelligently control electric water heaters. • Development of a machine-learning tool to forecast residential hot water demand. • Details and results of a six-month pilot study where the device was implemented. • Heating demand reduced by an average of 26.43% and cost saving of 35.54% per device. • Device is cost-effective and can reduce the need for imported fossil-fuel generation. Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer's hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative 'heat now' function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO 2 per year. The results from the consumer survey show that the device did not affect the consumer's comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities. [ABSTRACT FROM AUTHOR]

• Hybrid energy harvester with quasizero system is designed. • Different strategies for orbit jump using impulses are proposed and summarized. • A step-by-step method of correcting solutions is proposed. • Coexistence of chaotic and periodic solutions are checked. • Various parameters of quasizero system were analyzed. Energy harvesters based on non-linear systems are promising devices for extracting energy from mechanical vibrations. This paper presents a new design of energy harvester consisting of two coupled nonlinear systems; the Duffing oscillator and a system with quasizero stiffness. A numerical analysis of the dynamics of the harvester is carried out, presenting coexisting solutions and their energy efficiencies in both chaotic and periodic motion zones. The root mean squared (RMS) voltage results depend on the dimensionless excitation frequency, where high-energy orbits are coexisting with low-energy orbits. Therefore, the second part of the paper focuses on various strategies for jumps between the orbits using impulses. Different impulse characteristics and their sequences for periodic and chaotic zones are analyzed. Therefore, a detailed analysis is presented for many strategies using an impulse excitation diagram (IED) as a numerical tool for accurately estimating the amplitude of the impulse, its duration, and the moment of initiation. The probability of achieving a given solution is also determined. The simulation results show that achieving the most effective orbit with a single impulse, as well as several impulses, requires similar energy. However, the advantage of the step-by-step method is the lower energy required to initiate a single impulse which enables the use of a smaller regulator. This work can be a valuable tool for designing various systems and strategies for changing the orbit of a solution. [ABSTRACT FROM AUTHOR]

• Advanced climate-resilient energy retrofit technology reduces the use of non-renewable energy in the building sector. • Renewable District Heating as an optimal technology in deep energy renovation strategies. • Sustainable cities and buildings are feasible by Centralised District Heating system powered by biomass. • Key Performance Indicators (KPIs), to achieve nZEB performances established in the European legislations. This paper presents the implementation of a biomass-fuelled District Heating System (DHS), as a part of a deep energy renovation exercise to achieve a climate-resilient campus with minimum carbon dioxide emissions. The case study is carried out for the University of Valladolid, an average-sized university in Spain, with a continental weather climate. Prior to renovation, the different building blocks had a wide-ranging level of fossil fuel consumption for space heating and domestic hot water ranging between 60 and 430 kWh/m2·year. The application of this centralised heating system allows to achieve the minimum threshold for near zero-energy buildings (nZEB) of 100–120 kWh/m2, in accordance with the Spanish Standards. These values correspond to the maximum European indicators for offices in continental weather conditions. Results of this comprehensive study show that 15 out of the 19 buildings reached the nZEB target, due to the proposed strategy. The overall carbon dioxide emissions have dropped by 92.69% as compared to the original fossil-fuel powered boiler, thus bringing carbon dioxide emissions down to 1.57 kgCO 2 /m2·y. Therefore, it is shown that deep energy renovation strategies through renewable energy DHS have the potential of achieving nZEB for universities in continental weather conditions. [ABSTRACT FROM AUTHOR]

• The hotel system of a cruise ship was modelled by using a building simulation tool. • The modelled annual energy use was 55 MWh/passenger, with 20% for the hotel system. • Various energy efficiency measures were evaluated for the hotel system. • Heat pumps gave the largest energy savings with a 38% reduction. • Using a hot water storage tank could eliminate use of auxiliary boilers in ports. In light of climate change, the shipping industry is committed to reducing its greenhouse gas emissions. When transitioning from fossil fuels to low-carbon fuels, it is crucial to also reduce the ships' energy need. For cruise ships, the hotel system is a major energy consumer and the focus of this paper. This study aims to investigate the energy use of an existing fossil-fuelled cruise ship operating in a Nordic climate, and evaluate the effectiveness of various passive and active energy-saving measures. For this purpose, a dynamic energy model of the hotel system was developed in the building simulation tool IDA Indoor Climate and Energy (IDA ICE), including a customised weather file. Validation of the model was achieved through comparison with literature and some operational data from the actual cruise ship. Simulation results showed a total annual energy use of 55 MWh/passenger, with the hotel system accounting for 20%. The passive measures, such as increased insulation and improved windows, each resulted in less than a 1% reduction in the hotel's annual energy use. Larger energy savings were achieved by using heat pumps (38%), improved ventilation system (8–24%), and heating setback in port and during the night (5%). A hot water storage tank, charged with the engines' waste heat during sea operation, could reduce the use of auxiliary boilers, especially in port. A hot water tank of 600 m3 could cover 97% of the heating demand in port, thereby minimising the use of fuel-fired boilers. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel Magnetic Field Pre-polarization (MFPP) method is proposed to improve the efficiency of hydrogen production. • MFPP is easy to match with high-power industrial AWE equipment. • MFPP improve the hydrogen production efficiency by a maximum of 9.2%. • The MFPP method provides a way for improving the efficiency of industrial AWE system. Water electrolysis can become a promising mean to produce clean hydrogen. Improving the efficiency of water electrolysis is of great significance for accelerating the development of large-scale hydrogen production equipment and low-carbon economy. This paper presents a novel method for enhancing the alkaline water electrolysis (AWE) efficiency by Magnetic Field Pre-polarization (MFPP), which is easily matched to high-power industrial AWE system. Firstly, compactly-assembled structure and energy consumption of industrial AWE electrolyzer are analyzed. Secondly, the MFPP method was proposed to enhance the efficiency of AWE. The structure parameters of MFPP equipment are optimized by simulation and the physical model is designed. Finally, a system-level equipment was developed to evaluate the effect of MFPP on the performance of the alkaline water electrolysis (AWE) system. Repeatability measurement, I-V characteristic analysis and error statistics were carried out under different working conditions. The effectiveness of MFPP method has been proved on industrial electrolyzer (with 22 cells). Compared with the electrolysis voltage of 40 V-42 V@22_cell, the efficiency improvement is more significant when the voltage is in the range of 42–46 V@22_cell. Under the 0.75 T MFPP, the maximum hydrogen production efficiency increased by 9.2 %. The mean absolute deviation (MAD) of test results is less than 1.88 (with a proportion of 0.11 %) and the mean squared error (MSE) is less than 0.49. [ABSTRACT FROM AUTHOR]

• The system engineering based on photovoltaic thermal heat pump was constructed. • A new idea for the energy-saving reconstruction of district heating was introduced. • The prediction model of system heating performance was established and optimized. • The equivalent electricity consumption of heating was defined. • The economic performance of energy-saving reconstruction was studied. Actively promoting the reconstruction of clean heating is the key direction of the "Planning for clean heating in winter in northern China (2017–2021)". The photovoltaic thermal heat pump system was introduced in this paper to improve the comprehensive energy efficiency and reduce the operation energy consumption of the heat source system, which provided a new idea for the energy saving reconstruction of clean district heating. In order to verify the feasibility of the energy saving reconstruction by photovoltaic thermal heat pump, following studies were carried out in this paper: First, the integrated energy system engineering based on photovoltaic thermal heat pump was constructed to study the heating performance of the system in winter. Subsequently, the prediction model of heating performance based on back propagation neural network was established and optimized to simulate the heating performance throughout the year. Then, the calculation model of district heating system was established and the evaluation method of system operation energy efficiency and economic performance based on "equivalent electricity consumption of heating" was introduced. Finally, a residential area of Dalian was taking as an example to study the economic performance of energy saving reconstruction of district heating system based on three conventional heat sources by photovoltaic thermal heat pump. The results showed that after the capacity expansion and energy saving reconstruction based on photovoltaic thermal heat pump were conducted, the payback period of energy saving reconstruction investment was 5 years. Besides, with the increase of the configuration ratio of photovoltaic in the system from 1 to 2, the payback period was further shortened to 3 years. [ABSTRACT FROM AUTHOR]

• Optimization-based comparison of natural gas liquefaction processes is proposed. • Broad process flowsheet complexity and natural gas processing scales are considered. • The AP-X process has the lowest energy consumption and cost for 8 MTPA plants. • The single mixed refrigerant process has the highest profit for small-scale plants. • Energetically better processes may not provide a higher economic return. The demand for liquified natural gas grows as it is an energy resource that has more flexible means of transport than non-liquefied natural gas, and is more eco-friendly than other fossil fuels. This paper focused on comparing five different mixed-refrigerant liquefaction processes to determine which one is the most efficient in energetic and economic terms for four different natural gas processing scales (20,000 kg/h and 2, 4, and 8 MTPA of natural gas feed). Simpler processes with one or two refrigerant cycles (single mixed refrigerant, propane pre-cooled mixed refrigerant, and dual mixed refrigerant) and more complex processes with three cycles (mixed fluid cascade and AP-X) were analyzed. All processes were simulated in Aspen HYSYS, and the energy consumption was optimized by a communication between the simulator and an optimization algorithm based on Particle Swarm Optimization coded in MATLAB. The economic analysis was performed using the CAPCOST technique and the rule of six-tenths for upscaling. In the best-optimized scenario, the results show that the most complex process, AP-X, consumes the lowest amount of energy (0.2367 kWh/kg-LNG) while the simplest one, the single mixed refrigerant, consumes the highest value (0.2561 kWh/kg-LNG). In the economic analysis, the results indicate that energetically better processes may not provide a higher economic return even on large scales. [ABSTRACT FROM AUTHOR]

• An experimentally validated system-level model of a fuel cell electric vehicle. • Fuel cell electric vehicle is simulated with a coupled multi-domain model. • Model comprises multiple scales from intra-fuel cell phenomena to the vehicle level. • Model comprises a fuel cell and battery aging model. • Model enables multi-objective optimisation of component sizing and control strategy. One of the key challenges in the design of fuel cell vehicles arises from the need for the simultaneous optimisation of their efficiency and lifetime, which is, in particular, challenging when operating under transient operating conditions; further, the minimisation of costs and the use of critical materials should be considered. To enable a more detailed virtual exploration of the design space, this paper presents a high-fidelity multi-domain and multi-scale model of a fuel cell electric vehicle that is capable of modelling the coupled phenomena from the vehicle level to intra-fuel cell level with a mechanistically based fuel cell model. The high-fidelity multi-domain and multi-scale model of a fuel cell electric vehicle is based on consistent coupling of different domains, that is, mechanical, electrical, electrochemical, gas flow, thermal, and control, which are modelled with mechanistic governing equations coupled to empirical degradation models of electrochemical devices and solved with multirate solvers ensuring flux conservations and synchronization of time steps. Establishing a consistent model based causal interactions between different domains and the significantly different scales associated with the phenomena in these domains, which more consistently virtually replicates the phenomena in a real fuel cell electric vehicle, is crucial for evaluating the impact of the parameters from high scales on intra-fuel cell spatio-temporal characteristics impacting fuel cell degradation. The results demonstrate that the application of an experimentally validated multi-domain and multi-scale model of a fuel cell electric vehicle enables the virtual exploration of interactions considering the scale of vehicle characteristics and its driving pattern to the scale of intra-fuel cell spatio-temporal characteristics and its degradation early in the development stages of the fuel cell electric vehicle. Therefore, the proposed approach represents a valuable contribution to the virtualisation of the system-level design of powertrains of fuel cell electric vehicles and supports front loading in the development process. [ABSTRACT FROM AUTHOR]

• Integrating CO 2 capture with pyrolysis has a big potential to achieve negative emissions. • The pyrolysis reaction time shows clear impacts on CO 2 capture. • Techno-economic analysis is conducted for different technologies of CO 2 capture. • Chemical looping combustion shows the best techno-economic performance. Emerging negative emission technologies (NETs) are considered as effective measures to reduce carbon dioxide emissions to achieve the climate goal set by the Paris Agreement, and bioenergy with carbon capture and storage (BECCS) is one of the most important NETs. Integrating CO 2 capture with biomass pyrolysis (PyrCC) is attracting increasing interest, because biomass pyrolysis has been widely used to produce biooil to replace fossil fuel for decarbonizing the transport sector. In order to provide guidance to the selection of CO 2 capture technologies, this paper evaluated the technical and economic performances of PyrCC when different CO 2 capture technologies are integrated, including monoethanolamine-based chemical absorption (MEA-CA), temperature swing absorption (TSA), calcium looping (CaL), and chemical looping combustion (CLC). Generally speaking, CLC can realize the highest capture amount of CO 2 with the lowest energy penalty. Meanwhile, CLC and CaL show the lowest levelized cost of CO 2 (LCOC), which are around 56$/tCO 2 ; and on the contrary MEA-CA shows the highest one of 83$/tCO 2. In addition, the key process parameter of pyrolysis, reaction time, has clear effects on the performance of CO 2 capture as the longer reaction time leads to an increased amount of captured CO 2 and reduced energy penalty. As a result, when the reaction time increases, the LCOCs of all assessed technologies decrease. Moreover, the net present value and the payback time are also estimated for different technologies. At the carbon price of 70.1$/tCO 2 , MEA-CA and CLC show the longest and shortest payback time that are 5.9 years and 3.2 years respectively. [ABSTRACT FROM AUTHOR]

• The proposed approach involves multiple regression and superstructure optimisation. • Streams with evaporation and condensation are properly handled in model formulation. • The proposed approach provides better solutions with higher net power output. • The trade-off between the energy cost and the number of heat exchangers is explored. • The effect of electricity price on organic Rankine cycle integration is examined. Organic Rankine cycles can produce power from various low-to-medium temperature heat sources efficiently, and may be integrated into industrial processes to recover low-grade waste heat for improved energy efficiency. This paper presents a mathematical programming model for the synthesis of organic Rankine cycle-integrated heat exchanger networks. The model is based on an organic Rankine cycle representation of four alternative configurations and a modified stage-wise superstructure, where heat exchange between process streams and organic Rankine cycle streams can take place in all the stages. In addition, the flowrate and operating temperatures of the working fluid are treated as variables, and its thermodynamic properties (e.g. enthalpies and pump/turbine outlet temperatures) are correlated as functions of the temperatures. This allows the organic Rankine cycle and the heat exchanger network to be optimised simultaneously, with the objective of maximising the net power output or minimising the overall energy cost. Two literature examples are used to illustrate the proposed approach. The results show that apart from producing power, the organic Rankine cycle can reduce the cold utility consumption by up to 26% when integrated with a process. Furthermore, the overall energy cost can be minimised by increasing the net power output optimally (by 54.6%), despite increased hot (+38.9%) and cold utility requirements (+19.5%). The proposed approach is thus useful in assessing the benefits from process integration of organic Rankine cycles. [ABSTRACT FROM AUTHOR]

• A PEMFC-driven CCP system with heat pipe for data center applications is proposed. • New operation strategies for power and cooling supplies in a CCP system are proposed. • Performances of two PEMFC-driven CCP systems with multiple cooling are compared. • Effect of heat pipe on the power and cooling supplies in the CCP system was studied. With the dramatic development of data centers, the energy management system in data center applications has become significantly important in recent years. A proton exchange membrane fuel cell (PEMFC)-driven combined cooling and power (CCP) system with multiple cooling devices was proposed in this paper to fully utilize a natural cold source. Another CCP system without natural cold source utilization was also proposed for comparison. Power and cooling were supplied to these systems with new operational strategies. Based on the proposed model, the operational performance of the two CCP systems was analyzed for a sample data center. The seasonal and annual power supplied, cooling supplies, matching degrees, energy consumption for removing cooling loads, and system efficiencies in the two proposed CCP systems are discussed and analyzed in detail. Results indicate that by using the designed operational strategies, the power and cooling supplies almost matched perfectly with the requirements of the sample data center. The annual energy efficiencies can reach 86.53% and 85.02% for the two proposed CCP systems. [ABSTRACT FROM AUTHOR]

• A methodology to model hybrid evaporative-vapor compression systems was developed. • Their performance was compared to a standard vapor compression system. • ASHRAE data were used to evaluate these systems for 21 global climates. • Water consumption requirements of HEVC systems were analyzed. • HEVC systems are 20% more efficient than VCC in hot and dry climates. Currently, a majority of global residential air-conditioning requirements are met using cooling systems based on the vapor compression cycle (VCC). To meet the future demand for space cooling and to reduce the corresponding environmental impact, there is a need for alternative cooling systems which require less resources and exhibit a higher coefficient of performance (COP). One of proposed alternatives is called the hybrid evaporative-vapor compression (HEVC) cycle, which utilizes adiabatic latent cooling in combination with the VCC. In this paper, we conducted a feasibility analysis of a HEVC system by comparing its performance to that of VCC for various global climates. To accomplish this, we developed and validated models to simulate the performance of residential VCC and HEVC systems. ASHRAE weather data and design conditions were then used to compare the performance of the two systems. In general, HEVC systems are best suited for hot arid climates with energy reduction greater than 20%. Conversely, humid climates are not suitable for HEVC adoption due to degraded performance of evaporative coolers in these climates. In addition to comparing the energy savings from HEVC systems, it is also critical to analyze their water consumption. Generally, climates that benefit the most from HEVC technology tend to also experience water scarcity issues. Thus, both the energy savings and water consumption of HEVC systems must be analyzed to guide the discussion on their adoption. [ABSTRACT FROM AUTHOR]

Traditional desiccant-based dehumidification systems suffer from low energy efficiency due to the desiccant's fixed affinity to water, meaning it has a single moisture adsorption isotherm regardless of the temperature. We modeled a novel thermo-responsive desiccant with temperature-dependent isotherms in a desiccant wheel model for three representative dehumidification scenarios. These cases show remarkable improvement in moisture removal efficiency of this material because of its switchable water affinity below/above the critical temperature. Our work discusses how to apply and tune the thermo-responsive desiccants for further enhancement in dehumidification performance, depending on application. [Display omitted] • Developed a thermo-responsive desiccant wheel model for air dehumidification. • Demonstrated how and why thermo-responsiveness improves dehumidification efficiency. • Quantified thermo-responsive desiccants' improvement in dehumidification efficiency. • Provided guidance on desiccant material and system design for air dehumidification. The desiccant wheel is a promising technology for energy-efficient humidity control. However, its overall efficiency—hindered by the desiccant materials' properties—remains low because traditional desiccants (e.g., silica gels) have a single isotherm regardless of their adsorption temperature. Thermo-responsive materials have been proposed to break this fixed affinity to water vapor, with drastically different adsorption isotherms depending on temperature. Its potential for improving dehumidification efficiency, however, has not been addressed. In this paper, we model the potential of a thermo-responsive interpenetrating polymer network (IPN) desiccant with temperature-dependent adsorption isotherms for energy-efficient dehumidification via a validated transient desiccant wheel model for humidity control of buildings. Thermo-responsive desiccants can improve the dehumidification performance due to their thermo-responsive switchable hydrophilicity below/above the lower critical solution temperature. Our analysis shows that thermo-responsive IPN desiccants can potentially reduce energy consumption by up to 30% compared to silica gels. The savings depend strongly on the critical temperature of the thermo-responsive desiccant and should be higher when the inlet temperatures are expected to be higher. [ABSTRACT FROM AUTHOR]