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The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the imperative of leveraging renewable indigenous energy sources becomes evident. However, as such technologies are integrated into the existing system, it is necessary to shift from the current centralized infrastructure to a decentralized production strategy. This paper presents a method to identify a panel of technological solutions at the district level, intended to reduce complexity for the integration of decentralized models into a national-scale model. The framework's novelty lies in combining a global sensitivity analysis for solution generation with clustering to identify typical configurations. The global sensitivity analysis is performed on a mixed integer linear programming model, which optimally sizes and operates district energy systems. The sensitivity analysis determines the most influential parameters of the model using the Morris method and provides a representative sampling of the solution space by leveraging the Sobol sampling strategy. The latter is then clustered using a density-based algorithm to identify typical solutions. The framework is applied to a suburban and residential Swiss neighborhood. The first outcome of the research is the high sensitivity of the model to energy carrier prices. As a result, Sobol's sampling space separates itself into two system types: those based on a natural gas boiler and those relying on a combination of electrical heaters and heat pumps. For both types, the electricity demand is either fulfilled by PV panels or electricity imports. The identified configurations showcase that the framework successfully generates a panel of solutions composed of various system configurations and operations being representative of the overall solution space. • Energy carriers' retail prices are the most influential parameters of a district energy system. • Energy system configurations differ according to the consumed energy carrier and PV penetration. • PV deployment is affected by electricity tariffs but not by technological implementations. [ABSTRACT FROM AUTHOR]

This study proposes a novel component sizing method for electric delivery trucks (EDTs) employing dual motor coupling powertrain (DMCP) to enhance both the energy efficiency and operating profitability. A control-oriented model for the EDT is first established, encompassing the three-mode DMCP dynamics. Variations in component size and mass have been modeled, with consideration of their effects on the load capacity. To maximize the average profit per kilometer over the truck's lifespan, four objective functions are defined to accommodate to the diverse types of cargo being transported. We formulate the optimization problem in a bi-level form, and propose a solution method that combines particle swarm optimization (PSO) handling parameter filtering with iterative dynamic programming (IDP) to minimize energy consumption. Three real-world delivery tests show that component sizing leads to an increase in the average profit per kilometer by 2.62 %–8.10 %. Upon evaluating the impact of powertrain and battery mass/volume on cargo capacity, the battery pricing ceases to impact the sizing of components. However, the electricity price and freight significantly influence the optimal size of components. Moreover, a sensitivity analysis focusing on market price factors underscores the importance of component sizing for maximizing profit, particularly in scenarios where freight costs fluctuate in commercial settings. • Optimal sizing for an electric delivery truck with a dual motor powertrain. • Both size and mass of the components are taken into optimization. • The average lifetime profit per kilometer of the truck is maximized. • Sensitivity of the optimal component size to market price changes is reported. [ABSTRACT FROM AUTHOR]

Casing damage is one of the common problems encountered in reservoir development, which seriously affects the normal production of the oil field. In this study, through the analysis of oil field data, a casing damage model under the coupling effects of mudstone hydration-corrosion and sand production-corrosion was established. Thirty-four influencing factors of casing damage were determined in four categories: geology, engineering, development, and corrosion. Six machine learning methods were used to predict the probability of casing damage under the coupling effects of multiple factors. The generalization performance of the model was evaluated using the recall rate of casing damage wells and accuracy. The results show that the random forest and LightGBM models show the best generalization performance. The prediction accuracy rates of the two models on the test set were 84.2% and 85.9%, respectively, and the random forest model showed an excellent performance of 92.3% on the recall rate of casing damage wells. Finally, the optimized model was used to perform sensitivity analysis on each influencing factor, and the main controlling factors of casing damage were obtained. Engineering measures to prevent casing damage are proposed. This study has made outstanding contributions to improving the economic benefits of the oilfield. [Display omitted] • A machine learning method for casing damage prediction under multi-factor coupling conditions was established. • The proposed casing damage prediction model can accurately predict the downhole casing's real condition. • Several engineering measures were proposed to reduce the probability of casing damage for new and old wells. • Considered geological, engineering, development, and corrosion factors that affect casing damage. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive evaluation of the efficiency and cost implications of a modular cogeneration plant integrated with a Battery Energy Storage System (BESS) and an Organic Rankine Cycle (ORC) plant. The objective is to assess the performance of the system when waste heat recovery through the ORC cycle is added to the exhaust line and compare it with the original system. Techno-economic parameters were analyzed for two different configurations: one with the maximum BESS size and another with the maximum Primary Energy Savings (PES). The results demonstrate that the incorporation of the ORC plant yields several notable outcomes. While there is a slight increase in the Simple Payback Period (SPB) and Net Present Value (NPV) in both configurations, the magnitudes of these increases are relatively low. In contrast, the introduction of the ORC cycle leads to substantial reductions in carbon dioxide (CO2) emissions and significant improvements in Primary Energy Savings (PES). For the Max PES configuration, a remarkable 25% reduction in CO2 emissions and a substantial 22% improvement in PES were observed. To further investigate the system's response to varying economic conditions, a sensitivity analysis was performed. The analysis considered the effects of changes in the cost of battery, Combined Heat and Power (CHP) unit, ORC components, electricity, and fuel on the system's Simple Payback Period (SPB) and Net Present Value (NPV). The results revealed that the sensitivity of the Max PES configuration to economic parameters was higher compared to the Max BESS system. These findings highlight the considerable advantages of integrating an ORC plant into a cogeneration system with a BESS. The significant reduction in CO2 emissions and the noteworthy improvement in Primary Energy Savings (PES) outweigh the relatively minor negative impact on the Simple Payback Period (SPB) and Net Present Value (NPV). This integrated configuration demonstrates both environmental benefits and energy efficiency improvements. The results provide valuable insights for the design, optimization, and decision-making processes involved in the implementation of cogeneration systems with energy storage, promoting sustainable and cost-effective solutions. • Integration of an ORC plant into a modular cogeneration system with a BESS. • Techno-economic analysis for two CHP configurations: max BESS size and max PES. • ORC cycle introduction significantly cut CO2 emissions while SPB and NPV increases. • Max PES configuration exhibited 25% CO2 reduction and a 22% PES improvement. • Sensitivity analysis revealed system response to component and energy cost changes. [ABSTRACT FROM AUTHOR]

Integrated polygeneration systems have emerged as an effective and sustainable solution for maximizing the utilization of renewable fuels, and offer multiple economic and environmental advantages. This work proposes a new polygeneration system to produce hydrogen, heat, and power based on integrating biomass gasification, solid oxide fuel cell, gas turbine, organic Rankine cycle, and supercritical CO 2 Brayton cycle. The proposed system is simulated in Aspen Plus, and the performance is evaluated based on energy, exergy, economic, and emergy analyses. The overall energy and exergy efficiencies attain 76.82% and 60.64%, respectively. The levelized cost of hydrogen is 4.06 $/kg, comparable to those reported in the literature, and the yearly income generated through revenue sales of hydrogen, heat, and electricity can reach up to 58.42 M$. The emergy analysis showed that the system depends on purchased inputs but can efficiently use the available resources to generate valuable products. The hybrid system has low environmental impacts in the long term. It can serve as a low-cost, low-carbon, and profitable polygeneration system of hydrogen, heat, and power, with a good quality of energy conversion. • A conceptual system is proposed to produce hydrogen, heat, and power and evaluated. • The biomass gasification, SOFC, gas turbine, ORC, and supercritical CO 2 Brayton cycle are integrated. • The emergy analysis is performed to assess the system's sustainability and environmental effect. • The influence of multiple parameters on the performance of the system is clarified. • The system's overall exergy efficiency is 60.87%, and the levelized cost of hydrogen is 4.06 $/kg. [ABSTRACT FROM AUTHOR]

This work reports on the study of flame characteristics and laminar flame speeds (LFS) of sustainable aviation fuel (SAF) and conventional fuels (Jet-A1 and JP-5) premixed with air at elevated pressures. The experimental facilities included the constant volume combustion chamber (CVCC) and used a shadowgraph system to quantify the LFS. The effects of equivalence ratio and ambient pressure on the flame speeds were examined by varying the initial pressure 1, 2, and 3 bar (in addition to 0.1 and 0.5 bar for flame morphology) and the equivalence ratio from 0.8 to 1.8 at 423 K of temperature. The results indicated that JP-5 had the highest LFS value (90.1 cm/s) compared to the other two fuels. Jet-A1 and SAF have the same LFS result, which is less than a 15% difference, excluding the high equivalence ratio and initial pressure. The higher the initial pressure, the lower the LFS for all conditions. The equivalence ratio of 1.2 resulted in the highest LFSs. In addition, in order to understand the laminar flame speed of SAF and its combustion chemical kinetics. Therefore, the sensitivity analysis of SAF was performed. Results indicated that the chain branching reaction (O 2 + H OH + O) positively enhanced flame speed as equivalence ratios (φ) increased, while chain propagation reactions, notably O 2 + H HO 2 (+M), had a negative effect on flame speed, particularly diminishing their influence at higher φ due to limited oxygen availability. [Display omitted] • Laminar flame speeds of SAF and conventional fuels are studied. • Experimental results are quite consistent with those of numerical simulation. • Highest laminar flame speed is located at the equivalence ratio of 1.2 [ABSTRACT FROM AUTHOR]

Heating buildings have a huge heat capacity and have abilities to accept fluctuating heat supply from renewables. Creating a low-order scalable model that describes the thermal dynamics of coupled heat transfer, charge, and discharge processes among heating buildings is essential to unlocking the thermal storage potential of building mass and achieving space heating decarbonization. White-box models tend to be too complex and black-box models lack scalability. Thus, a low-order gray-box model with uncertain lumped parameters is proposed based on comprehensive heat transfer analyses and necessary assumptions. Thermal mass in heating buildings is divided into four independent heat storage units. The building envelope that can be heated directly or not is regarded as a different heat storage unit. To reduce the search space dimension of model identification, the decision variables are downsized to essential model parameters based on the sensitivity analysis. Moreover, a progressive dimension reduction method is introduced to further improve the probability that the optimization algorithm converges to a global minimum. Case studies show that the calibrated model with global optimal solutions of uncertain model parameters has a good performance on the prediction of the average temperature of indoor air, the average absolute error is only 0.1 °C. • A low-order gray-box model with uncertain parameters is built for heating buildings. • Thermal mass in heating buildings is divided into four independent heat storage units. • Uncertain model parameters can be reduced based on the sensitivity analysis. • The progressive dimension reduction method is proposed to find a global optimum. [ABSTRACT FROM AUTHOR]

Open adsorption process of gas mixtures involves complex heat and mass transfer mechanisms. Understanding of the physical mechanisms and their impacts on the adsorption process from gas mixtures is vital. In this study, a detailed analysis of an open CO 2 adsorption from CO 2 /N 2 mixtures using zeolite 13X-APG was investigated. Key physical mechanisms (unsteady, diffusive, convective, and component source, etc.) involved were scrutinized, and their order of magnitudes relative to the system energy complex were determined. The influences of these physical mechanisms on the equilibrium and dynamic nature throughout the capture were quantified. The validated computational fluid dynamics (CFD) simulations for the same adsorption domain were carried out to verify the rationality of the scaling method. The adsorption time, t ad ; the maximum average temperature, T ‾ max ; CO 2 removal rate, R re,CO2 ; equilibrium pressure drop, Δ P eq on different scale parameters and their sensitivity were investigated. The maximum relative sensitivity to porosity was found to be −0.945, 0.0235, −0.357, and −5.33 for t ad , T ‾ m a x , R re,CO2 and Δ P eq , respectively. It is observed that heat transfer by the conduction mechanism inside the bed significantly influences all scale parameters, except for Δ P eq. This work will contribute to a better understanding of the atmospheric CO 2 adsorption and provide guidance for the design optimization. [Display omitted] • System description of open CO 2 adsorption process is carried out. • Mechanisms of an open CO 2 adsorption process are analyzed by scaling method. • Reliability of scaling analysis is verified by comparison with CFD simulation. • Key bed parameters for adsorption performance are obtained by sensitivity analysis. • Increase in thermal conductivity has positive effect on performance except for ΔP eq. [ABSTRACT FROM AUTHOR]

In addressing the challenges associated with accurately predicting the efficiency of wave energy converters, particularly due to the intricate nature of certain parameters resistant to straightforward analytical derivation, this paper proposes a novel semi-parameter analytical dynamic model. This model not only effectively overcomes the inherent challenges but also undergoes rigorous validation through experimental testing. Building upon this foundation, we employ response surface methodology to develop a predictive framework for evaluating the energy harvesting efficiency of our innovative twin-turbine wave energy converter. The objectives of this predictive model are twofold: firstly, to eliminate the need for intricate numerical integration of dynamic differential equations governing the converter's behaviour; secondly, to distil the energy harvesting performance prediction model from intricate design parameters. This streamlined model facilitates an incisive analysis of design parameter sensitivities and interactive influences. Validation involves analysis of variance (ANOVA) and comparison against the semi-parameter analytical dynamic model. The innovative aspect of our wave energy converter lies in the creative use of twin turbines connected through a pulley-driven transmission system. This unique configuration significantly enhances generator rotational speed, enabling efficient power take-off mechanism placement within the buoy. The pulley-driven transmission system not only allows easy adjustment of the transmission ratio but also heightens generator efficiency by aligning its operational speed within the desired rated speed range. Additionally, the transmission system adeptly absorbs load fluctuations, impacts and vibrations from oceanic waves, enhancing device robustness and longevity. The streamlined design also facilitates maintenance access, affirming practicality. The harvested power values calculated by the response surface method (RSM) prediction model verified by the semi-parameter analytical model for all 30 data runs (II). [Display omitted] • A novel dual turbine wheel wave energy converter (WEC) featured with the rotational speed amplification of its generator. • A semi-parameter analytical model for prediction of the harvesting performance of the WEC validated by experiments. • The response surface method model validated by the analytical model and the analysis of variance. • The sensitivity of the design parameters and the interaction effects. • The design optimized for the maximum output power in a specific ocean area. [ABSTRACT FROM AUTHOR]

Mitigating climate change requires a variety of energy technologies and energy simulation approaches to evaluate the best possible system structures. Screening whether novel technologies are a viable solution for a particular country within a cost-optimised system setup is usually simulation- and time-intensive. This study introduces the novel add-on optimisation tool EP-ALISON-LUT for use in combination with EnergyPLAN applied to the test case of wave power in the case of Seychelles in 2030 and 2050 within a structured sensitivity analysis. The tool enables a high number of possible system setups and scenarios, including the import and domestic production of electricity-based fuels, to be modelled, allowing for an in-depth view of the system impacts of integrating wave power. The results indicate a limited role for wave power due to its relatively low yield, especially in 2030. However, in 2050, up to 500 MW of wave power capacity is possible with a lower or similar levelised cost of final energy compared to the reference scenario in 2019, which can benefit the diversification of the power generation portfolio. Thus, this novel tool is fast and effective in technology screening studies requiring a fast optimisation algorithm. • Viability screening for novel renewable energy technologies is needed. • Novel optimisation tool based on and in combination with EnergyPLAN is presented. • Structured sensitivity analysis of wave power on the case of Seychelles is made. • Wave power can hardly challenge low-cost solar photovoltaics in the Sunbelt region. • Structured sensitivity analysis showed options for portfolio diversification. [ABSTRACT FROM AUTHOR]

The efficiency of Concentrated Solar Power (CSP) plants strongly depends on steam condensation temperatures. Current cooling systems, either wet (water-cooled) or dry (air-cooled), present trade-offs. Wet cooling towers (WCT) optimize performance but raise concerns due to substantial water usage, especially in water-scarce prone locations of CSP plants. Dry cooling conserves water but sacrifices efficiency, specially during high ambient temperatures, coinciding with peak electricity demand. A potential compromise is a combined cooling system, integrating wet and dry methods, offering lower water consumption, improved efficiency and flexibility. Incorporating such systems into CSP plants is of considerable interest, aiming to optimize operations under diverse conditions. This research focuses on the first step towards this goal; developing static models for WCTs. Two approaches, Poppe and Artificial Neural Networks (ANN), are developed and thoroughly compared in terms of prediction capabilities, experimental and instrumentation requirements, sensitivity analysis, execution time, implementation and scalability. Both approaches have proven to be reliable, with Poppe providing better results, based on MAPE, for the outlet temperature and water consumption (0.87 % and 3.74 %, respectively) compared to a cascade-forward ANN model (1.82 % and 5.21 %, respectively). However, for the target application, the better execution time favours the use of ANNs. • Modelling of a Wet Cooling Tower integrated in a novel combined cooling system. • Thorough comparison between artificial neural networks and Poppe physical model. • Models validated with experimental data from a pilot plant. • Sensitivity analysis as verification tool of model quality. • Analysis of model performance as a function of data availability. [ABSTRACT FROM AUTHOR]

The micro-thermal power generation presents several challenges including reaction instability, large heat loss, and energy conversion. So, a micro-planar with multi-hole baffle for micro power generation is proposed with C 3 H 8 /CH 4 blended and reaction sensitivity analyzed. Effects of fuel sensitivities, baffle settings, operating conditions on combustion characteristics and performance of thermal are analyzed. Experimental results revealed the thermal performance of H 2 -fueled combustion with blended CH 4 and C 3 H 8 can be improved, while the H 2 /CH 4 /air burning obtains a higher improvement by promoting flame anchoring and reducing heat losses. Thermal reflux and high temperature zones can be generated at appropriate baffle positions and hole sizes to enhance the operating micro-combustor performance. Optimized baffles and CH 4 blending ratios significantly improved combustor output power and system efficiency. For instance, the maximum mean radiation temperature of 1347 K can be obtained in combustor C8-0.8 at m c = 25 % and m f = 4.08 × 10−5 kg/s. Meanwhile, micro-thermophotovoltaic (MTPV) system equipped with InGaAsSb cell can achieve a 3.4 W electrical power output and a system efficiency of 3.5 %. It provides solutions for recovering energy from exhaust gases and improving energy efficiency to develop efficient and sustainable micro-energy sources. [Display omitted] • Addition of CH 4 and C 3 H 8 to H 2 -fuel combustion is experimentally and numerically investigated. • H 2 -fueled characterization of combustion with CH 4 /C 3 H 8 blended and sensitivity analysis are presented. • The coupling of exhaust gas heat recovery, blended fuels, and baffle is employed to improve energy efficiency. • Energy efficiency and electrical output of micro-thermophotovoltaic are calculated and analyzed. [ABSTRACT FROM AUTHOR]

The increasing demand for eco-friendly and renewable energy has positioned hydrogen as a viable solution for global energy and environmental challenges. In-situ combustion gasification of heavy oil reservoirs offers potential for large-scale hydrogen production, injecting steam and air (or alternative gases) to trigger complex chemical reactions leading to hydrogen and syngas generation. However, a research gap exists due to the lack of a comprehensive numerical model for accurately simulating hydrogen production under SARA-based (saturate, aromatic, resin, asphaltene) hydrocarbon characterization in experiments and field study. This study addresses the gap by investigating in-situ combustion gasification for hydrogen generation in heavy oil reservoirs. A novel model based on SARA characterization is proposed, showing fidelity with experimental and numerical results. Different injection strategies, like pure oxygen and CO 2 , impact hydrogen production. Oxygen injection yields less hydrogen than air injection, highlighting the importance of oxygen control. CO 2 injection reduces hydrogen but aids carbon management. The oxygen to nitrogen ratio (61/39) demonstrates the highest hydrogen-to-syngas ratio. Practical implementation requires economic feasibility, operational practicality, safety, and environmental considerations. This study advances in-situ combustion gasification technology, facilitating efficient and sustainable hydrogen generation from heavy oil reservoirs. [Display omitted] • A new modeling approach based on SARA characterization is presented. • The theoretical results are validated with the experimental and numerical studies. • Impact of temperature on hydrogen yields at different stages are evaluated. • Effects of different gas injection schemes on the hydrogen yield are investigated. • Optimal gas mix ratio maximizes hydrogen yield, favors high hydrogen-to-syngas ratio. [ABSTRACT FROM AUTHOR]

This study delved into the integration of biomass gas and natural gas within a Combined Cooling, Heating, and Power (CCHP) system. A Semi-Isolated Green Energy CCHP (SIGE-CCHP) model was devised to scrutinize the performance of co-firing equipment across diverse optimization objectives, while manipulating the proportions of natural gas and biomass gas as inputs. Findings revealed that escalating the share of biomass gas led to a reduction in carbon emissions but triggered an escalation in operational and maintenance costs. However, at an optimal mixing ratio of 1:1, carbon emissions exhibited marginal increments, coupled with a substantial decrease in operational and maintenance expenses. Notably, when prioritizing operational and maintenance costs, the system exhibited optimal performance, resulting in a notable 26.76 % cost reduction. Conversely, when prioritizing carbon emissions, the system metamorphosed into a carbon sequestration entity, with a maximal capacity to absorb 2021.86 kg of carbon dioxide. This study furnishes theoretical underpinnings for optimizing the operation of co-firing equipment, augmented by a sensitivity analysis aimed at intuitively elucidating the repercussions of varying mixing ratios on the system. • Created a model for SIGE-CCHP to investigate the co-firing of biomass and natural gas at uncertain ratios. • Found that higher biomass gas ratios effectively lower carbon emissions in CCHP systems. • Identified a 1:1 biomass to natural gas ratio as optimal for balancing emissions and operational costs. • Achieved substantial cost savings in operation and maintenance, peaking at a 26.76 % reduction. • Presented the CCHP system's potential as a carbon sink, with a high carbon dioxide absorption capacity. [ABSTRACT FROM AUTHOR]

The optimal capacity configuration of combined wind-storage systems (CWSSs) serves as a foundation and premise for building new electricity system. This paper proposes a novel capacity configuration model of CWSS. The model simultaneously considers economy, stability and low carbon by respectively minimizing Levelized cost of electricity (LCOE) and maximizing effective load carrying capacity (ELCC). The multi-objective optimization model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II). And Multi-Attributive Border Approximation Area Comparison (MABAC) method is used to score the alternative solutions on the Pareto front and make decisions under different scenarios. The results of the case study show that: (1) The evaluation of the ELCC for CWSS can quantify its ability to replace the installed capacity of thermal power, thus greatly reducing the uncertainty of wind power output. (2) Under low-carbon scenario, economic scenario and standard scenario, the results of optimal capacity configuration for the CWSS show that LCOE and ELCC are negatively correlated. When the weights of ELCC and LCOE are equal, the three goals of economy, low carbon and safety and stability are achieved. (3) The results of a single sensitivity analysis on LCOE show that the sensitivity coefficient of electricity generation is the highest, exerting the greatest impact on the capacity configuration decision of the CWSS. A 20 % increase in power generation can reduce LCOE by 0.1203RMB/kWh. This paper provides energy planning recommendations for decision-makers in CWSSs, contributing to the development of a reliable new electricity system. • A multi-objective optimization of CWSSs considering ELCC and LCOE. • Multi-objective optimization model is established based on NSGA-II and MABAC. • The model of LCOE based on ELCC is proposed. • The model is presented to achieve the optimal capacity configuration of the CWSS. [ABSTRACT FROM AUTHOR]

Battery electric buses (BEBs) are appealing to transit operators for their elevated comfort, low noise, and zero tailpipe emissions. However, the degradation of BEB batteries over time challenges their performance and necessitates infrastructure adjustments. This study develops a generic multi-stage optimization model for BEB systems. The model addresses the dynamic influence of BEB battery degradation rates on the optimal BEB system configuration and operation, including component sizing, charging infrastructure allocation, BEB charging schedule, and battery replacement throughout the BEB usage lifecycle. A surrogate model-based space mapping (SMSM) algorithm is employed to address the inherent nonlinearity of incorporating battery degradation rates within the developed model. The model is tested on a real-world, multi-hub transit network, and the results highlight significant implications of battery degradation on the optimal spatiotemporal allocation of charging infrastructure, charging schedules, and battery replacement decisions throughout the 12-year BEB usage lifecycle. Sensitivity analysis highlights the influence of operational conditions on total system cost, indicating a 16.3 % increase in cost with a 50 % rise in both energy consumption rates and time-of-use (ToU) tariffs. Overall, the proposed model is a valuable decision-making tool for transit operators navigating BEB transit system planning. • We developed a generic multi-stage spatiotemporal optimization for Battery Electric Bus (BEB) system configuration. • The model addresses the influence of BEB battery degradation on the optimal system configuration. • Battery degradation significantly impacts BEB system configuration and operation throughout the usage lifecycle. • Additional charging infrastructure, battery replacement, and charging scheduling adjustments are required. • System configuration models must consider the 12-year usage lifecycle of BEBs. [ABSTRACT FROM AUTHOR]

The present work deals with the dynamic modelling of a small-scale thermo-hydraulic engine, designed for residential application. Comparing to Organic Rankine systems, the CAPILI cycle uses a hydraulic motor instead of a turbine. The solar activated CAPILI engine produces electricity through a liquid piston oscillating between two reservoirs connected alternatively to a high pressure evaporator and a low pressure condenser. The heat rejected by the condenser is discharged in the ground through geothermal probes at 13 °C while the solar panels provide heat at about 70 °C. The liquid piston drives a hydraulic motor coupled to an electrical generator to produce electricity. Based on these temperature conditions, the dynamic modelling approach undertaken in the study enables a sensitivity analysis of the most impacting parameters of the CAPILI system. In particular, the study focuses on the volume capacity and the filling ratio of transfer cylinders, the switching -balancing- volume of the cycle as well as the electrical load. The results highlight the effects of each investigated parameters on the whole behaviour of the simulated CAPILI system. The best performance of the investigated CAPILI system operating under the given operating conditions, highlights a thermal efficiency of 9.14 % and almost 55 % for the exergy efficiency. [Display omitted] • The new CAPILI energy system is investigated for a residential application. • The thermohydraulic system is activated with solar thermal energy to produce electricity. • Dynamic modelling and sensitivity analysis are conducted to assess the performance of the patented CAPILI system. • Sensitivity analysis includes volume capacity, filling ratio, switching volumes and the electrical loads. • The best thermodynamic performance we evaluated at 9.14% for the thermal efficiency and almost 55% for exergy performance. [ABSTRACT FROM AUTHOR]

Liquid hydrogen storage is one of the effective hydrogen storage methods due to its high density of 70.8 kg/m3 compared to gaseous hydrogen of 0.0838 kg/m3 at atmospheric pressure. Liquid hydrogen requires cryogenic storage technology, which minimizes heat flux by stacking multiple insulation layers in a high vacuum (10−1–10−5 Pa). However, large-scale tanks use a medium vacuum (100–10−1 Pa) to reduce maintenance expenses. Solid insulation is applied to prevent liquefaction of residual gas up to 150 K, followed by the stacking of multilayer insulation (MLI) with a vapor−cooling shield (VCS) to minimize the insulation thickness. In this study, a numerical model was developed to calculate the heat flux of a storage tank based on the physical tank shape. The insulation thickness was also optimized for two insulation systems (solid insulation + MLI and solid insulation + MLI + VCS). Optimal VCS placement on solid insulation, determined through sensitivity analysis, reduces 64.2 % of MLI layers under 5 Pa vacuum pressure. Total insulation thickness reduction of 51.4 % at 1 Pa vacuum pressure was obtained based on a hydrogen storage tank volume of 4200 m3. The effects of insulation thickness reduction are remained even when the vacuum pressure is increased to 5 Pa. • Modified McIntosh equation was developed for reflecting physical tank shape. • Insulation thickness of liquid hydrogen storage tank was optimized. • VCS location is determined above the perlite by sensitivity analysis. • The insulation thickness decreases 51.4 % at medium vacuum with MLI and VCS. • VCS has a high efficiency at high vacuum pressure, reducing MLI layers by 64.2 % at 5 Pa. [ABSTRACT FROM AUTHOR]

The solar radiation received by PV panels is often affected by neighboring shading objects, such as the surrounding terrain and adjacent buildings. Based on the dynamic spatial relationships among shading objects, the PV panel, and the sun, this research develops a method to quantify shading effects on solar radiation received by PV panels due to obstructions at different positions. By calculating the shading rates of direct and diffuse radiation, the sensitivities of PV panel's shading effects to various positions of 1080 shading blocks at sky dome are determined. It was found that the most sensitive areas of the PV panel's solar radiation shading rates were in the south, but their altitude angles varied greatly. With latitudes of 0°, 30°, and 60°, the maximum sensitivities of the direct and the diffuse radiation shading rates were found to be 1.21 % and 0.32 %, 1.69 % and 0.64 %, and 1.88 % and 1.05 %, respectively. The research offers a new approach to easily identify different sensitive shading areas of the direct and diffuse shading rates, which could provide theoretical supports for PV system planning, site selection, and layout optimization under diverse shading conditions. • A method to quantify shading effects on solar radiation of PV panels. • Shading effects from obstructions at different positions are determined. • Identify the most sensitive area affecting radiation shading rates of PV panels. • Radiation shading rate is affected by several factors, latitude, tilt angle, etc. [ABSTRACT FROM AUTHOR]

Evaluation of the performance of hydrokinetic turbines is an essential subject in the renewable energy field. In this regard, advanced machine learning methods including hybrid CatBoost and standalone Catboost and linear regression were used for the first time for the straight channel data and channel bend data. The hybrid models were developed using whale optimization (WO), grey wolf optimization (GWO), and Bayesian optimization (BO) algorithms. Sensitivity analysis was also conducted to find the most influential parameters in the best model. Results showed that the hybrid models can predict the goal parameters, including the coefficient of the power (C P), the maximum value of the coefficient of the power (C Pmax), and the tip speed ratio corresponding to the C pmax (TSR max) better than the standalone models. Among the models, GWO-CB is the best model, even in the k-fold cross-validation scenario. The R2 value of the GWO-CB reached 0.97, 0.94, and 0.84 for the C P , C Pmax , and TSR max , respectively. Among the effective parameters, the lateral position of the returning blade deflector and the number of stages had the maximum and minimum effects on the best model, respectively. The effect of the position of the turbines in the channel bend, which was considered in this research for the first time, was significant in the models. • Soft computing methods were used to evaluate the performance of the Savonius hydrokinetic turbines. • The optimization algorithms improve the accuracy of the standalone models. • Hybridized CatBoost model by Grey Wolf Optimization is the best model. • Lateral position of returning blade had the maximum effects on the best model. • Number of stages had the minimum effects on the best model. [ABSTRACT FROM AUTHOR]

A gradient cathode catalyst layer (CCL) with promoted mass transfer and Pt utilization could enhance the output performance of proton exchange membrane fuel cells (PEMFCs). Since operating conditions affect reaction and mass transfer rate inside the PEMFC, the gradient CCL can only exert its full potential within a narrow operating range. Assessing the impact of gradients, operating conditions, and their interactions on output performance is vital to maximizing the benefits of gradient CCLs. Therefore, a global sensitivity analysis was conducted using Sobol's approach based on a one-dimensional, two-phase, non-isothermal PEMFC model. Five parameters, including Pt gradient (α), ionomer gradient (β), temperature (T), anode relative humidity (RH a), and cathode relative humidity (RH c), were selected as model inputs. The results show that RH c has the most significant impact on output performance among the operating parameters. The advantage provided by the gradient design is presented in mass transport-constrained situations. The impact of α on current density is more significant than β. Meanwhile, regarding the interaction effect of operating conditions and gradients, α and RH c provide the most significant pairwise interaction effect. Finally, the mechanisms underlying these interactions were analyzed, and some options were proposed to maximize the benefits of gradient CCLs. • Impacts of parameters and their interactions on cell performance are assessed. • Gradient designs provide advantages in mass transport-constrained situations. • Cathode relative humidity is the most significant operating parameter. • Interactions between operating conditions and Pt gradient were investigated. [ABSTRACT FROM AUTHOR]

A solid oxide fuel cell (SOFC) hybrid system with an inter-cooled-recuperated gas turbine(ICRGT) and organic Rankine cycle (ORC) was designed to address exergy destruction in the burner of a conventional SOFC and gas turbine (GT) system. In unfired mode of this system, SOFC acts as the GT heater and reheater instead of burners; burners are used only to aid load regulation in fired mode. In unfired mode, the system electrical efficiency is 69.86 % and the overall efficiency is 82.22 %. The total exergy destruction of the system is 21.93 % lower than that in fired mode by exergy analysis, which indicates the effectiveness of reducing afterburner exergy destruction, and the superior energy efficiency of this mode. For the design analysis, we analyzed the influence of different factors on the system, including the number of SOFC modules, turbine outlet temperature, burner exhaust into the turbine, oxidant utilization of SOFC cathode(OUC), and fuel utilization of SOFC anode(FUA) on the system. For the sensitivity analysis, we determined the variation rules of the components and system parameters for fuel proportions of 70–105 %. This work provides a reference for optimizing the design and operation of SOFC-GT and other SOFC hybrid systems. • An SOFC-ICRGT-ORC system is designed with an electrical efficiency of 69.86 %. • Multistage SOFC modules are proposed to reduce exergy destruction by 21.93 %. • Intercooling and regenerative cycle ensures high efficiency of partial-fuel condition. • Energy utilization is improved by deep coupling of ORC, SOFC, and ICRGT system. [ABSTRACT FROM AUTHOR]

The application of phase change materials (PCMs) in renewable energy storage and building temperature regulation is considered an effective method to reduce the use of fossil fuels and carbon emissions. However, issues concerning the utilization of PCMs in high-humidity environments remain to be addressed to fill the gap in real-world applications. This paper establishes a simplified novel two-zone model, comprehensively considering the natural convection on the surface of the PCP in high-humidity environments, as well as the formation, movement, and evaporation of water films on the surface. Subsequently, numerical solutions and experimental validation are conducted, and the influence of high-humidity environments on the heat and moisture transfer is elucidated. The results indicate that a high-humidity environment can significantly suppress the sensible heat transfer with a ratio range of latent heat to sensible heat typically around 1–4. Furthermore, the increase in the height of PCP leads to an increase in the proportion of sensible heat transfer. On the contrary, increasing the thermal conductivity of the PCM can amplify the latent heat transfer. The results can help further study the thermal storage and the dehumidification potential of phase change units in application on a large scale under high humidity conditions. [Display omitted] • A simplified model for PCM thermal storage prediction in high humidity environments is established. • The variation of water film on the PCP surface is considered. • Sensitivity analysis for PCM thermal storage in high humidity environments is investigated. • A high-humidity environment can lead to a ratio range of latent heat to sensible heat typically around 1–4. [ABSTRACT FROM AUTHOR]

Given the importance of energy and its role in sustainable gas production, paying special attention to its consumption indicators in all industries, especially oil & gas is very important. The energy consumption index parameter, which equals the ratio of energy consumption to gas production, is one of the most significant indicators of the gas refinery. Artificial intelligence can be used to evaluate the effects of different parameters and find the factors influencing energy performance. Important variables such as inlet gas from various sources, the energy consumption of each part, and the amount of waste refinery gas are collected on different days of the year, then using the least square support vector machine method and networking and sensitivity analysis of the impact of each variable separately. The role of each in this parameter is determined so that it can be used to provide appropriate solutions to reduce energy consumption and the energy consumption index parameter for sustainable gas production. According to the results of this study, the steam unit has a 14 % impact on the energy consumption index, and the gas sweetening unit has an 18 % impact on gas consumption. So, more research on the optimization of these two units is necessary. • Annual energy index and consumption were analysed for Fajr Jam Gas Refinery Company. • SVM was exploited for the analysis. • Steam unit significantly influences energy consumption index. • Gas sweetening unit significantly impacts gas consumption. • For both energy index and consumption R2 was >0.98 [ABSTRACT FROM AUTHOR]

To boost the deployment of all offshore renewable energy technologies, it is fundamental to adopt convenient long-term strategic operation and maintenance (O&M) decisions. Due to the lack of experience and reliable information, performing extensive sensitivity analysis is a key factor for supporting strategic O&M decision-making. By evaluating various scenarios, sensitivity analyses provide valuable insights to identify critical factors and enhance decision confidence. To that end, the development of computationally efficient O&M models, where accessibility, availability, energy, and economic aspects are adequately articulated is crucial. Simulation-based O&M models, i.e. based on Monte Carlo methods, have been widely used to incorporate those fours aspects. However, the computational burden of simulation-based O&M models is prohibitive, limiting the feasibility of conducting extensive sensitivity analyses. In view of this, this study presents a computationally efficient analytical O&M model based on Markov Chains. This analytical O&M model is compared with two case studies presented in the literature, where simulation-based O&M models are employed, studying a floating offshore wind and a wave energy farm. Results demonstrate that the analytical O&M model achieves the same level of fidelity as simulation-based models (within 10% deviation), while reducing the computational burden by at least five orders of magnitude. • A computationally fast O&M model is developed enabling extensive sensitivity study. • Accessibility, availability, energy and cost aspects are included with Markov chains. • Comparison is made with Monte Carlo O&M models in offshore wind and wave studies. • The same level of fidelity is achieved as with the Monte Carlo O&M models. • The computational burden is reduced by at least five orders of magnitude. [ABSTRACT FROM AUTHOR]

Clean energy development for multiple productions interests many countries and companies. Hence, this paper presents an innovative solar-driven trigeneration arrangement equipped with a thermal energy storage unit (TESU); the whole system comprises parabolic dish collectors, a modified Rankine cycle, and a double-effect absorption chiller. The system is designed to enhance the integrity performance compared to previous designs. Heat loss recovery is performed using an internal heat exchanger to improve the Rankine cycle's performance. The operating framework of the arrangement contains three steady modes: solar mode associated with the state without applying TESU, storage mode corresponding to the state without solar energy using TESU, and solar-storage mode capable of storing energy in addition to launching the combined cycles. The prediction of the sensitivity of the net output power, heating capacity, input energy, power to heating and cooling ratios, and energy and exergy efficiencies is conducted in solar mode. Furthermore, the thermoeconomic analysis is carried out in solar mode to determine the equipment's financial aspect. Referring to the results, the overall energy efficiencies of the modes introduced before are equal to 97.23%, 81.98%, and 40.23%, and the corresponding exergy efficiencies are 15.53%, 14.47%, and 8.58%. Also, the exergoeconomic factor of the solar subsystem is 70.77%. [Display omitted] • Modeling a CCHP system based on a modified Rankine cycle, a double absorption chiller, parabolic dish collectors and a TSU. • Improving the trigeneration system's performance by eliminating the examined models' weaknesses. • Analyzing solar dish collector systems' thermodynamic and exergoeconomic. [ABSTRACT FROM AUTHOR]

The hybridization of solar and biomass energy systems is a promising technology for mitigating the issues of energy generation-related greenhouse gas emissions and high energy prices. The global warming potential and economic feasibility of a hybrid solar-bioenergy system, comprised of a concentrated solar tower, biomass gasifier, thermal storage, and combined cycle gas turbine, have been evaluated by using life cycle assessment and cost benefit analysis, respectively. Sensitivity analysis is carried out to identify the hotspots of costs and emissions. The net present worth of the proposed system at the 30th year was calculated to be about €–0.7 billion. There are two suggestions to enhance the economic viability of the system, allowing for a payback period of less than 10 years. The first suggestion involves reducing the O&M cost of the system by 19% at 43.9 €/MWh, and the second suggestion entails increasing the overall efficiency of the system by 20%. This system can save 787.7 kg of CO 2 -eq/ton waste-wood and generate a total of about 0.8 million MWh of electricity each year. The findings provide scientific evidence for the design and deployment of the hybrid technology to enhance energy security, while reducing carbon emissions. Overall, this study highlights the potential benefits of hybrid solar-bioenergy systems and encourages the adoption of sustainable energy practices for a greener future. • Carbon footprint and economics of CSTGB-based electricity generation was explored • The CSTGB system generated 0.8 million MWh electricity annually • The CSTGB system saved 787.7 kgCO 2 -eq/ton waste-wood • CCS had the most significant impact on the carbon footprint • Operation and maintenance cost significantly affected economic viability [ABSTRACT FROM AUTHOR]

Drying is an energy intensive process, which takes a big portion of global energy consumption. The innovation of energy-saving drying techniques are crucial for the sustainable development. To achieve energy saving of evaporative drying, exergy destructions and losses in the dryers should be located. In this study, the drying process is divided into three subprocesses, i.e., heat transfer, moisture evaporation and diffusion, and drying exhaust emission. Thermodynamic models of dryers are developed, and performance of evaporative dryers, including rotary, solar, spouted bed, etc. Are analyzed and compared. The exergy consumption rate, i.e., the exergy consumption rate to dehydrate 1 kg water, is defined and used to evaluate the energy utilization of dryers. The exergy consumption rate of dryers ranges from 330 kJ (kg H 2 O)−1 to 10,490 kJ (kg H 2 O)−1, which is mainly related to the energy consumption rate and energy grade. The irreversibility of the three subprocesses of dryers are compared, e.g., for the rotary dryer with hot air at 300 °C as heat source, the exergy loss and destruction of the subprocesses are 415.7, 451.3, and 293.4 kJ (kg H 2 O) −1, respectively. The results of this study can provide guidance to enhance the energy utilization efficiency of evaporation dryers. • The exergy consumption rate is defined to evaluate drying energy efficiency. • Irreversibility of drying subprocesses are assessed with exergy analysis model. • The exergy consumption rate of dryers ranges from 330 to 10,490 kJ (kg H 2 O)−1. • Exergy consumption rate is related to energy consumption rate and energy grade. [ABSTRACT FROM AUTHOR]

Pore-scale asphaltene deposition not only affects reservoir permeability and hydrocarbon production efficiency but also impacts reservoirs performance used in other applications, such as geothermal and CO2 storage. The limited availability of comprehensive experimental data has impeded the development of precise predictive models for deposition behavior. This study employs real-time microfluidic experiments and a Lattice Boltzmann-Immersed Boundary numerical model to investigate pore-scale asphaltene deposition. Experimental results directly observe porosity reduction in diverse pore geometries, offering a detailed explanation of asphaltene deposition phenomena. The LB-IB model accurately replicates complex deposition behavior with over 93% agreement with experimental results across various geometries, using only two adjustable parameters. This enables precise predictions of asphaltene phase behavior in porous media. Sensitivity analyses explore the influence of reservoir geometry, flow rates, and asphaltene concentrations on deposition behavior, underscoring their significant impact on porosity reduction and emphasizing the importance of understanding asphaltene deposition. Additionally, the study compares the proposed model to the Wang-Civan model, showcasing its superior accuracy in capturing deposition mechanisms, and suggests modifications to include an upper limit for deposition when throats are entirely filled with asphaltene. This study improves our understanding of pore-scale asphaltene deposition, aiding in optimizing hydrocarbon extraction practices and industry efficiency. [Display omitted] • A new model can predict asphaltene deposition behavior beyond literature limitations • Analyzing pore geometries, injection rates, and asphaltene concentrations to optimize • An innovative microfluidic setup to observe asphaltene-induced porosity reduction • Investigating and evaluating LB-IB model prediction using experimental data • Comparing experimental results with observations in the literature [ABSTRACT FROM AUTHOR]

Supercritical water gasification (SCWG) is a potential clean technology for utilizing solid fuel or waste without producing gaseous pollution. Efficient energy integration and recovery strategies play an essential role in the endothermal gasification process. Previous approaches have only focused on the heat source or heat transfer with very little attention to the utilization of the abundant supercritical water in the product gas. This paper proposes an innovative series design for heat integration in the SCWG system while taking the gasification process into account. Notably, the first-stage oxidized hot fluid containing abundant water is directly utilized as the gasification agent for the second-stage gasification reactor. The thermodynamic analysis shows that the cold gas and exergy efficiency in the two-stage series system is 17.4% and 15.0% higher than in the previous single-stage gasification reactor. The sensitivity analysis illustrates that increasing series stages and feedstock concentration while decreasing oxidation proportion and the agent-slurry ratio can improve the overall system efficiency by decreasing the demand for oxygen, power consumption, and heat transfer rate. The series design should offer an innovative and practical approach for the heat supply in the industrial SCWG plant. • Series design for supercritical water gasification of coal-water slurry was simulated. • The optimized cold gas and exergy efficiency is improved by 17.4% and 15.0%. • The Sankey diagram shows that the series design reduces reaction and heat recovery exergy loss. • Higher series stages facilitate methane production, less oxygen demand, and higher efficiency. • The critical parameters were studied by sensitivity analysis for optimization. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks have been introduced as promising alternative materials to improve the performance of conventional desiccant wheels (DWs) in desiccant cooling systems. However, simultaneously achieving high dehumidification and energy performance of DWs coated with advanced desiccant materials is challenging owing to the trade-off between these indices. In this study, based on multi-objective optimization, the dehumidification and energy performance improvement of a DW coated with MIL-100 (Fe) (MCDW) over a conventional DW coated with silica gel (SGDW) and silica composite (SCDW) were evaluated. Based on the data measured in this study, a metamodel was developed using an artificial neural network to predict the performance of the MCDW. Furthermore, Morris sensitivity analysis was conducted to determine the decision variables. The optimal solutions were determined using multi-objective optimization and a Euclidean distance-based approach. Finally, a comparative performance evaluation between the optimized MCDW and SCDW was conducted under baseline conditions. As a result, the optimized MCDW showed higher dehumidification and energy performance than the optimized SCDW and SGDW. • The performance of a MIL-100 (Fe) coated desiccant wheel (MCDW) is measured. • Operating parameters of the MCDW are optimized using multi-objective optimization. • The multi-objective optimized MCDW and silica gel-type DWs are compared. • The optimized MCDW shows higher performance than the silica gel-type DWs. [ABSTRACT FROM AUTHOR]

This study aims to investigate the feasibility of using raw biogas as the main fuel in homogeneous charge compression ignition (HCCI) engines and to identify the effects of different trace elements found in biogas on engine performance and emissions. This study was conducted using many models that simulate the operation of HCCI process. These models include the chemical kinetics, combustion model, and the heat transfer models. Raw biogas composition was selected based on the most available traces. The traces that were studied are Hydrogen Sulfide (H 2 S), Ammonia (NH 3), Carbon monoxide (CO), Nitrogen (N 2), Oxygen (O 2), and Water vapor (H 2 O). Other parameters like the equivalence ratio, intake temperature, and pressure were also studied. It was found that raw biogas mixtures could produce higher power outputs in the presence of H 2 S and NH 3. However, H 2 S and NH 3 were found to increase the SO x and NO x percentages owing to their higher in-cylinder temperatures. Other traces such as CO and H 2 O can reduce the power output, but at the same time, they can reduce the engine NO x emissions but not the CO emissions. Engine performance was positively affected by increasing the intake pressure and equivalence ratio within the studied ranges. However, higher intake temperatures and reduce the engine efficiency and increase the engine emissions. On the other hand increasing the compression ratio (CR) increase the system efficiency but up to a certain limit. Considering the exhaust gas residual (EGR), this parameter reduce system performance but at the same time lower the NO x emissions significantly. A sensitivity analysis for the mentioned parameters indicates that EGR is the most effective parameter while CR are the least significant one. • The potential of using raw biogas in HCCI engines was examined. • The effect of different biogas traces on engine performance and emissions was studied. • Raw biogas can enhance the engine efficiency and power output. • The presence of H 2 S and NH 3 increases NO x and SO x emissions. • Sensitivity analysis indicated that EGR has the most significant effect on system performance. [ABSTRACT FROM AUTHOR]

The energy of solar airships comes from solar array, and the amount of solar radiation received by solar array determines airship endurance. Many studies optimize the solar array layout to receive more solar radiation. However, most of them are for flexible thin-film array. The influence of the size of the currently commonly used crystalline silicon panels on received energy is not considered. This paper establishes a solar irradiation model and a receiving energy model suitable for crystalline silicon arrays. The effects of array shape, array location, and module size on the energy received by crystalline silicon solar arrays are analyzed at different flight latitudes. Then, an improved genetic algorithm is designed to optimize the array geometric parameters. The results show that altering the size and layout of solar modules significantly influences energy capture, with opposing trends between regions of mid-low latitudes and high latitudes. Airships flying at mid-low latitudes should adopt larger solar modules, while airships flying at high latitudes should adopt smaller ones. After the optimization using the proposed algorithm, the received energy of solar array is further enhanced. The optimization resulted in a 2% improvement at mid-low latitudes and an 18% improvement at high latitudes over conventional optimization. • Develop energy receiving model for crystalline silicon solar cell array. • Optimize geometric parameters of solar array using improved genetic algorithms to increase received energy. • Evaluate the improved effectiveness of adding solar module size in optimization. [ABSTRACT FROM AUTHOR]

A prediction model of NO x emissions in heavy-duty gas turbine combustors based on the moderate and intense low-oxygen dilution (MILD) combustion was proposed, and the inlet parameter sensitivity analysis was also investigated to identify the impact weight for the practical operation of heavy-duty gas turbines. The optimal space-filling (OSF) design was first adopted to determine the optimal combination of gas temperature (T gas), primary air temperature (T first), secondary air temperature (T secondary), gas mass flow (m gas), primary air mass flow (m first), and secondary air mass flow (m secondary) for minimum NO x emissions. Based on the results of the OSF design, the minimum NO x emissions of 24.11 mg/m3 were obtained, and the corresponding T gas , T first , T secondary , m gas , m first , and m secondary were 769.67 K, 825.10 K, 722.61 K, 1.71 kg/s, 4.29 kg/s, and 7.69 kg/s within the range of 10% fluctuation of rated input parameters. The sensitivity ranking of the six inlet parameters was secondly achieved as m first > T first > T secondary > m secondary > m gas > T gas with the base of NO x emissions. Finally, a novel prediction model with six inlet parameters was established to quickly and accurately calculate the NO x emissions influenced by complicated boundary conditions in MILD combustion of heavy-duty gas turbines. • A NO x emission prediction model for heavy-duty gas turbines was proposed. • The impact weights of six inlet parameter sensitivities for NO x were investigated. • The optimal boundary combination for minimum NO x emission was obtained. • An optimal space-filling design was adopted to determine the optimal combination. [ABSTRACT FROM AUTHOR]

As a promising large-scale physical energy storage technology, the main challenge of compressed CO 2 energy storage currently is the issue of low-pressure CO 2 high-density storage. To address that issue, a novel trans -critical compressed CO 2 energy storage system based on 13X zeolite variable temperature adsorption (TSA-TC-CCES) is proposed in this paper. Based on the principle of 13X zeolite adsorption gas storage, combined with the temperature swing adsorption (TSA) method and contact heat exchange scheme, it realizes low-pressure CO 2 high-density storage and real-time internal circulation of heat. Under the design conditions, the adsorption tower volume, system round-trip efficiency, energy storage density, energy efficiency, and energy cycle efficiency are 360.55 m3, 89.19%, 6.29 kW ⋅ h/m3, 70.8%, and 93.53%, respectively. The sensitivity analysis results indicate that energy storage density and round-trip efficiency decrease with increasing adsorption temperature while growing with increasing desorption temperature. The increase in storage pressure positively affects energy storage density, while the increasing storage temperature harms energy storage density. The release pressure at the critical point causes abrupt changes in system performance. • A trans -critical compressed CO 2 energy storage system based on 13X zeolite temperature swing adsorption is proposed. • The contact heat exchange solution enables instant internal circulation of heat, eliminating the need for heat storage media. • This system utilizes temperature fronts to control the reheat temperature and compressor inlet temperature. [ABSTRACT FROM AUTHOR]

The properties of an ethane-ethylene mixture were analyzed under varying equivalence ratios (ranging from 0.58 to 1.4) and ethylene volume fractions (ranging from 0% to 100%) with varying dilution ratios, using a 20-liter apparatus. The study aimed to understand the explosion characteristics of the mixture and to identify the elementary reactions that have the most significant impact on the rise in pressure and ethylene consumption during the explosion process. The results showed that adding ethylene increases the maximum explosion pressure and broadens the flammability limit range. In addition, the work investigated the effects of diluting the mixture with either nitrogen or carbon dioxide. It was found that carbon dioxide has a higher specific heat capacity compared to nitrogen, which enhances the heat absorption capacity of the mixture. The collision of carbon dioxide molecules with the activated radicals in the reaction transfers energy from the radicals to carbon dioxide, reducing the radicals' activity and causing a more significant dilution effect than nitrogen. Finally, a chemical kinetics analysis of the premixed gas using the UC San Diego mechanism revealed that the production rate of key free radicals gradually increases with the addition of ethylene, enhancing the explosion reaction intensity. • Ethylene addition broadens flammability limit & increases max explosion pressure. • Carbon dioxide dilution enhances heat absorption & reduces radicals' activity. • Carbon dioxide dilution causes a more significant dilution effect than nitrogen. • Sensitivity analysis using UC San Diego mechanism enhances explosion reaction intensity. • Study provides insights into explosion characteristics of ethane-ethylene mixtures. [ABSTRACT FROM AUTHOR]

Real-time control of district heating networks in the case of failures requires for accurate and fast strategies able to guarantee thermal comfort to all connected users. In this paper, we demonstrate a control framework that responds to these essential requirements. We minimize a global measure of discomfort based on a smooth maximum approximation. The optimization problem is solved through a gradient-based algorithm that can be naturally integrated with distributed meter readings leading to high accuracy of both forward and sensitivity analysis. Objective function gradients are computed by a discrete adjoint method, which is fast and nearly insensitive to the dimensionality of the optimization problem. The proposed framework is tested with numerical experiments on a reference medium-size distribution network in Turin. Results show that the thermal comfort of most critical users increases quickly, yielding to a nearly homogeneous discomfort distribution at the end of the optimization process. Studying the effect of the inlet pressure head on the optimized system performance reveals that a centralized operation results in increased robustness of the network and allows reducing backup pumping equipment. Furthermore, applying the proposed framework at the distribution network level yields remarkable benefits also in case of failures in the main transportation network. [ABSTRACT FROM AUTHOR]

This study estimated energy use flow of buffalo farms, energy use indices, production efficiency, energy use targets, impact of energy inputs on energy output, and sensitivity analysis of energy inputs. A well-structured questionnaire was used to collect data of 360 domestic buffalo farms from Punjab Pakistan during May–July 2017. Results revealed that milk production was mainly dependent on renewable energy inputs, particularly millet, minerals, concentrates, and sorghum. Energy use efficiency (0.08) and production efficiency (0.24) indicate that energy inputs were overused. An artificial neural network (ANN) method suggested that 30.5% of total energy input could be saved if farmers followed the targeted inputs recommended by ANN. The Cobb-Douglas production function found a negative significant impact of sorghum, millet, and wheat straw; and positive significant impact of labour, concentrates, and electricity on energy output. Among the non-renewable energy sources, electricity was found to be the most wasteful use of energy input, mainly due to the mismanagement of farmers. Sensitivity analysis estimated that a unit increase in renewable energy significantly decreased milk yield by 0.02 unit. While a unit increase in non-renewable energy significantly increased milk yield by 0.01 unit. This study stresses the importance of using energy inputs at the target quantities prognosticated by ANN method, and recommends the use of energy-efficient equipment. • Energy mismanagement is common problem in livestock production system of Pakistan. • Artificial neural network (ANN) method was used to optimize energy inputs. • Renewable energy inputs were overused for milk production in the study area. • Electricity was overused by 28.2% mainly due to farmers' mismanagement. • ANN suggested 33% improvement in energy ratio by using target values of energy inputs. [ABSTRACT FROM AUTHOR]

A systematic approach is proposed to investigate the influence of different thermodynamic models and multi-parametric variations propagated through single, double and triple effect absorption refrigeration (ABR) processes and to identify the models with the least sensitivity to variability. The approach highlights the parameters which mainly affect the ABR process performance in all indicators simultaneously. A sensitivity index quantifies the variation range of the ABR performance indicators with respect to parameter variability. Cost functions are developed which combine both performance indicators and state process variables. These are used to identify thermodynamic models which simultaneously a) enable close match of simulation results and reference data, b) maintain low process variability and c) give rise to process state and performance profiles of low non-linearity, despite changes in model parameters. We employ up to 12 thermodynamic models for NH 3 /H 2 O, comprising different eNRTL models and equations of state such as Redlich-Kwong, Peng-Robinson, Schwartzentruber-Renon and Cubic-Plus-Association. We consider uncertainty in up to 6 component and process parameters and up to 7 ABR performance indicators. The e NRTL-Helgenson model is selected in all ABR process cases. Peng-Robinson for hydrocarbon water systems is selected in single-effect ABR, whereas Redlich-Kwong is selected for the double and triple-effect cases. • Consideration of multiple thermodynamic models in absorption refrigeration. • Systematic identification of appropriate model using sensitivity analysis. • Application to single, double and triple effect systems. • Model selected based on proximity to reference data, parameter variability and non-linearity. [ABSTRACT FROM AUTHOR]

An extensive investigation of a novel thermodynamic trigeneration cycle, operating with two-phase expanders and compressors and single-component wet working fluids, is carried out. Among the available fluids in the commercial software Aspen Plus 9.0® database and commonly used in industrial applications, four chemical species are selected according to several criteria in order to ensure the technical feasibility of the thermodynamic cycle. Using Aspen Plus 9.0®, an extensive sensitivity analysis in steady state conditions considering six process parameters is carried out for each selected working fluid. The combined effects associated to the process parameters on several energy performance indicators are described. To this purpose, the Parallel Coordinate Plots (PCPs) and energy performance maps associated to each indicator are adopted in original way. For each working fluid, each PCP allows the selection of the arrays of the process parameters corresponding to high values of the respective indicator. Each energy performance map allows determining the working fluid associated to the respective maximum indicator in function of the temperatures of the trigenerative end-user and external environment. The proposed method of analysis is a useful and new tool for similar investigation associated to a generic thermodynamic cycle. • Aspen® simulation model of the novel CCHP cycle at steady state is presented. • Working fluids are selected for the technical feasibility of the novel CCHP cycle. • Parallel Coordinate Plots allow the selection of the best process parameters arrays. • Energy performance maps allows determining the best working fluids. • Proposed method of analysis is a useful and new tool for thermodynamic cycles. [ABSTRACT FROM AUTHOR]

The purpose of this work was to investigate the viability of a microalgae catalytic pyrolysis/hydrotreating plant to kerosene, diesel and gasoline using Aspen Plus. A series of 2000 dry tonne per day scenarios were simulated with the final goal of determine the price in USD per litre of the drop-in fuels produced and the economic feasibility of the technique at a large-scale. Different scenarios (mechanical, solar and thermal drying), microalgae (Nannochloropsis and Isochrysis) and catalysts (Li-LSX zeolite, Ni-Ce/Al 2 O 3 , Ni-Ce/ZrO 2). For this work, a systematic design approach by Douglas was considered and the whole process was actualised with the Marshall and Swift Cost Index of 2016. The lowest minimum fuel selling price (1.418 $/L) was found for Isochrysis and Li-LSX-zeolite. Finally, the sensitivity analysis showed that the bio-oil yield was the most influent factor, leading to a variation of the fuel price between 1.158 $/L to 1.751 $/L assuming a 20% of variation. The techno-economic assessment and sensitivity analysis indicated that Isochrysis and Li-LSX-zeolites are promising for the production of drop-in fuels, but further research is required to further reduce the price of the feedstock production and drying to be competitive with lignocellulosic materials. • A techno-economic analysis of microalgae to drop-in fuels was performed by Aspen. • A minimum fuel selling price of $1.418 (Isochrysis and Li-LSX-zeolite) was obtained. • The sensitivity analysis showed that the bio-oil yield was the most influent factor. • A drastic microalgae production and drying costs are required for cost-effective microalgae biofuels. [ABSTRACT FROM AUTHOR]

The energy conversion efficiency of coal utilization is a focal point in modern energy management practices and in academic literature. This paper explores life cycle energy efficiency of coal (LEEC), which encompasses six distinct stages from the design of coal mines to the comprehensive reuse of resources. First, the study analyzes improvements to the evaluation of efficiencies in energy systems. Secondly, it proposes a multi-stage energy efficiency analysis and constructs an innovative LEEC model for coal exploitation and utilization. Thirdly, the energy efficiency of coal exploitation and utilization was evaluated, and found that total efficiency in China increased from 33.11% in 2010 to 37.69% in 2014, saving 126 Mtce of energy. Finally, this study found that mine design is the most important stage in improving energy efficiency, followed by the coal processing stage. Moreover, we found that coal processing has an indirect contribution to other stages in the life cycle utilization of coal, such as reducing transportation capacity. The primary research contribution is the development of a comprehensive LEEC model for the entire life cycle of coal, rather than just single-stage evaluation. This lays the foundation for further studies on energy efficiency of industrial chains and across entire systems. • Exploration of energy efficiency across the entire coal life cycle. • The energy efficiency of coal exploitation and utilization increased annually. • Analysis shows the implicit contribution of coal processing to life cycle. • Mine design is a crucial stage for overall life cycle energy efficiency. [ABSTRACT FROM AUTHOR]

A solar-powered cascade system is proposed for dehumidification, cooling and heating in hot summer and cold winter areas of China. To evaluate the energy, environmental and economic sustainability of the system, the emergy evaluation of the proposed system is analyzed and compared with different energy sources. The effects of solar radiation intensity, air temperature and relative humidity on the emergy performance of the system are discussed. The results show that the input total emergy and transformity of the proposed system are 9.88 × 1016 sej/y and 4.75 × 105 sej/J, which are lower than that of the systems powered by other energy. The environmental load ratio and emergy sustainability index are the highest when the system is powered by solar energy, with the value reaching 1.33 and 1.66, respectively. According to parameter sensitivity analysis, the total emergy input decreases by 6.31% and the emergy saving ratio increases by 29.09% as the increase of solar radiation intensity. The system has the best emergy performance at air temperature of 26.18 °C. The system has lower environmental load and higher sustainability in lower relative humidity climate. This work proves the viability and sustainability of the novel cascade system. • A solar-powered system is proposed for dehumidification, cooling and heating. • Emergy analysis method is conducted to evaluate the sustainability of the system. • The environmental loading ratio and emergy sustainability index are 1.33 and 1.66. • The system has the optimal emergy performance at air temperature of 26.18 °C. • This work proves the viability of the system in HSCWA of China. [ABSTRACT FROM AUTHOR]

The early design stage provides the greatest opportunities to achieve energy-efficient buildings; however, designers require relevant performance data to manage interdisciplinary and conflicting objectives. Thus, for the first time an integrated multi-stage sensitivity analysis and multi-objective optimization approach was developed for four different zones of arid climate in PCM integrated residential buildings. A multi-stage sensitivity analysis is proposed to identify the relative importance of early design stage parameters, which covers envelope thermophysics, building layout and energy-efficiency measures, considering energy and economic indicators. Three global sensitivity analysis approaches (Standardized rank regression coefficient, Partial rank correlation coefficient and Morris method) were employed in the first stage, while the local sensitivity analysis was employed in the second stage. A multi-objective optimization using the genetic algorithm was performed to obtain the Pareto frontier, and two sets of solutions were proposed: the most energy-efficient and the most cost-effective. Finally, multi-criteria decision making was carried out. It was found that sensitivity analysis results showed different rankings of parameters. For each climatic zone, a different set of optimal solutions existed. Compared to reference building, the best solutions reduced total energy consumption by 11.1–34.6%, while the payback period reduced by 17.6–48.5 years. • Multi-stage SA was used to select the most sensitive parameters. • Early design stage parameters were optimized in four arid climate zones. • Multi-objective optimization approach was performed to find optimal solution. • The most energy-efficient and most cost-effective solutions were determined. • Total energy consumption reduced by 11.1–34.6% in all locations. [ABSTRACT FROM AUTHOR]

The optimal operation of short-term cascade hydropower systems, with dozens of hydropower stations, multistage calculation periods and complex hydraulic constraints, faces a serious "curse of dimensionality" problem, and it is difficult to solve this problem directly or obtain a precise optimal hydropower dispatching plan in an acceptable time. This study presents an efficient solution framework based on the load decomposition strategy to alleviate the dimensionality problem. First, the load decomposition strategy is exploited by reducing the number of optimization stages to reduce the dimensionality of the original complicated optimization problem. By continuously adjusting the number of time periods and load of each stage, the load decomposition strategy can divide the original optimization problem into two subproblems with different optimization periods, namely, the segmented load process and burr load process. Second, a hydropower station classification method is proposed, in which all stations are divided into balanced power stations for the burr load and main dispatching stations for the base load, significantly reducing the participation of most hydropower stations in the frequent dispatch process. Finally, according to the transformation of the water balance relationship, a mathematical expression that accurately describes the complex hydraulic connection problem between the two subproblems is constructed to obtain more refined solution results. Practical project cases involving a large-scale hydropower system with 7 stations on the Wu River of China are used to test the sensitivity and efficiency of the proposed method. The sensitivity analysis indicates that the different optimization stages in this method can quickly obtain a globally optimal result and improved computational efficiency. Moreover, compared with the 24-point and 96-point traditional optimization scheduling methods, the water discharge of the presented method is reduced by 6.6% and 7.8% in the dry season and 4.0% and 4.9% in the flood season in a limited time, respectively, which suggests that the solving difficulties caused by the "curse of dimensionality" can be effectively alleviated and the solution efficiency can be greatly improved. The simulation calculations for different seasons and scales also indicate that the proposed method is an effective tool for the optimal operation of large-scale hydropower systems. • A solution framework for short-term cascade hydropower system is proposed. • The load decomposition strategy alleviates the dimensionality challenge. • The balanced power station bears frequently fluctuating load process. • Short-term hydropower optimal dispatching plan can be obtained quickly. [ABSTRACT FROM AUTHOR]

The present work investigates the effect of wall thermal conditions on the NO emission in the different inlet temperatures and oxidant dilution levels. The studies are performed for the CH 4 -air, oxygen-enriched, and oxy-CH 4 combustion under the HTC and MILD regimes by numerical simulation of the non-premixed furnace and chemical calculations of the WSR reactor. It is found that higher heat loss from the wall and more replacement of CO 2 with N 2 facilitate the establishment of the MILD regime and significant reduction of NO production. The results show an optimum point for achieving the minimum heat loss and NO emission. Under the MILD regime, dominant mechanisms in the NO emission are prompt through reaction CH + N 2 ⇌HCN + N and thermal routes for the CH 4 -air condition and N 2 O-intermediate by reactions N 2 O + O⇌2NO and N 2 O + H⇌N 2 +OH and thermal mechanisms for oxy-CH 4 combustion. In both atmospheres, the thermal route plays the main role in the low heat loss from walls, while other routes are more active in the low maximum flame temperature. Reaction N + NO⇌N 2 +O by supplying O radical from reactions O + CO + M⇌CO 2 +M and H + O 2 +M⇌HO 2 +M is activated by increasing O 2 level, while reactions N + NO⇌N 2 +O and N + OH⇌NO + H show the main contribution in the sensitivity analysis of NO. • Effect of wall thermal conditions in the different T in and X O2 is studied. • There is observed an optimum point between low heat loss from the wall and NO emission. • In high T in or X O2 , thermal route emitted high values of NO and MILD or HTC regimes. • N 2 O-intermediate is responsible for NO emission in oxy-CH 4 MILD combustion. • Prompt mechanism plays the first role in NO production under CH 4 -air MILD regime. [ABSTRACT FROM AUTHOR]

To estimate the hazard of explosion during hydrogen/methane gas transportation, the experiments were conducted in a small-scale duct. It studied the effect of hydrogen fraction and equivalent ratio distribution on flame propagation characteristics and pressure characteristics. It showed that the flame propagation process can be divided into steady-state and broken-state. During the steady-state, the addition of hydrogen will accelerate the evolution of the flame structure, leading to a reduction in the observed flame structure patterns. And the pressure transferred from bimodal to unimodal distribution. When entering the broken-state, a secondary explosion occurred. Then the flame propagation velocity started to increase dramatically and emitted a dazzling white light. This secondary explosion is more dangerous and significantly influenced by the equivalence ratio of duct A (φ A) where the ignition source is located. Therefore, it was calculated the parameters of laminar combustion of φ A , and the variation of key radicals was analyzed. Finally, the fitted equations of the maximum flame propagation speed (V max) and the maximum explosion pressure (P max) with laminar burning velocity (S L) were obtained. It was found that the growth rate of V max gradually decreased with increasing S L , while the growth rate of P max gradually increased. • The effect of hydrogen fraction on the non-uniform field explosion was studied. • The flame propagation process can be divided into steady-state and broken-state. • The premixed gas can be divided into rich-methane and poor-methane. • Discuss the evolution regularities of the elementary reactions and radical particles. • Analysis the relation between fuel fundamental parameters and explosion parameters. [ABSTRACT FROM AUTHOR]

Clean-energy vehicles are conducive to reducing greenhouse gas emissions and environmental pollution. The environmental assessment of hydrogen fuel cell vehicles (FCV) and natural gas vehicles (NGV) under different fuel production paths can achieve efficient utilization of renewable energy and reduce environmental pollution. This study conducted a life cycle assessment on hydrogen FCV and NGV under different fuel production paths. The results indicated that compared with NGV, hydrogen FCV exhibit better environmental performance. Among the fuel production methods, wind-power-assisted electrolysis of water for hydrogen production exhibits superior environmental performance. The acidification potential, global warming potential, eutrophication potential, and photochemical ozone creation potential indicators of the wind-powered fuel cell vehicles process were 91.44, 73.33, 96.47, and 93.39% lower than those of the coal synthetic natural gas vehicles process, respectively. Subsequently, the environmental impact of the internal structure in the manufacturing process of hydrogen FCV and NGV was investigated. The results showed that the power system accounted for nearly 50% of the environmental impact in the manufacturing process of hydrogen FCV. Furthermore, the influence of the six methods used for fuel production on environmental performance was evaluated through sensitivity analysis. This study provides a reference for the sustainable development of such vehicles. • Life cycle environmental performance is compared for new energy vehicles. • Environmental performance was analyzed for both vehicle powertrains. • Sensitivity analysis of fuel production efficiency was performed. • Improvement strategies are proposed to reduce environmental impact. [ABSTRACT FROM AUTHOR]

The temperature of the converter gas at the outlet of the converter is as high as 1600 °C, and the output is about 120–150 Nm3/tons steel. In order to improve the utilization efficiency of heat energy, and the recovery rate and calorific value of the converter gas, medical waste (MW) gasification of by the converter gas was proposed. At the same time, the technical feasibility and sensitivity analysis of MW gasification were conducted by Aspen Plus soft on the basis of Gibbs free energy minimization. The simulated results were successfully validated with the experimental data from MW gasification in a bench scale gasifier. Moreover, the influence of the gasification temperature, steam/MW ratio and MW/converter gas ratio on the composition, the low heating value (LHV), gas yield (GY), and carbon conversion efficiency (CCE) were evaluated. During the simulation, the mixed gas (CO: CO 2 : N 2 = 40: 20: 40) was used as the simulated converter. The simulated results demonstrated that the optimum parameters were 1050 °C gasification temperature, 0.8 steam/MW ratio, and 0.08 kg/m3 MW/converter gas ratio, CO, H 2 , and CO 2 reached to 46.54%, 38.09%, and 0.10% due to Boudouard reaction and water-gas shift reaction. After MW gasification, CO 2 content in the converter gas was decreased from 20% to 0.10%, and LHV was increased from 5480 to 10,021 kJ/Nm3. • No additional heat was need for MW gasification by the converter gas. • 1050 °C, 0.8 steam/WM ratio, and 0.08 kg/m3 MW/converter gas were the optimum parameters. • CO 2 content of the converter gas was decreased from 20% to 0.10% by MW gasification. • LHV was increased from 5480 to 10,125 kJ/Nm3 by MW gasification. [ABSTRACT FROM AUTHOR]

The spatial configuration of fracture structures controls the efficiency of heat production in enhanced geothermal systems. However, it is still impossible to fully characterize complex fracture structures, because in practice limited information is available in deep geothermal reservoirs. A simplified representation of fracture structures is necessary, but requires insight into the major parameter(s) controlling the flow and heat transport. In this study, we examine fracture structures at macroscopic and microscopic scales; the fracture orientation is used to characterize the macroscopic fracture structure, and the mean, standard deviation and correlation length of apertures are used to represent the microscopic fracture structure. Effects of these parameters on flow and heat transport and subsequently on heat production are systematically analyzed and compared. It is found that the effect of macroscopic structure on heat production is dependent on the density-driven flow induced by temperature contrast in geothermal systems, and the effect of microscopic structure is related to perturbations in flow paths, cross-sectional flow area and density-driven flow. A sensitivity analysis clarifies that in enhanced geothermal systems, priority should be given to characterize the macroscopic structure of the fracture network, followed by the estimation of the mean and correlation length of fracture apertures. • Effects of macroscopic and microscopic fracture structures on heat production are compared. • Heat production is controlled by macroscopic structure, affected by microscopic structure. • Priority should be given to characterize macroscopic structure with limited reservoir information. [ABSTRACT FROM AUTHOR]