Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (47,414)

Search Parameters:
Keywords = energy efficiency

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 1752 KiB  
Article
Hybrid Cloud-Based Information and Control System Using LSTM-DNN Neural Networks for Optimization of Metallurgical Production
by Kuldashbay Avazov, Jasur Sevinov, Barnokhon Temerbekova, Gulnora Bekimbetova, Ulugbek Mamanazarov, Akmalbek Abdusalomov and Young Im Cho
Processes 2025, 13(7), 2237; https://doi.org/10.3390/pr13072237 (registering DOI) - 13 Jul 2025
Abstract
A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the [...] Read more.
A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
Show Figures

Figure 1

26 pages, 5101 KiB  
Article
Design Optimization of Cesium Contents for Mixed Cation MA1−xCsxPbI3-Based Efficient Perovskite Solar Cell
by Syed Abdul Moiz, Ahmed N. M. Alahmadi and Mohammed Saleh Alshaikh
Nanomaterials 2025, 15(14), 1085; https://doi.org/10.3390/nano15141085 (registering DOI) - 13 Jul 2025
Abstract
Perovskite solar cells (PSCs) have already been reported as a promising alternative to traditional energy sources due to their excellent power conversion efficiency, affordability, and versatility, which is particularly relevant considering the growing worldwide demand for energy and increasing scarcity of natural resources. [...] Read more.
Perovskite solar cells (PSCs) have already been reported as a promising alternative to traditional energy sources due to their excellent power conversion efficiency, affordability, and versatility, which is particularly relevant considering the growing worldwide demand for energy and increasing scarcity of natural resources. However, operational concerns under environmental stresses hinder its economic feasibility. Through the addition of cesium (Cs), this study investigates how to optimize perovskite solar cells (PSCs) based on methylammonium lead-iodide (MAPbI3) by creating mixed-cation compositions of MA1−xCsxPbI3 (x = 0, 0.25, 0.5, 0.75, 1) for devices A to E, respectively. The impact of cesium content on the following factors, such as open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and power conversion efficiency (PCE), was investigated using simulation software, with ITO/TiO2/MA1−xCsxPbI3/Spiro-OMeTAD/Au as a device architecture. Due to diminished defect density, the device with x = 0.5 (MA0.5Cs0.5PbI3) attains a maximum power conversion efficiency of 18.53%, with a Voc of 0.9238 V, Jsc of 24.22 mA/cm2, and a fill factor of 82.81%. The optimal doping density of TiO2 is approximately 1020 cm−3, while the optimal thicknesses of the electron transport layer (TiO2, 10–30 nm), the hole-transport layer (Spiro-OMeTAD, about 10–20 nm), and the perovskite absorber (750 nm) were identified to maximize efficiency. The inclusion of a small amount of Cs may improve photovoltaic responses; however, at elevated concentrations (x > 0.5), power conversion efficiency (PCE) diminished due to the presence of trap states. The results show that mixed-cation perovskite solar cells can be a great commercially viable option because they strike a good balance between efficiency and performance. Full article
(This article belongs to the Section Solar Energy and Solar Cells)
14 pages, 666 KiB  
Article
Synthesis of Multifunctional Hyperbranched Polymers via Atom Transfer Radical Self-Condensing Vinyl Polymerization for Applications in Polyurethane-Based Anion Exchange Membranes
by Nhat Hong Nguyen, Chih-Feng Huang and Tongsai Jamnongkan
Polymers 2025, 17(14), 1930; https://doi.org/10.3390/polym17141930 (registering DOI) - 13 Jul 2025
Abstract
Anion exchange membranes (AEMs) are vital for electrochemical energy devices such as alkaline fuel cells and water electrolyzers, enabling the use of non-precious metal catalysts despite challenges from alkaline degradation. Hyperbranched polymers (hbPs) with their globular structure, high functional group density, and simple [...] Read more.
Anion exchange membranes (AEMs) are vital for electrochemical energy devices such as alkaline fuel cells and water electrolyzers, enabling the use of non-precious metal catalysts despite challenges from alkaline degradation. Hyperbranched polymers (hbPs) with their globular structure, high functional group density, and simple synthesis, offer a promising platform for enhancing transport and stability. In this study, multifunctional hbPs were synthesized from 4-vinylbenzyl chloride (VBC) and 2-hydroxyethyl methacrylate (HEMA) via atom transfer radical self-condensing vinyl polymerization (ATR-SCVP) and crosslinked into polyurethane-based AEMs. Characterization confirmed successful copolymerization and crosslinking, with excellent alkaline stability. Membranes crosslinked with higher molecular weight (MW) and VBC-richer hbPs (e.g., OH-hbP1-PU) exhibited high water uptake (75%) but low ion-exchange capacity (1.54 mmol/g) and conductivity (186 µS/cm), attributed to steric hindrance and insufficient ionic network connectivity. In contrast, OH-hbP2-PU exhibited optimal properties, with the highest OH conductivity (338 µS/cm) and IEC (2.64 mmol/g), highlighting a balanced structure for efficient ion transport. This work offers a tunable strategy for high-performance AEM development through tailored hbP architecture. Full article
(This article belongs to the Special Issue Development and Innovation of Stimuli-Responsive Polymers)
18 pages, 4312 KiB  
Article
Influence of Rare Earth Elements on the Radiation-Shielding Behavior of Serpentinite-Based Materials
by Ayşe Didem Kılıç and Demet Yılmaz
Appl. Sci. 2025, 15(14), 7837; https://doi.org/10.3390/app15147837 (registering DOI) - 13 Jul 2025
Abstract
In this study, the neutron and gamma radiation-shielding properties of serpentinites from the Guleman ophiolite complex were investigated, and results were evaluated in comparison with rare earth element (REE) content. The linear and mass attenuation coefficients (LAC and MAC), half-value layer (HVL), mean [...] Read more.
In this study, the neutron and gamma radiation-shielding properties of serpentinites from the Guleman ophiolite complex were investigated, and results were evaluated in comparison with rare earth element (REE) content. The linear and mass attenuation coefficients (LAC and MAC), half-value layer (HVL), mean free path (MFP), and effective atomic numbers (Zeff) of serpentinite samples were experimentally measured in the energy range of 80.99–383.85 keV. Theoretical MAC values were calculated. Additionally, fast neutron removal cross-sections, as well as thermal and fast neutron macroscopic cross-sections, were theoretically determined. The absorbed equivalent dose rates of serpentinite samples were also measured. The radiation protection efficiency (RPE) for gamma rays and neutrons were determined. It was observed that the presence of rare earth elements within serpentinite structure has a significant impact on thermal neutron cross-sections, while crystalline water content (LOI) plays an influential role in fast neutron cross-sections. Moreover, it has been observed that the concentration of gadolinium exerts a more substantial influence on the macroscopic cross-sections of thermal neutrons than on those of fast neutrons. The research results reveal the mineralogical, geochemical, morphological and radiation-shielding properties of serpentinite rocks contribute significantly to new visions for the use of this naturally occurring rock as a geological repository for nuclear waste or as a wall-covering material in radiotherapy centers and nuclear facilities instead of concrete. Full article
(This article belongs to the Special Issue Advanced Functional Materials and Their Applications)
Show Figures

Figure 1

36 pages, 944 KiB  
Article
An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems
by Min Xie, Lei Qing, Jia-Nan Ye and Yan-Xuan Lu
Entropy 2025, 27(7), 748; https://doi.org/10.3390/e27070748 (registering DOI) - 13 Jul 2025
Abstract
Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents [...] Read more.
Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for uncertainties in renewable output, load variation, and Hydrogen-enriched compressed natural gas (HCNG) combustion. Case studies based on a 186-bus UIES coupled with a 20-node HCNG network show that the method improves exergy efficiency by up to 2.18% while maintaining cost robustness across varying confidence levels. These results underscore the significance of integrating exergy into real-time robust optimization for resilient and high-quality energy scheduling. Full article
(This article belongs to the Section Thermodynamics)
22 pages, 1725 KiB  
Article
Capacity Optimization for Coordinated Operation of Hybrid Electrolytic Cells Based on Wavelet Packet
by Yi Yang, Bowen Zhou, Yang Xu, Juan Zhang, Bo Yang, Guiping Zhou and Shunjiang Wang
Sustainability 2025, 17(14), 6412; https://doi.org/10.3390/su17146412 (registering DOI) - 13 Jul 2025
Abstract
Hydrogen production through electrolysis of water can achieve efficient, stable and diversified utilization of renewable energy. To this end, a hybrid electrolyzer system for hydrogen production based on bi-layer optimization is constructed. Firstly, the wind and photovoltaic power is decomposed into high-frequency and [...] Read more.
Hydrogen production through electrolysis of water can achieve efficient, stable and diversified utilization of renewable energy. To this end, a hybrid electrolyzer system for hydrogen production based on bi-layer optimization is constructed. Firstly, the wind and photovoltaic power is decomposed into high-frequency and low-frequency components by an adaptive wavelet packet. The low-frequency power is allocated to the alkaline electrolyzers (AWE) to ensure its stability, and the high-frequency power is allocated to the proton exchange membrane electrolyzers (PEM) with a faster response characteristic, thereby improving the energy utilization rate. This paper proposes a bi-layer optimization model, in which the upper-layer objective is to minimize the cost of mixed hydrogen production, and the lower-layer optimization objective is to maximize the utilization rate of renewable energy. The differential evolution algorithm optimizes the upper-layer objective, with results sent to the lower layer. Then, the YALMIP toolbox is used to solve the lower-layer objective. Through case analysis, the optimal proportion of AWE and PEM hydrogen electrolyzers obtained by this optimization method is 89.5 and 10.5, respectively. Compared with a single type of electrolyzer, the method proposed in this paper effectively improves the energy utilization efficiency and reduces the cost of hydrogen production. Full article
(This article belongs to the Topic Clean Energy Technologies and Assessment, 2nd Edition)
Show Figures

Figure 1

23 pages, 13783 KiB  
Article
Synthesis and Characterization of a Nanocomposite Based on Opuntia ficus indica for Efficient Removal of Methylene Blue Dye: Adsorption Kinetics and Optimization by Response Surface Methodology
by Yasser Boumezough, Gianluca Viscusi, Sihem Arris, Giuliana Gorrasi and Sónia A. C. Carabineiro
Int. J. Mol. Sci. 2025, 26(14), 6717; https://doi.org/10.3390/ijms26146717 (registering DOI) - 13 Jul 2025
Abstract
In this study, an efficient and cost-effective nanocomposite material based on Opuntia ficus indica (cactus) powder modified with iron oxide nanoparticles was developed as an adsorbent for the removal of methylene blue (MB), a common water pollutant. The nanocomposite was synthesized through the [...] Read more.
In this study, an efficient and cost-effective nanocomposite material based on Opuntia ficus indica (cactus) powder modified with iron oxide nanoparticles was developed as an adsorbent for the removal of methylene blue (MB), a common water pollutant. The nanocomposite was synthesized through the co-precipitation method of Fe2+ and Fe3+ ions and characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) and thermogravimetric analysis (TGA). Batch adsorption experiments were conducted over 24 h, varying different operational conditions, such as pH, temperature and initial pollutant concentration. Furthermore, a Box–Behnken design was employed to develop an empirical model for predicting removal efficiency and optimizing the adsorption conditions. The effects of adsorption variables including contact time (1–60 min), initial MB concentration (20–100 mg/L), pH (2–12), adsorbent dosage (2–6 g/L) and temperature (25–55 °C) on the removal capacity were examined. Under optimal conditions, the maximum removal efficiency of MB reached approximately 96%, with a maximum adsorption capacity of 174 mg/g, as predicted by the Langmuir model. The synthesized cactus/iron oxide nanocomposite demonstrated significant potential as an adsorbent for treating MB-contaminated water. Full article
(This article belongs to the Special Issue Molecular Research and Applications of Nanomaterials)
Show Figures

Figure 1

28 pages, 10424 KiB  
Article
The Application of Wind Power Prediction Based on the NGBoost–GRU Fusion Model in Traffic Renewable Energy System
by Fudong Li, Yongjun Gan and Xiaolong Li
Sustainability 2025, 17(14), 6405; https://doi.org/10.3390/su17146405 (registering DOI) - 13 Jul 2025
Abstract
In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. [...] Read more.
In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article
Show Figures

Figure 1

18 pages, 4285 KiB  
Article
Application of a Phase-Change Material Heat Exchanger to Improve the Efficiency of Heat Pumps at Partial Loads
by Koharu Tani, Sayaka Kindaichi, Keita Kawasaki and Daisaku Nishina
Energies 2025, 18(14), 3694; https://doi.org/10.3390/en18143694 (registering DOI) - 12 Jul 2025
Abstract
Inverter-equipped heat pumps allow for increased energy efficiency. However, air conditioning (AC) systems often operate at low load ratios below where inverter control is effective, which reduces their energy efficiency. We developed an AC system that increases the apparent load ratio of the [...] Read more.
Inverter-equipped heat pumps allow for increased energy efficiency. However, air conditioning (AC) systems often operate at low load ratios below where inverter control is effective, which reduces their energy efficiency. We developed an AC system that increases the apparent load ratio of the heat pump by using a phase-change material (PCM). Cooling and heating experiments were conducted with a PCM heat exchanger, which comprised aluminum plates and fins filled with paraffinic PCM. The result indicated a high heat transfer coefficient of >70 W/(m2·K). A simplified numerical model of the PCM heat exchanger as a lumped constant system was created based on the experiment. The calculations generally reproduced the experimental results, with root mean squared errors of 0.39 K for cooling and 0.84 K for heating, confirming their accuracy. Simulations were then conducted to evaluate the energy performance of the proposed system for the cooling season. While low load operation accounted for 39% of the total AC time for a non-PCM system, it was reduced to 2.7% for the proposed system. The proposed system demonstrated load ratios of 50–60% for most of the season, achieving an energy reduction of 11.4% owing to the improved efficiency at partial load ratios. Full article
(This article belongs to the Section J1: Heat and Mass Transfer)
Show Figures

Figure 1

22 pages, 5638 KiB  
Article
Synthesis of Fe–Cu Alloys via Ball Milling for Electrode Fabrication Used in Electrochemical Nitrate Removal from Wastewater
by Hannanatullgharah Hayeedah, Aparporn Sakulkalavek, Bhanupol Klongratog, Nuttakrit Somdock, Pisan Srirach, Pichet Limsuwan and Kittisakchai Naemchanthara
Processes 2025, 13(7), 2232; https://doi.org/10.3390/pr13072232 (registering DOI) - 12 Jul 2025
Abstract
Fe and Cu powders were mixed at a 50:50 ratio. Then, Fe-Cu alloys were prepared using the ball milling technique with different milling times of 6, 12, 18, 24, 30, 36, and 42 h. The crystalline structure was analyzed using X-ray diffraction (XRD), [...] Read more.
Fe and Cu powders were mixed at a 50:50 ratio. Then, Fe-Cu alloys were prepared using the ball milling technique with different milling times of 6, 12, 18, 24, 30, 36, and 42 h. The crystalline structure was analyzed using X-ray diffraction (XRD), and it was found that the optimum milling time was 30 h. The homogeneity of the Fe and Cu elements in the Fe–Cu alloys was analyzed using the scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM–EDX) mapping technique. Additionally, the crystal orientation of the Fe–Cu alloys was investigated using transmission electron microscopy (TEM). To fabricate the cathode for nitrate removal via electrolysis, an Fe–Cu alloy milled for 30 h was deposited onto a copper substrate using mechanical milling, then annealed at 800 °C. A pulsed DC electrolysis method was developed to test the nitrate removal efficiency of the Fe–Cu-coated cathode. The anode used was an Al sheet. The synthesized wastewater was prepared from KNO3. Nitrate removal experiments from the synthesized wastewater were performed for durations of 0–4 h. The results show that the nitrate removal efficiency at 4 h was 96.90% compared to 74.40% with the Cu cathode. Full article
(This article belongs to the Section Environmental and Green Processes)
24 pages, 9506 KiB  
Article
An Integrated Assessment Approach for Underground Gas Storage in Multi-Layered Water-Bearing Gas Reservoirs
by Junyu You, Ziang He, Xiaoliang Huang, Ziyi Feng, Qiqi Wanyan, Songze Li and Hongcheng Xu
Sustainability 2025, 17(14), 6401; https://doi.org/10.3390/su17146401 (registering DOI) - 12 Jul 2025
Abstract
In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas [...] Read more.
In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article
Show Figures

Figure 1

43 pages, 4478 KiB  
Article
Development and Fuel Economy Optimization of Series–Parallel Hybrid Powertrain for Van-Style VW Crafter Vehicle
by Ahmed Nabil Farouk Abdelbaky, Aminu Babangida, Abdullahi Bala Kunya and Péter Tamás Szemes
Energies 2025, 18(14), 3688; https://doi.org/10.3390/en18143688 (registering DOI) - 12 Jul 2025
Abstract
The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, [...] Read more.
The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO2 emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance. Full article
17 pages, 627 KiB  
Review
Energy Efficiency Measurement Method and Thermal Environment in Data Centers—A Literature Review
by Zaki Ghifari Muhamad Setyo, Hom Bahadur Rijal, Naja Aqilah and Norhayati Abdullah
Energies 2025, 18(14), 3689; https://doi.org/10.3390/en18143689 (registering DOI) - 12 Jul 2025
Abstract
The increase in data center facilities has led to higher energy consumption and a larger carbon footprint, prompting improvements in thermal environments for energy efficiency and server lifespan. Existing literature studies often overlook categorizing equipment for power usage effectiveness (PUE), addressing power efficiency [...] Read more.
The increase in data center facilities has led to higher energy consumption and a larger carbon footprint, prompting improvements in thermal environments for energy efficiency and server lifespan. Existing literature studies often overlook categorizing equipment for power usage effectiveness (PUE), addressing power efficiency measurement limitations and employee thermal comfort. These issues are addressed through an investigation of the PUE metric, a comparative analysis of various data center types and their respective cooling conditions, an evaluation of PUE in relation to established thermal standards and an assessment of employee thermal comfort based on defined criteria. Thirty-nine papers and nine websites were reviewed. The results indicated an average information technology (IT) power usage of 44.8% and a PUE of 2.23, which reflects average efficiency, while passive cooling was found to be more applicable to larger-scale data centers, such as Hyperscale or Colocation facilities. Additionally, indoor air temperatures averaged 16.5 °C with 19% relative humidity, remaining within the allowable range defined by ASHRAE standards, although employee thermal comfort remains an underexplored area in existing data center research. These findings highlight the necessity for clearer standards on power metrics, comprehensive thermal guidelines and the exploration of alternative methods for power metrics and cooling solutions. Full article
(This article belongs to the Section G: Energy and Buildings)
Show Figures

Figure 1

36 pages, 1777 KiB  
Review
Review of Challenges in Heat Exchanger Network Development for Electrified Industrial Energy Systems
by Stanislav Boldyryev, Oleksandr S. Ivashchuk, Goran Krajačić and Volodymyr M. Atamanyuk
Energies 2025, 18(14), 3685; https://doi.org/10.3390/en18143685 (registering DOI) - 12 Jul 2025
Abstract
Shifting towards electrified industrial energy systems is pivotal for meeting global decarbonization objectives, especially since process heat is a significant contributor to greenhouse gas emissions in the industrial sector. This review examines the changing role of heat exchanger networks (HENs) within electrified process [...] Read more.
Shifting towards electrified industrial energy systems is pivotal for meeting global decarbonization objectives, especially since process heat is a significant contributor to greenhouse gas emissions in the industrial sector. This review examines the changing role of heat exchanger networks (HENs) within electrified process industries, where electricity-driven technologies, including electric heaters, steam boilers, heat pumps, mechanical vapour recompression, and organic Rankine cycles, are increasingly supplanting traditional fossil-fuel-based utilities. The analysis identifies key challenges associated with multi-utility integration, multi-pinch configurations, and low-grade heat utilisation that influence HEN design, retrofitting, and optimisation efforts. A comparative evaluation of various methodological frameworks, including mathematical programming, insights-based methods, and hybrid approaches, is presented, highlighting their relevance to the specific constraints and opportunities of electrified systems. Case studies from the chemicals, food processing, and cement sectors demonstrate the practicality and advantages of employing electrified heat exchanger networks (HENs), particularly in terms of energy efficiency, emissions reduction, and enhanced operational flexibility. The review concludes that effective strategies for the design of HENs are crucial in industrial electrification, facilitating increases in efficiency, reductions in emissions, and improvements in economic feasibility, especially when they are integrated with renewable energy sources and advanced control systems. Future initiatives must focus on harmonising technical advances with system-level resilience and economic sustainability considerations. Full article
20 pages, 1865 KiB  
Article
Cavitation Performance Analysis in the Runner Region of a Bulb Turbine
by Feng Zhou, Qifei Li, Lu Xin, Xiangyu Chen, Shiang Zhang and Yuqian Qiao
Processes 2025, 13(7), 2231; https://doi.org/10.3390/pr13072231 (registering DOI) - 12 Jul 2025
Abstract
As a core component in renewable energy systems for grid regulation, hydropower units are increasingly exposed to flow conditions that elevate the risk of cavitation and erosion, posing significant challenges to the safe operation of flow-passage components. In this study, model testing and [...] Read more.
As a core component in renewable energy systems for grid regulation, hydropower units are increasingly exposed to flow conditions that elevate the risk of cavitation and erosion, posing significant challenges to the safe operation of flow-passage components. In this study, model testing and computational fluid dynamics (CFD) simulations are employed to investigate the hydraulic performance and cavitation behavior of a bulb turbine operating under rated head conditions and varying cavitation numbers. The analysis focuses on how changes in cavitation intensity affect flow characteristics and efficiency within the runner region. The results show that as the cavitation number approaches its critical value, the generation, growth, and collapse of vapor cavities increasingly disturb the main flow, causing a marked drop in blade hydraulic performance and overall turbine efficiency. Cavitation predominantly occurs on the blade’s suction side near the trailing edge rim and in the clearance zone near the hub, with bubble coverage expanding as the cavitation number decreases. A periodic inverse correlation between surface pressure and the cavitation area is observed, reflecting the strongly unsteady nature of cavitating flows. Furthermore, lower cavitation numbers lead to intensified pressure pulsations, aggravating flow unsteadiness and raising the risk of vibration. Full article
Back to TopTop