Utility Demand Research Articles

High internal-phase Pickering emulsions constructed using myofibrillar proteins from large yellow croaker: Effect of glycerol.

Exploring the emulsification of myofibrillar protein (MP) from large yellow croaker (Larimichthys crocea) could meet the demand for high-value development and utilization of fish proteins. Therefore, MPs as the emulsifier to form stable high internal-phase Pickering emulsions (HIPPEs) with the addition of glycerol and the effects of different glycerol addition ratios of HIPPEs were investigated. HIPPEs could be constructed by MPs with the glycerol addition at a ratio of 15%-30% (v/v) compared to those without the addition of glycerol. With the increase in glycerol ratios, the absolute value of Zeta potential increased and reached 22.57±0.29mV at the glycerol ratio of 30%. All the HIPPEs stabilized by MPs with the addition of glycerol possessed storage stability. Besides, the centrifugal stability constant (Ke), backscattered light intensity and reflected light stability index confirmed that the addition of glycerol was beneficial for the formation of stable HIPPEs prepared by MPs. Additionally, HIPPEs stabilized by MPs with the addition of glycerol possessed small emulsion droplets and viscoelastic behavior. These findings could be helpful for the development and utilization of MPs of large yellow croaker in the food industry.

High‐Performance Bionic Architectures via 3D Printing of Recycled Kevlar: A Study of Laser Ablation and Mechanical Properties

ABSTRACTThe study investigates the potential of 3D printing as an eco‐friendly and cost‐effective method to repurpose waste fabrics into valuable products. It explores the use of bio‐inspired structures in 3D printing to gain insights into sandwich structures' mechanical properties and laser ablation characteristics. By incorporating natural skeleton designs such as nacreous and foliated hierarchies and industrial waste material (KF), the research aims to enhance mechanical performance, laser ablation characteristics, and sustainable waste management. The study demonstrates the synergistic application of two distinct additive manufacturing techniques (FDM and DIW) at the micron scale to create nacre‐inspired sheet, columnar, and laminated structures, allowing for manipulation of rigid and pliant material phases within KF waste. The featured architectures are found in the Mollusk Seashell, which improves toughness and strength simultaneously for high‐end mechanical applications. During impact loading, the study highlights the enhanced energy dissipation and delamination properties of foliated structures compared to nacre‐like ones. The research emphasizes that the mass loss of NS + KF comprises the lowest value compared to NC + KF, then FL + KF, and pristine + KF at 375 mJ for 210 s. Ultimately, the findings hold promise for applications in aerospace, defense, automobile, thermal management, laser ablation, and structural components due to the demand for waste utilization, accessibility, environmental responsibility, and green manufacturing practices.

Oxidative stress response to gasoline generator exhaust emission in adult male wistar rats

Petroleum-powered generators are commonly used in many developing countries as an alternative to meet utility demands. Generator exhaust emission significantly contributes to air pollution, which remains a constant threat to human health due to the presence of aromatic hydrocarbons and other harmful gases. This study assessed oxidative stress parameters in response to exhaust emission from gasoline generator engine in adult male wistar rats. Forty-eight (48) adult wistar rats weighing between 180-200g were randomly allocated to four (4) groups (A-D) of twelve (12) rats each. After the acclimatization period, the control group (A) were kept unexposed, whereas rats in groups (C-D) were exposed daily at 2, 4, and 8-hour intervals for 4, 8, and 12 weeks, respectively. Tissue samples were obtained at four weeks intervals. Fresh lung tissues weighing 1g were rinsed twice in phosphate-buffered saline (PBS, pH 8.0), homogenized and centrifuged at 3000 revolutions per minute for 20 min at 4°C. Supernatant levels of malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione S-transferase (GST), reduced glutathione (GSH), nitric oxide (NO), hydrogen peroxide (H2O2), catalase (CAT), and total antioxidant status (TAS) were determined using standard protocols. The findings revealed elevated oxidant levels of MDA, NO, and H2O2, whereas SOD, GPX, GST, GSH, CAT, and TAS were significantly reduced across the exposure time points compared to the unexposed control rats (p < 0.05). The research findings revealed that exposure to emissions from gasoline generators induced oxidative stress in the exposed rats, with the extent of disruption to their oxidative balance dependent on the duration and length of exposure time.

Mass Transfer‐Enhanced Photothermal Membranes with Synergistic Light Utilization for High‐Turbidity Wastewater Purification

The photo‐Fenton process faces significant limitations in treating high‐turbidity, colored wastewater due to light attenuation and impurity interference (blocked mass transfer). To address these issues, we developed a suspended photothermal Fenton membrane by loading a photothermal catalyst on a hydrophobically modified cotton filter paper, enabling precise suspension 1 mm below the water surface. This design achieved 89.49 % light utilization and high chemical oxygen demand (COD) removal, even in wastewater with extreme chromaticity (10 multiples) and turbidity (703 NTU). The enhanced photothermal conversion accelerated molybdenum co‐catalyzed Fenton reactions and improved peroxymonosulfate (PMS) activation, maintaining over 90 % phenol removal for 15 days. Mechanistic simulations revealed improved mass transfer of reactive oxygen species (ROS) and pollutants at the solid‐liquid interface, with PMS diffusion identified as the rate‐limiting step. The membrane resisted fouling from suspended solids and maintained stable operation in soil‐containing solutions for 10 days. This innovative approach offers an efficient solution for degrading pollutants in dark‐colored, high‐turbidity wastewater, overcoming traditional process limitations.

Mass Transfer-Enhanced Photothermal Membranes with Synergistic Light Utilization for High-Turbidity Wastewater Purification.

The photo-Fenton process faces significant limitations in treating high-turbidity, colored wastewater due to light attenuation and impurity interference (blocked mass transfer). To address these issues, we developed a suspended photothermal Fenton membrane by loading a photothermal catalyst on a hydrophobically modified cotton filter paper, enabling precise suspension 1 mm below the water surface. This design achieved 89.49 % light utilization and high chemical oxygen demand (COD) removal, even in wastewater with extreme chromaticity (10 multiples) and turbidity (703 NTU). The enhanced photothermal conversion accelerated molybdenum co-catalyzed Fenton reactions and improved peroxymonosulfate (PMS) activation, maintaining over 90 % phenol removal for 15 days. Mechanistic simulations revealed improved mass transfer of reactive oxygen species (ROS) and pollutants at the solid-liquid interface, with PMS diffusion identified as the rate-limiting step. The membrane resisted fouling from suspended solids and maintained stable operation in soil-containing solutions for 10 days. This innovative approach offers an efficient solution for degrading pollutants in dark-colored, high-turbidity wastewater, overcoming traditional process limitations.

Research on a Passenger Flow Prediction Model Based on BWO-TCLS-Self-Attention

In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This shift has brought increasing attention to the safety of personnel flow in deep spaces. In addressing challenges in deep space passenger flow prediction, such as irregular flow patterns, surges in extreme conditions, large data dimensions, and redundant features complicating the model, this paper proposes a deep space passenger flow prediction model that integrates a Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) network. The model first employs a dual-layer LSTM network structure with a Dropout layer to capture complex temporal dynamics while preventing overfitting. Then, a Self-Attention mechanism and TCN network are introduced to reduce redundant feature data and enhance the model’s performance and speed. Finally, the Beluga Whale Optimization (BWO) algorithm is used to optimize hyperparameters, further improving the prediction accuracy of the network. Experimental results demonstrate that the BWO-TCLS-Self-Attention model proposed in this paper achieves an R2 value of 96.94%, with MAE and RMSE values of 118.464 and 218.118, respectively. Compared with some mainstream prediction models, the R2 value has increased, while both MAE and RMSE values have decreased, indicating its ability to accurately predict passenger flow in deep underground spaces.

Comprehensive evaluation of various CO2 capture technologies through rigorous simulation: Economic, equipment footprint, and environmental analysis

The comprehensive evaluation of various CO2 capture technologies from multiple perspectives remains limited, yet it is crucial for the successful implementation and deployment of carbon capture solutions to achieve carbon neutrality. This study presents a framework for assessing representative CO2 capture processes from key point sources through rigorous simulation. Eight scenarios were developed and compared, comprising four standalone processes (i.e., physical absorption (PHYABS), chemical absorption (CHEABS), dual-reflux pressure swing adsorption (DRPSA) and pressure-temperature swing adsorption (PTSA)) and four hybrid processes that integrate different adsorption and absorption processes. To evaluate each scenario, an integrated indicator, the Economics, Equipment footprint, and Environmental Score (EEES), was introduced. Our results indicate that the standalone CHEABS exhibits the lowest EEES of 0.120, highlighting its technological readiness and superiority over other processes. In contrast, the standalone PHYABS (EEES=0.168) and the hybrid PHYABS/PTSA process (EEES=0.242) emerge as viable alternatives, balancing environmental performance with economic and spatial considerations. Standalone PTSA (EEES=0.465) and DRPSA (EEES=0.706) are less favorable because of their higher utility demands and larger equipment footprints. Similarly, hybrid processes, namely, DRPSA/CHEABS (EEES=0.891), CHEABS/PTSA (EEES=0.837), and DRPSA/PHYABS (EEES=0.784), are less advantageous across all three metrics. Furthermore, sensitivity analyses indicated that carbon permit prices exert a negligible effect on the process economics. Additionally, it appears that government subsidies may play a crucial role in facilitating the development of CO2 capture technologies within the industrial sector. Overall, this study provides a robust framework for evaluating CO2 capture processes and offers practical recommendations for technology deployment.

Synergistic leaching of lithium from clay-type lithium ore using sulfuric acid and oxalic acid

With the escalating market demand for lithium, the development and efficient utilization of lithium resources have become crucial. This study introduced a method for leaching lithium from raw clay-type lithium ores using a composite sulfuric acid oxalic acid system. The experimental results revealed that the optimal conditions for the leaching process included roasting temperature of 600 °C, sulfuric acid concentration of 0.8 mol/L, liquid-solid ratio of 5 mL/g, leaching temperature of 90 °C, leaching duration of 90 min, and oxalic acid dosage of 2 g. Under these conditions, the leaching efficiency of lithium reached 93.45 %. The structural changes during the lithium leaching process and leaching mechanism were analyzed by XRD, SEM, TOF-SIMS. It was found that the minerals after mixed-acid leaching showed a loose morphology, a decrease in the average particle size, and a significant increase in the specific surface area as well as the pore volume, leading to improved lithium leaching efficiency. Furthermore, the mechanism underlying the mixed-acid leaching of lithium from clay-type lithium ores was explored. According to this mechanism, sulfuric acid first dissociated H+, which disrupted the mineral structure, allowing further destruction by oxalic acid. During this process, Li+ was continuously replaced by H+ and reacted with C2O42− dissociated from oxalic acid to form water-soluble Li2C2O4.

Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework

The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.

Pea Pod–Derived Biochar–Zeolitic Imidazolate Framework Composite for the Photocatalytic Degradation of Two Organic Dyes Crystal Violet and Victoria Blue

ABSTRACTThe rapid increase of water pollution globally is a vital environmental concern that requires the immediate attention of researchers to find new innovative ways to remove unwanted environmental pollutants from our water sources. With the advances in the field of sustainable engineering, there is a high demand for the effective utilization of biomass resources for the removal of aqueous environmental contaminants. Biochar is carbon carbon‐enriched substance obtained by biomass pyrolysis; it is inexpensive and has received wide attention in the field of wastewater treatment. Herein, we describe the synthesis of pea pod (PO) biochar‐reinforced zeolitic imidazolate framework (ZIF‐8) nanocomposite (PO@ZIF‐8) through the in situ precipitation of ZIF‐8 onto the biochar surface. The crystal growth, morphology, chemical composition, and optical characteristics of fabricated PO@ZIF‐8 nanocomposite were scrutinized through PXRD, SEM, TEM, UV‐DRS, PL, and XPS analysis. The dodecahedral‐shaped PO@ZIF‐8 particles have an average size between 140 and 160 nm. The biochar‐reinforced ZIF‐8 nanocomposite showed significantly higher photocatalytic activity than the pure ZIF‐8 for the degradation of two commonly found organic dyes crystal violet (CV) and Victoria Blue (VB) in the presence of direct sunlight irradiation. The PO@ZIF‐8 nanocomposite showed the highest degradation efficiency of ~87% and ~80% within 50 min of irradiation time at a catalyst dosage of 20 mg for 30 ppm CV and VB dye solutions at pH 8. First‐order kinetics were obeyed during the photodegradation with 0.041 and 0.030 min−1 as the constant of degradation for the removal of CV and VB. The radical scavenger experiment and the photoluminescence analysis confirm the active participation of ·OH radical in the degradation of both dyes. LC–MS and TOC analysis was also performed to determine the degradation products and for evaluation of the degradation progress. Moreover, the synthesized PO@ZIF‐8 composite also exhibit good stability till the fourth cycle with high degradation efficiency thus making it a good choice of catalyst in the field of environmental decontamination.

Classification and Evaluation of Marginal Land for Potential Cultivation in Northwest China Based on Contiguity and Restrictive Factors

Water, land, and other environmental conditions restrict marginal land (ML) conversion into newly cultivated land. Accurately evaluating ML’s development and utilization potential (DUP) can provide critical support for increasing new cultivated land and ensuring food security. This study focuses on Northwest China, using spatial identification of different types of ML based on remote sensing images, and constructs a county-level DUP evaluation model through contiguous characteristics and restrictive factors to determine new cultivated-land potential, water demand, and liftable grain production. The results show that the DUP of ML in Northwest China is 12.59 million ha, with low-efficiency cultivated land (LCL) and two types of restoration land (TTRL) accounting for 3.29% and 5.95%, and other marginal land (OML) making up 90.76%. The total water demand for ML development and utilization is 69.87 billion cubic meters, which can increase grain production by 62.31 million tons. The coordinated development of water, land, and food promotes an increase in grain production, with water resources being the main restrictive factor. This model effectively evaluates DUP and provides a scientific basis for promoting the rational use of water and land resources. Further research should set up more detailed water resource utilization strategies and scenarios as well as find more development and utilization techniques.

Construction of Ion-Imprinted Graphene Oxide Mixed-Matrix Membranes for Selective Adsorption and Separation of Tm3.

Efficient adsorption and separation of rare earth from other similar rare earth wastewater has become an urgent demand for resource utilization of ion-type rare earth minerals in China. Herein, thulium (Tm) ion-imprinted graphene oxide (GO)-doped polyether sulfone (PES) membranes (GO-TII/PES-2 membranes) were prepared, in which ion-imprinted graphene oxide was applied as an efficient Tm3+ ionic ligand in the imprinted layer and polyether sulfone was applied as a carrier in the membrane matrix to achieve the selective adsorption and separation of Tm3+ and neighboring rare earth ions. Combined with an ion rectifier, the separation and purification performances of Tm3+ were explored. The separation factors β(Tm3+/Tb3+), β(Tm3+/Sm3+), β(Tm3+/Nd3+), and β(Tm3+/Ce3+) in the dynamic adsorption process increased significantly from 1.22, 1.04, 1.04, and 1.02 for nonimprinting to 3.07, 3.91, 3.91, and 3.33 for imprinted membranes. The GO-TII/PES-2 membrane adsorbed about three times more Tm3+ than the nonionic-imprinted (GO-NII/PES) membrane by adding a color developer and quantifying Tm3+ based on a fast and easy UV-photometric method. After eight dynamic permeations, the adsorption of Tm3+ by the GO-TII/PES-2 membrane decreased by only 13%, indicating that the membrane has good reuse performance. Additionally, the investigation examined the influence of Tm3+ on wheat seed germination, underscoring its potential application in agriculture and the importance of adsorbing and separating rare earth ions.

EVALUATING THE CHARACTERISTICS OF THE VTSM SPECTRUM SENSING METHOD IN COGNITIVE RADIO NETWORKS

The article investigates the complexities associated with spectrum analysis in cognitive radio networks (CRNs). It begins by acknowledging the evolving challenges of spectrum sensing due to the dynamic nature of wireless environments and the increasing demand for efficient spectrum utilization. The study thoroughly examines various traditional spectrum sensing methods, including Energy Detector Based Sensing, Waveform-Based Sensing, Cyclostationarity-Based Sensing, and Matched Filtering. The focus of the research is on the Variable Time Segment Monitoring (VTSM) method, a novel approach that optimizes spectrum sensing by dynamically adjusting the time segments used for analyzing spectral characteristics. The paper highlights the VTSM method's ability to enhance detection accuracy and reduce false alarms by adapting to different signal environments, making it particularly suited for complex and dynamic CRN scenarios. Furthermore, the article compares VTSM with traditional methods across key performance metrics such as detection accuracy, false alarm rate, latency, and computational complexity. The analysis reveals that while traditional methods have their strengths, VTSM offers a balanced approach, combining flexibility, accuracy, and moderate computational demands, thereby providing a versatile solution for modern spectrum sensing challenges. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions, which are crucial for optimizing network performance and ensuring reliable communication in increasingly congested and complex wireless environments. The article concludes by emphasizing the importance of selecting the appropriate spectrum sensing method based on the specific requirements of the CRN application, considering factors such as accuracy, computational resources, and environmental dynamics. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions.

Construction of nitrogen-rich groups @ zirconium-based metal-organic frameworks for efficient iodine capture

Efficient capture of radioactive iodine serves as an inevitable demand for secure utilization of nuclear energy, environmental conservation, and human health. In this contribution, a series of iodine adsorbent materials Im@UiO-66 were fabricated by encapsulating imidazole (Im) molecules into the pore of a classical zirconium-based metal–organic frameworks UiO-66, employing a simple and feasible vapor-diffusion strategy. Compared with original UiO-66, the resulting composites achieved a significant enhancement in iodine capture performance. Particularly, Im@UiO-66-3 demonstrated outstanding iodine adsorption performance with capacities of 4.66 g g−1 for vapor and 915 mg g−1 for solution, which were 3.5 and 9.2 times of the original UiO-66, respectively. Moreover, the introduction of nitrogen through ligand encapsulation provided additional sites for iodine immobilization. The primary mechanism underlying this remarkable performance was identified as charge transfer between iodine and imidazole (Im) molecules. The research offers valuable insights for the design of high-efficiency iodine adsorbents.

A comprehensive analysis of dynamic PAPR reduction schemes in MIMO-OFDM systems

<span>In <span>this paper, an attempt develops three different methods, namely, Hybrid Maximal-Minimum (Max-Min) model with Decomposed Selective Mapping (D-SLM) in a UFMC, Modified Enhancement Asymmetric Arithmetic Coding Scheme (M-EAAC) and Dynamic Threshold-based Logarithmic Companding (DTLC) is carried out in Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) technology to enhance the PAPR reduction. These methods allow increased data rate request through threshold limit adjustment in a desired out-of-band (OOB) range, allows data transmission for the selected for the candidate sequences for maximizing the channel utility, data capacity and computational demands and varying threshold limit to analyse the nonlinear companding effect, respectively on D-UFMC-SLM, M-EAAC SCS-TI and DTLC. The extensive analysis shows that the proposed M-EAAC SCS-TI achieves a reduced CCDF PAPR, increased average spectral efficiency and redued Bit Error Rate (BER) than the other proposed DTLC and D-UFMC-SLM methods.</span></span>

Boosting methanolysis of polycarbonate (PC) for bisphenol A recovery from end-of-life PC and PC/ABS blend

Poly(bisphenol A carbonate) (PC) is an essential polymer from fossil sources. Currently, PC lacks any economically viable recycling strategy, which leads to a loss of valuable resources, and the accumulation of PC in the environment can have a negative impact on human health due to the leaching of toxic Bisphenol A (BPA). BPA recovery from PC wastes is a prerequisite to meet the demand for sustainable resource utilization and address environmental concerns. Hence, this work demonstrates the efficient BPA recovery from PC wastes via methanolysis at high PC loading (47 wt%) in the presence of a low amount of sodium hydroxide (NaOH, ≤0.05 wt%) at moderate temperatures (125 °C – 175 °C). Selected conditions prevented unwanted decomposition of BPA and ensured a mild alkaline product requiring simple purification steps to recover BPA. The environmental impact factor (ξ) of these processes was shown to be quite low compared to the ones represented in the literature. When these processes were used to recover BPA from real wastes: 93 ± 1.5 % of pure BPA was recovered from end-of-life PC roof panels (EOL-PC) after 30 min at 175 °C with 0.004 wt% of NaOH. On the other hand, direct recovery of BPA from the EOL-PC/acrylonitrile butadiene styrene (EOL-PC/ABS) blend was hindered by additive contamination and proceeded at a slower reaction rate due to the presence of ABS. However, subjecting EOL-PC/ABS to a decolorization-depolymerization method ensured ∼94 ± 2.5 % BPA yield.

GIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing

AbstractTask scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. GIJA integrates the principles of the Geyser‐inspired algorithm with the Jaya algorithm, augmented by a Levy Flight mechanism, to address the complexities of task scheduling optimization. The motivation for this research stems from the increasing demand for efficient resource utilization and task management in cloud computing, driven by the proliferation of Internet of Things (IoT) devices and the growing reliance on cloud‐based services. Traditional task scheduling algorithms often face challenges in handling dynamic workloads, heterogeneous resources, and varying performance objectives, necessitating innovative optimization techniques. GIJA leverages the eruptive dynamics of geysers, inspired by nature's efficiency in channeling resources, to guide task scheduling decisions. By combining this Geyser‐inspired approach with the simplicity and effectiveness of the Jaya algorithm, GIJA offers a robust optimization framework capable of adapting to diverse cloud computing environments. Additionally, the integration of the Levy Flight mechanism introduces stochasticity into the optimization process, enabling the exploration of solution spaces and accelerating convergence. To evaluate the efficacy of GIJA, extensive experiments are conducted using synthetic and real‐world datasets representative of cloud computing workloads. Comparative analyses against existing task scheduling algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, and GIAA, demonstrate the superior performance of GIJA in terms of solution quality, convergence rate, diversity, and robustness. The findings of GIJA provide a promising solution quality for addressing the complexities of task scheduling in cloud environments (95%), with implications for enhancing system performance, scalability, and resource utilization.

Design and optimization of the radial inflow turbogenerator for organic Rankine cycle system based on the Genetic Algorithm

The backdrop of energy-saving and emission-reduction sets a higher demand for low-grade energy utilization which can be realized by Organic Rankine Cycle systems. A 50-kW radial inflow turbogenerator, the crucial heat conversion equipment, with aerostatic bearings for Organic Rankine Cycle system, is designed. It is optimized focusing on the performance of the turbogenerator which uses R245fa as the working fluid. The study employs a one-dimensional thermodynamic model, developed in MATLAB and bolstered by real-time data from Refprop, ensuring the model’s precision and dependability. On this basis, Genetic Algorithm is applied to optimize design parameters and significantly elevate the system’s efficiency, achieving circumferential efficiency of 85.29%. The turbogenerator demonstrates a rated bearing load capacity of 2.43 kN. The research delves into the influence of inlet temperature, inlet pressure, and rotational speed of turbogenerator on its performance. It reveals that an increase in both inlet temperature and inlet pressure lead to a decrease in isentropic efficiency and an increase in output power. The research also shows that an increase in the rotational speed contributes to increases in isentropic efficiency and output power.

Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security

This paper introduces the Possibilistic-Probabilistic Risk-based Smart Energy Hub (PPR-SEH) scheduling model, addressing cybersecurity challenges in the transition to advanced energy systems characterized by decarbonization, decentralization, and digitalization. The model integrates cutting-edge technologies like Carbon Capture Utilization and Storage (CCUS) and demand response programs. It addresses uncertainties from renewable energy sources, demand variability, fuel supply, and energy price fluctuations using a Z-number-based approach. Covering various energy types—electricity, heat, cooling, gas, and water—the PPR-SEH model offers a comprehensive energy management solution. Employing a mixed-integer linear programming (MILP) framework optimized with the CPLEX solver in GAMS software, it uses an epsilon constraint method and a fuzzy satisfying approach for solution selection. The model also evaluates the economic and environmental impacts of peer-to-peer energy-sharing markets linked with carbon emission trading, enhancing the efficiency and sustainability of energy distribution. The findings reveal that incorporating robust cybersecurity measures and integrating demand response and CCUS significantly influence operational efficiency and sustainability, evidenced by an increase in costs and pollution by approximately 11.43 % and 1.8 %, respectively, compared to deterministic approaches. This study underscores the importance of robust planning and the beneficial impact of a carbon system on load curve management and economic returns in smart energy systems.

Simplifying Land Cover-Geoprocessing-Model Migration with a PAMC-LC Containerization Strategy in the Open Web Environment

Land cover and its changes over time are significant for better understanding the Earth’s fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs.