World Academy of Science, Engineering and Technology

Authors: Haowen Shen, Fengxiang Chen

Abstract:

To ensure the stable operation and comfort of fuel cell vehicles, vehicle-level thermal management is essential. To coordinate the temperature control of key components and reduce the energy consumption of thermal management, this paper proposes an integrated thermal management solution. An integrated thermal management model is established, encompassing the fuel cell, lithium-ion battery, electric motor, and passenger cabin, with dedicated control strategies designed for high temperature conditions. The reinforcement learning algorithm, based on the actor-critic architecture, leverages both value estimation and optimal policy estimation for controller training to achieve optimal control performance. Simulation results demonstrate that this control strategy, through self-learning, can effectively regulate the temperatures of the cabin and battery without the need for a pre-established mathematical model.

Keywords: automotive air conditioning, fuel cell vehicles, reinforcement learning, thermal management

Procedia PDF Downloads 1

Authors: Ze Bai, Yaohua Zhao, Zhenhua Quan

Abstract:

To address the issues of increased compressor exhaust temperature and reduced heating performance of air-source heat pumps under low ambient temperature conditions, a supplementary heat PVT-air multiple heat sources vapor-injected direct expansion heat pump system based on micro heat pipe arrays (MHPA-VI-DXHP) was proposed. The heating performance of the system under winter heating conditions was studied through experiments, and the compressor parameters and heating performance of the system under different refrigerant charge amounts, compressor speeds, vapor injected temperatures, and load water temperatures were analyzed. The results indicated that the average compressor exhaust temperature of the system was 75.41 °C, the average heating capacity was 2.92 kW, and the average heating COP was 2.94. Compared to a refrigerant charge of 4.3 kg, a refrigerant charge of 5.5 kg resulted in an 11.64% decrease in vapor injected pressure, a 23.78% increase in total refrigerant circulation flow rate, a 0.83% decrease in compressor exhaust refrigerant specific entropy, a 3.62% decrease in compressor exhaust temperature, and a 21.98% increase in system heating capacity and a 20.78% increase in heating COP. Increases in system load, water temperature and compressor speed have a negative impact on the system's heating COP. However, an increase in vapor-injected temperature can significantly improve the system's heating performance. In conclusion, the proposed MHPA-VI-DXHP system combines solar, air and low-grade industrial waste heat sources, demonstrating strong competitiveness, reliability, and potential for widespread application.

Keywords: heat pump, vapor injected, solar energy, utilization of low-grade industrial waste heat, heating performance

Procedia PDF Downloads 1

Authors: Meng Yi, Leng Tian, Zaihe Chen, Jin Gao, Bocong Huang, Ran Zhou

Abstract:

CO2 flooding technology is widely applicable to low-permeability oil and gas reservoir development, offering significant potential for both enhanced oil recovery and carbon emission reduction. In alignment with national "dual carbon" goals, the oil and gas industry is prioritizing low-carbon transformation, particularly within resource development. The minimum miscibility pressure (MMP) of CO2 is a critical parameter governing the effectiveness of CO2 flooding applications. This paper systematically reviews the development of CO2 minimum miscibility pressure prediction techniques, analyzing both experimental determination methods and theoretical calculation approaches. Furthermore, it synthesizes current research on CO2 minimum miscibility pressure prediction under micro-nano pore confinement conditions and highlights the distinct displacement mechanisms of near-miscible flooding influenced by these confinement effects. These insights provide a foundational direction for advancing CO2 minimum miscibility pressure prediction technology.

Keywords: minimum miscibility pressure prediction, CO2 flooding technology, MMP of micro-nano pore, near-miscible flooding mechanism

Procedia PDF Downloads 2

Authors: Bocong Huang, Leng Tian, Aoyang Chen, Meng Yi, Xiaolong Chai, Zechuan Wang, Wenkui Huang

Abstract:

The unique lithology of tight conglomerate leads to severe microscopic heterogeneity. CO₂ pre-fracturing has shown promising potential for enhancing oil recovery. In conglomerate reservoirs, fractures function as the dominant seepage pathways. The matrix serves as the primary storage domain for oil. Deciphering the evolutionary traits of the matrix during CO₂ pre-fracturing is therefore of critical importance. In this paper, three lithologies were classified according to gravel size and content. CO₂ pre-fracturing experiments were conducted to analyze the evolution of microscopic pore structures and oil mobilization characteristics across four stages: hydraulic fracturing, CO₂ injection, soak period, and flowback. The results show that the type and content of cement in conglomerates govern their friability during operational processes. Well-sorted conglomerate cores generally exhibit lower permeability. Cores from the Baikouquan Formation (3300m depth) show superior porosity and permeability compared to those from the Wuerhe Formation (4000m depth). During high-pressure water injection, gravel-marginal fractures tend to form along the edges of gravel particles, which enhances inter-pore connectivity and increases total porosity by approximately 19.4%. However, due to the tightness of the reservoir, the matrix volume accessible to injected water is limited. The combined stages of CO₂ injection, soaking, and flowback exert a minor impact on total porosity, with an increase of only 2.9%. During CO₂ injection, oil in large, medium, and small pores within the conglomerate matrix migrates toward micro pores. As injection pressure increases, a fraction of the oil is mobilized, with the maximum mobilization ratio reaching 8.29%. During the soaking period, CO₂ dissolution in oil drives a small amount of oil into large and medium pores. The overall distribution of oil remains largely unchanged. During flowback, the mobilization efficiency of oil in large pores approaches 83%, while that in small pores is approximately 14.6%. The minimum threshold of mobilized pore throats being 0.13 μm.

Keywords: tight conglomerate, CO2 pre-fracturing, pore throat structure, high-pressure water injection, well soaking and flowback

Procedia PDF Downloads 6

Authors: Fatima Huseynzade, Musa Mutlu Can

Abstract:

The study focuses on understanding the photocatalytic efficacy of Li-doped nanoporous spinel oxide gallate (MGa₂O₄) thick films fabricated using tape casting systems. Transition metals, specifically Cu, Zn and Co, were introduced by M. Structural analysis was carried out through Rietveld refinements, revealing a discernible shift in lattice parameters that corresponded to alterations in electronic energy configuration and optical transmittance. In summary, the photocatalytic performance of spinel oxide gallate was determined to be contingent on the quantity of Li, impacting both structural modifications and the generation of electronic energy levels. Chemical synthesis technique was used to synthesize Lithium (Li) doped MGa₂O₄ (Cu, Co, and Zn) with the best ratio of 20%. Synthesized were in homogeneous size and with the desired stoichiometric ratio. Structural analysis and crystal structure of doped atoms were determined, and in the last study, the ionic conduction mechanism was done. First of all, the Lithium (Li) doped MGa₂O₄ (20%) powders produced chemically through citrate-based sol-gel route were pressed under 1.2 MPa to form 10mm wide (in diameter) & 1 mm thick pellets. Next, these pellets were annealed up to 900°C for 6 hours. AC electrical analysis was done on these pellets to obtain ionic conductivity and ionic activation energy. X-Ray powder diffractometer (XRD) and X-Ray photoelectron spectroscopy (XPS) were employed for structural characterizations. Subsequently, Rietveld refinements were conducted to investigate the variation in the amount of Li atoms within the spinel lattice. Additionally, the study delved into Li amount-dependent ionic conductivities under 1.5AM Sun illumination using AC impedance spectroscopy.

Keywords: renewable energy, fuel cells, spinel oxides, material science

Procedia PDF Downloads 9

Authors: Jae Hyeok Lee, Young Hyun Hong, Young-Gyu Jeon, Hyun-Kyung Kim

Abstract:

Lithium-ion capacitors are energy storage devices that combine the advantages of lithium-ion batteries and supercapacitors. They exhibit excellent cycle reversibility and rate performance, and can achieve a wider operating range than supercapacitors, resulting in higher energy density. In general, Lithium-ion capacitors (LIC) mitigate initial irreversibility, extend their lifespan, and enhance energy density by forming a Solid Electrolyte Interface (SEI) in advance through a pre-lithiation process on the anode. However, if there are cracks in the SEI formed during this process or if it forms unevenly, the SEI may continually grow, leading to ongoing consumption of Li+ ions, which reduces capacity. In addition, solvent molecules can penetrate directly into the electrodes and cause the electrodes to detach, shortening the lifespan of the battery and having a fatal effect on various battery performance. In this study, we induced uniform growth of the Solid Electrolyte Interphase (SEI) by employing an SEI-forming additive during the pre-lithiation process on soft carbon. Our goal is to enhance the operating voltage and boost cycle reversibility when using lithium-ion capacitors (LICs) as anodes by utilizing SEI-forming additives such as PTSS, TPBX, and LIDFP. Furthermore, we aimed to improve the rate capability by reducing the resistance of Li+ ions through the formation of a thin SEI. More details on the electrochemical and structural properties will be presented at the meeting.

Keywords: lithium-ion capacitors (LICs), solid electrolyte interface, SEI-forming additives, electrochemical performance

Procedia PDF Downloads 9

Authors: Young Hyun Hong, Young-Gyu Jeon, Jae Hyeok Lee, Hyun-Kyung Kim

Abstract:

Anode-free lithium metal batteries (AFLMBs) have attracted considerable attention owing to their high energy density and safety. However, the non-uniform lithium plating and stripping behavior on the lithiophobic Cu current collector is an unavoidable hurdle that results in the growth of lithium dendrites and dead lithium. This has caused its restricted utilization and hence poor coulombic efficiency. To address this issue, it is essential to achieve uniform and reversible lithium deposition on the Cu current collector. In this study, we have designed a thin composite layer by incorporating lithiophilic zinc oxide (ZnO) nanoparticles on nanoperforated graphene (NPG). In this composite layer, the lithiophilic ZnO nanoparticles and functional groups present on nanoperforated graphene sheets reduced the energy barrier for lithium nucleation and facilitated the uniform lithium deposition. Furthermore, the nanoperforations in the graphene structure worked as lithium-ion transportation. This has resulted in an increasing number of pathways for Li ion movement. Consequently, the thin ZnO/NPG composite layer can effectively induce uniform lithium deposition and reversible lithium plating/stripping reactions, leading to a high coulombic efficiency and long electrochemical cyclability.

Keywords: anode-free lithium metal batteries, ZnO/NPG composite, lithium deposition, coulombic efficiency

Procedia PDF Downloads 10

Authors: Young-Gyu Jeon, JaeHyeok Lee, Young Hyun Hong, Hyun-Kyung Kim

Abstract:

Lithium-ion batteries (LIBs) have been serving as one of the most advanced energy storage rechargeable devices in portable electronic devices and electric vehicles. On the other hand, it comprises aggressive, flammable, and explosive organic electrolytes, which raises high concerns about safety issues, such as the risk of fire or explosion. This calls for the development of safe and environment friendly electrolytes. All-Solid-State Batteries (ASSBs) utilize solid electrolytes over liquid electrolytes. The class of solid-state electrolytes has emerged as a promising, safe alternative. Oxide-based solid electrolytes are chemically stable but have high interfacial resistance, reducing Li-ion diffusion. Especially, ASSBs are often manufactured in composite cathode forms, which can lead to void formation and increased resistance. Minimizing this resistance is essential for commercialization. This study presents a synthesis of novel solid-state electrolyte Li₅.₈Ga₀.₄La₃Zr₂O₁₂(LGLZO) on carbon nanotube (CNT) surfaces (LGLZO/CNT) that conducts both electrons and ions. The LGLZO/CNT composite minimizes voids and enhances the charge supply, improving cathode performance. Further details will be discussed in the meeting.

Keywords: all-solid-state battery, oxide-based solid electrolyte, composite cathode, mixed electronic and ionic conductor

Procedia PDF Downloads 7

Authors: Sina Bonakdar

Abstract:

This paper presents the design and experimental validation of an advanced plasma-enhanced quantum vacuum stimulator (PQVS), integrating high-temperature superconductors (HTS), pulsed Helmholtz coils, a terahertz generator, and GaN/SiC-based switching circuits to achieve high-efficiency energy harvesting from quantum vacuum fluctuations. Operating in an ultra-high vacuum (UHV) at 77 K with liquid nitrogen cooling, the system employs YBa₂Cu₃O₇ nanostructures, femtosecond lasers, and cold plasma to stimulate zero-point energy (ZPE). Theoretical calculations indicate potential outputs in the milliwatt range with efficiencies of 15-20%, a significant advancement over prior nano- to microwatt outputs. The study details the system’s components, experimental setup, and COMSOL multi-physics simulations, comparing its performance with alternative energy harvesting methods. Challenges such as cooling complexity and quantum uncertainties are discussed, alongside applications in quantum physics, aerospace, and sustainable energy. The results establish the HTS-enhanced PQVS as a promising technology for next-generation energy solutions.

Keywords: plasma quantum vacuum stimulator, zero-point energy, a terahertz generator, femtoseconds lasers

Procedia PDF Downloads 8

Authors: Shan Huang, Ning Liu, Ying Fan

Abstract:

Climate Policy Uncertainty (CPU), driven by the complexity of climate governance and its integration with economic and social development, often leads firms to hesitate in making sustainable investments. ESG performance is a key indicator of a company’s sustainability capacity and serves as a tool for signaling stakeholders and managing risks. Understanding ESG strategies under CPU is crucial for both corporate development and global climate governance. Previous studies have focused on the impacts of single climate policies and physical climate change risks. As an important transition risk, this study fills the research gap in the field of climate policy and risk. While existing literature focuses on CPU’s impact on innovation and economic performance, this paper extends the analysis to ESG strategies as a sustainability indicator. Using the China-specific CPU index, the study provides empirical insights from a developing country context. It identifies two mechanisms—pre-investment and signaling—and offers policy recommendations for both governments and firms. The sample includes 3,634 observations from 882 Chinese non-financial listed companies between 2010 and 2022. The CPU index is built using a deep learning algorithm applied to 1.7 million articles from six major Chinese newspapers. ESG data is sourced from Refinitiv’s ASSET4 ESG database, offering comprehensive and objective metrics. A fixed-effects model is used to examine the impact of CPU on ESG performance, with robust tests conducted via variable replacement, EPU, and COVID-19 controls, and IV-2SLS methods. This study shows that firms improve ESG performance under lagged CPU. It enhances environmental and social performance, as these dimensions help firms hedge policy risk and signal to stakeholders directly, while governance performance remains largely unaffected due to its stability. Mechanism tests reveal that CPU drives ESG improvement through increased R&D investment as a preemptive strategy. Regarding the capacity for preemptive investment, CPU more strongly promotes ESG performance among state-owned enterprises, large-sized firms, and firms with lower financial constraints. Regarding the willingness for preemptive investment, CPU more effectively drives ESG improvement in high-pollution firms and those with executives of higher knowledge levels. The effect is stronger for firms with higher external oversight (e.g., institutional ownership and media attention), reflecting a stronger incentive to signal sustainability efforts. This study challenges the conventional belief that CPU hinders sustainable investment and supports the growth options theory. The research validates ESG as a signaling tool and fills key gaps by treating CPU as a transition risk, linking policy ambiguity to ESG strategies, and offering empirical evidence from an emerging economy via a China-specific CPU index. Policy recommendations suggest that firms should initiate comprehensive green transformations from internal governance structures, as enhancing ESG performance simultaneously addresses environmental governance and stabilizes market. Government can design gradual climate policy frameworks to encourage enterprises to proactively invest in ESG, while also enhancing the capacity of enterprises—particularly high-pollution ones—to engage in sustainable investments through the provision of financial support. This should be complemented by establishing mandatory ESG disclosure mechanisms and market feedback channels.

Keywords: Climate Policy Uncertainty, ESG performance, preemptive investment, signal

Procedia PDF Downloads 10

Authors: Khandoker Islam

Abstract:

Accurate load forecasting at the distribution level is essential for effective planning, investment decisions, and operational reliability in contemporary power systems. Traditional methods—ranging from time-series and regression models to machine learning approaches—often depend on aggregated consumption data or simplified assumptions, making them less effective in spatially diverse and rapidly urbanizing contexts. This paper introduces a field data-integrated load forecasting methodology that leverages various legislation classifications, alongside verified historical electricity consumption records, to generate high-resolution, grounded demand forecasts. The framework differentiates service areas into realistic constraints such as maximum building use, permitted land uses, and typical load saturation levels. By anchoring forecasts in customer location, explicit and regulatory-aware inputs, this approach enhances the precision of distribution load projections and enables more informed decisions in substation planning, transformer sizing, and feeder design. The proposed methodology represents a significant advancement over conventional forecasting models by aligning technical analysis with real-world urban development patterns and infrastructure limitations.

Keywords: c++, machine learning, power system module, machine manipulation

Procedia PDF Downloads 10

Authors: Ceren Cömert, Coşku Kasnakoğlu

Abstract:

Quadcopters are used in many research areas due to their maneuverability. However, agile maneuvers decrease flight duration. Thus, managing the state of charge of the battery became an essential part of the design. Estimating the state of charge of the batteries is crucial to optimizing energy management, planning flight duration, and extending the battery life cycle. Estimating the state of charge accurately also prevents overcharging and discharge. State of charge is a complex process and depends on many factors, like temperature, aging, etc. There are different techniques in order to estimate the state of charge. This paper reviews state-of-charge estimation methods and briefly introduces concepts like cell imbalance and cell balancing techniques. Furthermore, Coulomb counting and Kalman filter methods are used to estimate the state of charge of the quadcopter’s battery. State-of-health estimation techniques are included, too.

Keywords: quadcopters, state of charge, battery, coulomb counting, kalman filter

Procedia PDF Downloads 13

Authors: Muhammed L. Fatty, Abuzar Khan, Mohammed Qamar, Qasem Ahmed Drmosh

Abstract:

To address the urgent need for clean energy carriers, the authors reported the fabrication and evaluation of an indirect Z-scheme photoanode comprising sequentially sputtered W₁₈O₄₉, Au, and Bi2O3 thin films, followed by annealing at 600 °C. XRD and Raman analyses confirmed the coexistence of monoclinic W₁₈O₄₉, metallic Au, and α-Bi₂O₃ phases, with enhanced crystallinity upon Bi₂O₃ incorporation. SEM and XPS revealed a morphological evolution from grain-like W₁₈O₄₉ to porous nanorods in Bi₂O₃-Au-W₁₈O₄₉, accompanied by distinct chemical states of W, Au, and Bi. UV-Vis spectroscopy showed a pronounced red shift of the absorption edge to 556 nm for the BO10 sample, corresponding to a reduced band gap of 2.23 eV. Photoelectrochemical tests under 460 W illumination in 0.5 M Na₂SO₄ demonstrated a peak photocurrent density of 1.52 mA/cm² at 1.4 V vs. RHE for BO10, alongside a charge-transfer resistance of ~150 Ω and stable photocurrent over 4.24 h. These findings illustrate that strategic Au decoration and Bi₂O₃ coupling significantly enhance light harvesting, charge separation, and interfacial kinetics in PEC water splitting.

Keywords: Z-scheme, photoanode, charge-transfer, photocurrent density, photoelectrochemical, water splitting

Procedia PDF Downloads 11

Authors: Bárbara Brasil Marques

Abstract:

This paper aims to explore future projections for the supply and demand of low-carbon ammonia, particularly considering its emerging role as a maritime fuel. With the potential for supply shortages to drive up fertilizer production costs and, consequently, food prices, the paper also seeks to identify regulatory mechanisms that could influence these trends and help mitigate any negative impacts. This study employed a mixed qualitative-quantitative approach. It began with a review of relevant technical and scientific literature, critically assessing key studies to map the current state of the field. Data analysis involved a comparative examination of studies, identifying similarities and differences, and exploring their implications. Additionally, quantitative methods were applied through calculations and numerical comparisons to support the conclusions. This methodology was chosen for its ability to integrate diverse perspectives, leading to more robust and well-supported conclusions. The study highlights that ammonia demand is projected to nearly triple by 2050, driven by its emerging role as a maritime fuel, along with a 40% increase in nitrogen demand for traditional uses. However, predicting the supply of low-carbon ammonia, particularly green ammonia, is challenging, as it depends on green hydrogen production, which also has other potential applications. Current hydrogen production projects suggest that the supply won’t meet ammonia production needs by 2050, creating a significant supply-demand gap. This shortage and uncertainty surrounding low-carbon ammonia pose a major challenge to meeting growing demand. To address this, regulatory measures are essential to balance the needs of the maritime and fertilizer sectors, ensuring that ammonia production for fertilizers is not hindered by its rising use as a maritime fuel. The study suggests regulatory incentives to promote research and development of low-carbon ammonia, aiming to reduce costs and expand supply without one application undermining the other. Additionally, regulation can encourage the development of alternative maritime fuels, alleviating pressure on ammonia and fostering a more balanced fuel market. The conclusions also emphasize the need for international cooperation and policies that stabilize the ammonia supply chain, ensuring food security while meeting the evolving demands of global industries. This paper offers new insights by highlighting the significant gap between projected supply and demand for ammonia by 2050, and proposing regulatory solutions to balance sector needs. These findings are valuable for engineers, as they shed light on the logistical and economic challenges of transitioning to cleaner fuels. Promoting alternative fuels and stabilizing the supply chain are key factors for engineers working on renewable energy technologies and ensuring a sustainable future.

Keywords: low-carbon ammonia, nitrogen fertilizers, marine fuel, food security

Procedia PDF Downloads 11

Authors: Nora Arbaoui, Rachid Tadili

Abstract:

To enhance climatic conditions and boost agricultural production during the cold season in a Mediterranean climate, this project proposes the development of an integrated and autonomous solar system for heating, climate control, and post-harvest conservation in greenhouses. To evaluate the system's effectiveness, experiments were carried out using two identical north-south oriented greenhouses. The first is equipped with an active solar system integrated into the double-glazed roof, along with a thermal storage unit, while the second serves as a control greenhouse without any additional system. The solar system led to an average increase of 7 °C in the internal temperature of the experimental greenhouse compared to the outdoor environment, and 5 °C compared to the control greenhouse. This temperature gain created a more favorable microclimate for crop growth, positively impacting plant development, quality, and overall yield.

Keywords: renewable energy, solar system, agricultural greenhouse, heating, storage, drying

Procedia PDF Downloads 18

Authors: Saeed Yari, Liam Bird, Sepideh Rahimisheik, Albin Conde Reis, Mahsa Mohammad, Joke Hadermann, James Robinson, Paul Shearing, Momo Safari

Abstract:

In the pursuit of environmentally friendly battery technologies, this study investigates the microstructural and transport properties of water-processed sulfur electrodes and compares them with conventional N-Methyl-2-pyrrolidone (NMP)-based electrodes. Lithium-sulfur (Li-S) battery cathodes are the focus due to their potential for high energy density, low cost, and environmental sustainability.X-ray micro-computed tomography (X-ray μCT) was used to analyze electrode microstructures. The results show that sulfur particles in PVDF/NMP electrodes remain clustered, whereas in water-based electrodes using CMC-SBR or LiPAA binders, sulfur is more finely dispersed. This enhanced dispersion in water-processed electrodes is attributed to the high Zeta potential of aqueous dispersions, which suppresses particle aggregation—unlike in NMP-based formulations.Microstructural differences in the carbon-binder domain were also binder-dependent. CMC-SBR formed thin, continuous carbon-binder films that facilitated long-range connectivity between sulfur particles. Conversely, LiPAA generated thicker, more localized domains with short-range connectivity, limiting electronic pathways. These findings emphasize how binder selection directly affects the spatial organization of conductive components and thus overall electrode performance.Electrode thickness further influences structural integrity. In water-based systems, thicker electrodes were prone to cracking, especially around large sulfur agglomerates. These cracks can propagate through the electrode and result in delamination, compromising electronic conductivity. This is primarily caused by the high surface tension of water, which increases capillary pressure during drying, leading to stress accumulation and mechanical failure.Electrochemical impedance spectroscopy (EIS) using symmetric cells was performed to assess contact resistance and tortuosity—key indicators of ionic and electronic transport efficiency. NMP-based electrodes displayed lower tortuosity and stable resistance, reflecting their dense carbon-sulfur packing. Water-based electrodes showed greater variability: CMC-SBR’s well-connected network reduced contact resistance, whereas LiPAA’s isolated domains led to higher tortuosity and increased resistance.These differences significantly impact the electrochemical behavior of Li-S cells. Electrodes with better-connected carbon-binder networks facilitate electron transport and improve sulfur utilization, yielding better cycling stability and capacity retention. In contrast, poorly connected structures and high tortuosity hinder ion movement and increase polarization, degrading performance. Cracking and delamination in thicker water-processed electrodes further exacerbate these issues by disrupting conductive pathways.In conclusion, this study provides critical insights into the role of binder chemistry and processing in shaping the microstructure and electrochemical properties of water-based sulfur electrodes. By understanding and controlling these variables, researchers can develop environmentally sustainable Li-S batteries without compromising performance. These findings contribute to the growing body of knowledge needed to transition from toxic solvent systems to greener, high-performance alternatives in battery manufacturing.

Keywords: Li-S battery, water-based, tomogrpahy, transport

Procedia PDF Downloads 19

Authors: Umi Saadah

Abstract:

Bonded contact is widely utilized in finite element analysis (FEA) to model load transfer at connections in offshore wind structures. While this simplification improves computational efficiency, its accuracy in capturing real-world behavior remains a subject of debate. Offshore wind turbine foundations, including transition pieces and jacket structures, experience complex loading from wind, waves, and operational forces, potentially leading to slip, separation, and non-uniform stress distribution at connections. This study investigates the implications of using bonded contact in offshore wind structural simulations, comparing it with nonlinear contact models under varying environmental conditions. A comprehensive validation using field data and experimental studies is conducted to assess the impact on fatigue performance, failure predictions, and structural integrity. The findings provide best-practice recommendations for offshore wind developers regarding the appropriate use of bonded contact in design and verification stages.

Keywords: ANSYS APDL, bonded contact, finite element analysis, fatigue, jacket foundation, offshore wind turbine, structural integrity

Procedia PDF Downloads 15

Authors: Umi Saadah

Abstract:

This study introduces an efficient methodology for fatigue assessment of transition pieces (TPs) in jacket foundations for offshore wind structures, employing the influence matrix (IM) approach. Traditional fatigue analysis relies heavily on detailed finite element (DFE) simulations, which, while accurate, are computationally intensive and time-consuming, particularly across the full spectrum of Design Load Cases (DLCs). The IM method addresses this challenge by decoupling structural response calculations from external loading scenarios. It computes a pre-established matrix of unit load responses at critical hotspots, which is later used to reconstruct stress histories efficiently for various load conditions. The entire procedure is implemented using ANSYS APDL, where custom scripts generate and apply the IM to predict hotspot stresses with significantly reduced computational demand. The results demonstrate that the IM approach maintains high accuracy in stress prediction while reducing simulation time by several orders of magnitude. This makes it an effective solution for large-scale fatigue evaluations in offshore wind turbine (OWT) design and operation. Future enhancements may involve integrating machine learning models to automate and further accelerate the fatigue evaluation process.

Keywords: ANSYS APDL, fatigue analysis, influence matrix, jacket foundation, offshore wind turbine, transition pieces

Procedia PDF Downloads 15

Authors: Xu Lou, Jing Wang, Huiqing Liu

Abstract:

Oil shale has abundant reserves and is an important alternative resource for traditional oil and natural gas. In situ mining is the most promising method for oil shale, which mainly includes electric heating and steam injection heating methods. But both methods have obvious drawbacks Electric heating methods typically require significant energy consumption, while steam injection results in significant heat loss from the surface to underground. This article combines two methods organically and uses wind and solar complementary power to drive an electric heater, thereby heating steam. By introducing the volatility index complementarity rate, the energy supply effect of electric heating wells in the all-weather wind solar complementary process can be optimized. Then we substitute it into the in-situ transformation model of oil shale. The results show that in the process of simultaneous electric heating and steam injection, the complementary fluctuation of wind and solar energy supply will generate additional secondary seepage channels. This is mainly due to the instantaneous temperature change, which leads to local thermal stress concentration effect. From the perspective of temperature field, fluctuating temperature changes lead to periodic rapid heating in local areas and also trigger the "thermal stress fatigue" effect. At this point, the permeability mainly increases significantly along the path of the main damage fracture. This provides a new technological approach for efficient in-situ upgrading and development of oil shale.

Keywords: oil shale, electric heating, complementary wind and solar energy, fluctuating energy supply, seepage characteristics

Procedia PDF Downloads 13

Authors: Ahmed Mourtada Elseman

Abstract:

The improper disposal of spent lead-acid batteries poses significant environmental risks due to their toxic components. This study introduces an eco-conscious method to repurpose these hazardous wastes into high-value perovskite materials for photovoltaic applications. Lead halides (PbI₂, PbCl₂, PbBr₂) were successfully synthesized from recycled battery lead and characterized by XRD, XPS, TEM, UV–vis, PL, and optical bandgap analysis, confirming phase purity and optical activity. These halides were then used to fabricate CH₃NH₃PbI₃ perovskite thin films via inkjet printing at 1.0 M and 1.3 M precursor concentrations. Structural characterization indicated a tetragonal perovskite phase with a crystallite size reduction from 36.8 nm to 31.3 nm at higher concentration, along with a slight narrowing of the optical bandgap (1.557 eV to 1.546 eV), enhancing solar absorption. A lead halide recovery cost assessment demonstrated the economic viability of the recycling process. This research presents a scalable, low-cost, and sustainable route to integrate circular economy principles into solar cell fabrication, offering a viable pathway to reduce e-waste while accelerating clean energy adoption.

Keywords: spent lead acid batteries, lead halide, perovskite thin film, characterization

Procedia PDF Downloads 12

Authors: Abdulkadir Mustapha

Abstract:

The Ajaokuta-Kaduna-Kano (AKK) Gas Pipeline Project represents a pivotal development in Nigeria’s energy infrastructure, aimed at enhancing domestic gas utilization, industrial expansion, and national energy security. Spanning 614 kilometers and designed to transport pipeline-quality natural gas at a minimum pressure of 1,000 psig, the AKK pipeline connects the southern gas-rich region of Ajaokuta to the northern commercial hub of Kano. This study assesses the project from an engineering management perspective, analyzing financial viability, operational frameworks, and future implications for national development. Using a descriptive survey research design, data were collected from 370 stakeholders, including engineers, project managers, government officials, and environmental analysts selected through stratified random sampling. A structured questionnaire, validated for reliability using Cronbach’s Alpha (α = 0.89), facilitated the collection of both quantitative and qualitative data. The study identifies key challenges such as infrastructure deficits, environmental concerns, funding constraints, and operational inefficiencies. However, it also highlights the project’s potential to drive industrialization, reduce gas flaring, and serve as a model for sustainable energy infrastructure in Nigeria. Findings suggest that robust engineering management practices, strategic policy support, and stakeholder engagement are crucial to maximizing the project’s long-term impact. The study concludes with recommendations for improving project delivery, strengthening environmental compliance, and enhancing national energy policy frameworks.

Keywords: engineering management, gas pipeline, pipeline project, energy infrastructure, Nigeria, Ajaokuta-Kaduna-Kano (AKK)

Procedia PDF Downloads 18

Authors: Umi Saadah

Abstract:

This study presents an advanced finite element analysis (FEA) approach to assess the corrosion-fatigue interaction in the transition piece (TP) of offshore wind turbine jacket foundations. Offshore wind structures operate in harsh marine environments, leading to material degradation due to cyclic loading and corrosion-induced stress weakening. While extensive research has been conducted on fatigue life assessment, limited studies have explored the combined effects of corrosion progression and fatigue performance on the structural integrity of TP. This research develops a numerical framework integrating time-dependent corrosion models and fatigue life estimation within an FEA-based verification methodology using Ansys. The methodology incorporates degradation of material properties, realistic loading conditions, and cumulative damage analysis. A case study based on Taiwan’s offshore wind farms (Chang-Hwa OWF) demonstrates the impact of corrosion-induced degradation on the fatigue resistance of TP. The results highlight critical failure locations, life expectancy reduction trends, and structural optimization insights. The study provides a robust predictive approach to improve design strategies, inspection, and maintenance planning of offshore wind foundations, with significant implications for extending service life and reducing operational risks.

Keywords: finite element analysis, offshore wind, transition piece, corrosion fatigue interaction, structural integrity, fatigue life prediction

Procedia PDF Downloads 19

Authors: Vipul Raj

Abstract:

While significant developments have been seen in modern large wind turbines, one of the major gaps in the vertical-axis wind turbine (VAWT) industry is the lack of adaptive control algorithms that are more suitable for a wide range of smaller and structurally simpler turbine types. In addition, a more simple and compact control strategy independent of anemometers is always a great advantage in commercial implementations of the technology. Although real-time wind measurements are a part of the modern power control strategy, it is not practical to implement the approach in small-scale VAWTs. Modern VAWTs use direct shaft connection between turbine rotors and generators. This simple structure makes it difficult and uneconomical to mount any wind measurement components. This is also supported by the fact that VAWT does not require yaw control, as it is omnidirectional and could be set at low positions. In view of the simplified structure and impracticality of the small system, an anemometer-free VAWT is always of commercial value. While this can be an issue during wind speeds as the generator system would find it unsuitable to adapt to the varying wind speed cases (low and high), here comes the need of an optimized adaptive algorithm that can help the entire machine side systems to adjust accordingly to the varying wind speeds and, this here in this project is implemented by the generator electromagnetic torque control. The project investigates blade dynamics and shaft dynamics of designed and fabricated VAWT prototypes in a modified wind tunnel (that exists in the campus of the University of Queensland) and, then conducts an experimental approach to study the turbine - generator relationship to develop a control logic that relates the generator Voltage and current to the rotor torque and angular velocity for optimal power production. The control strategy is then optimized for maximum power point tracking and less response time. The hint of the control logic would be: (1) A known load is assigned to the generator; (2) Find the wind speed from the power curve (power being function of torque and angular velocity); (3) There would be a point of peak generator power as a function of the rotor angular velocity and torque; (4) Optimize VAWT system to adapt the best rotor speed to obtain maximum power. Both 3-blade and 5-blade VAWTs are considered for this project at different pitch angles so that to find the best configuration that generates the optimum result. The airfoil profile chosen for the VAWT prototypes is WUP1615, which was developed by the wind team of the University of Padua.

Keywords: wind turbines, vertical axis wind turbines, optimization, generator torque control, PMSG generator

Procedia PDF Downloads 20

Authors: Hani Abdallah Al-Rawashdeh

Abstract:

Solar chimneys present a promising renewable energy solution, especially in regions with abundant solar resources like Jordan. They can harness solar energy for electricity generation and water desalination, showcasing their potential as a sustainable energy system. The integration of solar chimneys with desalination systems further emphasizes their ability to address both energy and water scarcity issues effectively. Solar chimneys help reduce dependence on fossil fuels, lowering greenhouse gas emissions and mitigating climate change. They also foster local economic development by creating jobs in the construction, operation, and maintenance sectors, especially in rural areas. Jordan's high solar radiation levels and vast desert areas make it an ideal location for solar chimney projects. However, careful adherence to seismic design codes and environmental impact assessments is necessary to ensure resilience and minimize ecological disruption.

Keywords: photovoltaic, concentrated system, cooling system, solar chimneys

Procedia PDF Downloads 24

Authors: Arbaoui Nora, Rachid Tadili

Abstract:

The utilization of solar energy for heating agricultural greenhouses represents a sustainable method for enhancing crop productivity, particularly during colder seasons. This study details the development and implementation of a cost-effective solar heating system aimed at improving the greenhouse microclimate while decreasing dependence on conventional heating methods. By employing a natural heating and cooling technique, the system significantly reduces fuel consumption and CO₂ emissions, establishing itself as an environmentally friendly alternative to traditional greenhouse heating solutions. The proposed heating system is innovative in three primary ways. First, it is designed to optimize solar energy absorption through a straightforward and original method, which bypasses the need for complex and expensive technologies. Second, it uses water as a heat transfer fluid, ensuring efficient energy storage and distribution throughout the greenhouse. Third, the system features thermal storage tanks positioned inside the greenhouse— a distinctive characteristic that negates the requirement for additional insulation. This strategic placement allows the stored heat to be released directly into the greenhouse environment at night, thereby maintaining an optimal temperature for plant growth. Experimental results from the cold season illustrate the system's effectiveness in regulating the greenhouse climate. In comparison to a control greenhouse, the experimental greenhouse equipped with the solar heating system experienced a temperature increase of 4°C. Additionally, the system succeeded in maintaining an indoor temperature that was 6°C higher than the external ambient air. These findings underscore the potential of this approach to enhance energy efficiency in greenhouse agriculture while promoting sustainability. By integrating an economical and efficient solar heating solution, this study advances eco-friendly agricultural practices. The results indicate that the proposed system can significantly reduce both greenhouse energy costs and environmental impact, all while ensuring stable growing conditions for crops throughout the colder months.

Keywords: renewable energy, solar system, agricultural greenhouse, heating, storage

Procedia PDF Downloads 18

Authors: Swati Sharma

Abstract:

The present work reports the asymmetric supercapacitor device of surfactant-assisted zeolitic imidazolate framework-8 (ZIF-8) with outstanding performance utilizing various electrolytes. For this, ZIF-8 was synthesized utilizing a solvothermal technique with non-ionic (polyvinylpyrrolidone; P1) and cationic (cetyltrimethylammonium bromide; C1) surfactants. Comprehensive characterization techniques, including X-ray diffraction and Brunauer-Emmett-Teller revealed the cubic phase (I-43 m space group) and the formation of macropores of the surfactant-modified electrodes, respectively. Scanning electron microscopy, field emission scanning electron microscopy, and high-resolution transmission electron microscopy elucidated the morphological impact of different surfactants on fabricated electrodes. The structural information is captured by RAMAN, and Fourier transforms infrared analysis. Electrochemical investigations in a three-electrode configuration confirmed that the optimized C1 sample demonstrated exceptional energy storage performance, achieving a significant specific capacity of 2442.5 C/g at a current density of 5 A/g. In addition, employing C1 (cathode) and activated carbon (anode) electrodes in the device yielded remarkable performance characteristics in wide (0.0-2.0 V) potential window, including a specific capacitance of 212.2 F/g (6 A/g), an energy density of 117.9 Wh/kg (6 A/g), and a power density of 16670.8 W/kg (30 A/g). Moreover, at 25 A/g, the device exhibited excellent coulombic efficiency (100%) and cycling stability (83.3%) over 16,000 charge-discharge cycles. The practical applicability of the device was demonstrated by powering light emitting diodes. These findings provide insights into the significance of surfactant-assisted electrodes in developing sustainable energy storage systems and highlight its adaptability for a range of supercapacitor applications.

Keywords: zeolitic imidazolate framework-8, solvothermal method, surfactants, organic electrolyte, asymmetric supercapacitor device

Procedia PDF Downloads 19

Authors: Žydrūnas Kavaliauskas, Mindaugas Milieška, Giedrius Blažiūnas, Giedrius Gecevičius, Hassan Zhairabany

Abstract:

Due to the increasing global energy demand and the need to manage climate change, hybrid renewable energy systems (HRES) integrating solar, wind, biomass and energy storage technologies have become key solutions to ensure a sustainable and reliable energy supply. However, the variability of renewable sources and the complexity of grid integration require advanced tools for accurate forecasting and optimized system design. This study examines the application of artificial intelligence (AI), in particular deep learning frameworks such as TensorFlow and PyTorch, and the ensemble-based Random Forest algorithm, to improve power generation forecasting, grid load forecasting, and conceptual design of HRES projects. By integrating forecasting modeling with locally adapted system proposals, the study contributes to improving energy efficiency and reducing CO₂ emissions, thereby helping to achieve global sustainability goals. The research methodology is based on a comprehensive database covering a decade of data from authoritative sources such as IRENA Renewable Energy Statistics, ENTSO-E Transparency Platform, Global Wind Atlas, and Global Solar Atlas. This data, which includes solar, wind and biomass energy generation, grid load, and CO₂ emissions, was processed using the pandas library for time series analysis, normalized with StandardScaler and split into training (80%) and testing (20%) sets using a sliding window approach (look_back = 30). The impact of greenhouse gas emissions reductions was an important part of the study, predicting the decline in CO₂ emissions as the share of renewable energy sources increased from 10% to 90%. TensorFlow predicted an 80% reduction in emissions, with a steep decline up to 50% of the share of renewables, after which the decline slowed down, indicating diminishing returns as the share of renewables increases. The study findings highlight the transformative potential of AI in energy systems management. The stability of TensorFlow is suitable for rapid adaptation, while the consistency of PyTorch supports long-term planning and sustainability strategies. The Random Forest model, while valuable for smaller datasets or non-time series tasks, still remains useful. By providing accurate forecasts and tailored HRES designs, these AI tools help optimize energy generation, improve grid stability, and accelerate the transition to a low-carbon future. The proposed HRES designs, especially PyTorch’s focus on wind and solar, match the renewable energy potential of the study area, suggesting sustainable energy supply systems.

Keywords: hybrid renewable energy systems, artificial intelligence, deep learning, neural networks

Procedia PDF Downloads 19

Authors: Žydrūnas Kavaliauskas, Mindaugas Milieška, Giedrius Blažiūnas, Giedrius Gecevičius, Hassan Zhairabany

Abstract:

The growing global demand for energy and the need to lower carbon dioxide (CO₂) emissions are making the integration of renewable energy sources (RES) into power systems increasingly important. This study explores the application of advanced artificial intelligence (AI) techniques for generation forecasting, load prediction, and CO₂ emission evaluation in hybrid renewable energy systems (HRES). Deep learning frameworks such as TensorFlow and PyTorch, alongside the classic Random Forest algorithm, were employed to develop neural network models for solar, wind, and biomass energy production, as well as for grid load forecasting. Historical datasets were utilized to train predictive models based on LSTM, MLP architectures, and ensemble methods. Model performance was compared, revealing that TensorFlow generally provided more stable and accurate forecasts for time series data, while PyTorch demonstrated strong generalization capabilities. A key innovation of this work is its holistic perspective, combining HRES forecasting techniques with the conceptual design of renewable energy systems for specific sites. The findings indicate that increasing the share of renewable energy from 10% to 90% could potentially cut CO₂ emissions by up to 80%. TensorFlow models suggested that the most significant emission reductions occur when increasing renewable penetration from 10% to 50%, after which the reduction rate diminishes. Conversely, PyTorch models indicated a more linear decline in emissions across the full range. These insights highlight how AI-driven forecasting can enhance HRES management and offer practical strategies for localized energy system planning.

Keywords: Hybrid Renewable Energy Systems, Artificial Intelligence, Deep Learning, Neural Networks

Procedia PDF Downloads 20

Authors: Nurlan Najafzade

Abstract:

As urbanization continues to rise globally, the demand for sustainable energy solutions has become increasingly critical. This research paper explores strategies for optimizing solar energy efficiency in urban environments, where unique challenges such as limited installation space, shading from buildings, and urban pollution can hinder solar energy performance. The paper begins with an overview of the significance of solar energy in urban settings and the potential it holds for reducing reliance on fossil fuels. It then delves into advanced measurement tools essential for assessing solar energy efficiency, including sun path analyzers, shading analysis tools, PV performance monitoring systems, and thermal cameras. Key strategies such as maximizing roof space utilization, implementing Building-Integrated Photovoltaics (BIPV), deploying adaptive solar tracking systems, and utilizing energy storage solutions are discussed in detail, highlighting their potential to enhance energy output and reliability. Furthermore, innovative ideas for future advancements in solar technology are presented, emphasizing the importance of integrating smart technologies and community engagement in optimizing solar energy systems. This research aims to contribute to the ongoing discourse on renewable energy solutions in urban areas, providing actionable insights for policymakers, urban planners, and researchers seeking to foster sustainable energy practices in cities.

Keywords: solar energy, urban environments, energy efficiency, measurement tools, sustainable solutions

Procedia PDF Downloads 16

Authors: Nangoma Yudaya Nassali

Abstract:

Hydropower is a vast source of energy, providing over 87% of total renewable electricity production, thus playing a key role in green power generation. Furthermore, it has a fundamental influence on power market prices since it can be used as a cache for more volatile renewable sources, and it is relatively cheap to scale up and down. Thus the prediction of power or energy is such a crucial matter that it will assist policy and decision-makers in auspiciously noticing the sudden or abrupt changes in the electricity demand. This study compared the performance of multi-layer perceptron feedforward, convolutional neural network deep learning models and the random forest for regression analysis and statistical techniques for development of the prediction models. In addition, a traditional physical model was implemented as a baseline for comparison. Daily data on water levels and lake discharge downstream of Isimba HPP recorded at SW-R, Victoria Nile at Mbulamuti water level gauge for a period of 31 years (1991 – 2022) was obtained from the Ministry of Water and Environment, Directorate of Water Resources Management were used as the main input variables. Prediction models based on hourly data were developed, the traditional GM (1,1) physical model was used as a baseline for comparison, the random forest regressor, the MLP-supervised deep learning model (FFNN) and CNN for the prediction of output power generation. The performance metrics used for evaluation were: Pearson Correlation Coefficient (r), mean absolute percentage error (MAPE), root mean square error (RMSE), the Nash Sutcliffe Efficiency (NSE) and mean absolute error (MAE). The hydropower generation model achieved the following regression fitting results with the MLP with the highest accuracy with a MAPE of 17.74%, MAE of 20.51MW, RMSE of 24.88, an NSE of 0.95 and Pearson Correlation Coefficient R of 1.00 showing the best results for hydropower prediction. The supervised deep-learning model developed for the prediction of hydropower generation will reduce the operational and maintenance costs and increase or optimize the energy output of hydropower generation. The results can thus help policymakers and organizations to plan energy management using evidence-based forecasts and manage water and energy resources more efficiently.

Keywords: artificial neural networks, machine learning, maintenance cost, prediction models

Procedia PDF Downloads 22