Storage Management Research Articles

Quantitative assessment of CO2 leakage risk in geologic carbon storage management

AbstractLarge‐scale geological storage of carbon dioxide (CO2) is indispensable for mitigating climate change but faces significant challenges, especially in the accurate quantitative assessment of leakage risks to ensure long‐term security. Given these circumstances, this paper proposes an innovative approach for quantitatively assessing CO2 leakage risk to address the previous limitations of limited accuracy and insufficient data. We construct a fault tree and transform it into a Bayesian network–directed acyclic graph, and then use judgment sets along with fuzzy set theory to obtain prior probabilities of root nodes. The feature, event, and process method was utilized to identify key components and subsequently determine the conditional probability table (CPT) of the leaf node. The subjective experience assessments from experts are defuzzified to obtain the CPTs of intermediate nodes. The obtained basic probability parameters are input into the directed acyclic graph to complete the model construction. After calculating the leakage probability using this model, it is combined with the severity of impacts to conduct a comprehensive risk assessment. Furthermore, critical CO2 risk sources can be determined through posterior probability calculations when intermediate nodes are designated as deterministic risk events. The gradual implementation process of the proposed model is demonstrated via a typical case study. The results indicate an overall CO2 leakage probability of 29%, with probabilities of leakage along faults/fractures, caprock, and well identified as 32%, 28%, and 19%, respectively. The project is categorized as a medium‐low risk level. When leakage is confirmed, tectonic movement, thickness, and delamination at interface connections/the presence of cracks are the critical risk sources, and measures to mitigate key risks are outlined. The identified key risk factors conform to empirical evidence and previous research, validating the accuracy of the model. This study is instrumental in CO2 geological storage risk assessment and scalable development program design. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Economic impacts of a drastic gas supply shock and short-term mitigation strategies

The Russian invasion of Ukraine on February 24, 2022 entailed the threat of a drastic and sudden reduction of natural gas supply to the European Union. This paper presents a techno-economic analysis of the consequences of a sudden gas supply shock to Austria, one of the most dependent countries on imports of Russian gas. Our analysis comprises (a) a detailed assessment of supply and demand side countermeasures to mitigate the immediate shortfall in Russian gas imports, (b) a mapping of the net reduction in gas supply to industrial sectors to quantify direct economic shocks and expected relative reductions in gross output and (c) the quantification of higher-order economic impacts through using a dynamic out-of-equilibrium input–output model. Our results show that potential economic consequences can range from relatively mild to highly severe, depending on the implementation and success of counteracting mitigation measures. We find that securing alternative gas imports, storage management, and incentivizing fuel switching represent the most important short-term policy levers to mitigate the adverse impacts of a sudden import stop.

Experimental study on natural convection heat transfer performance of microencapsulated phase change material slurry in a square cavity

Microencapsulated phase change slurry (MicroEPCMS), consisting of microencapsulated phase change material (MicroEPCM) and base liquid, represents a novel latent heat functional fluid that can absorb a large amount of heat within its phase change temperature range. It has found widespread use as a functional liquid cooling medium for heat dissipation in batteries, electronic devices, and other devices. In this work, MicroEPCMS with different MicroEPCM concentration were prepared. The physical properties of MicroEPCMS including density, thermal conductivity, and dynamic viscosity were tested within different temperature ranges. The results showed that the MicroEPCMS exhibits a superior thermal energy storage capacity compared to water. In the phase change range of MicroEPCM, the MicroEPCMS has a higher thermal conductivity compared to water. In addition, the natural convection heat transfer characteristics of MicroEPCMS in a square cavity were investigated under varying heating power. The results indicated that MicroEPCMS can enhance natural convection heat transfer by absorbing heat with a minimal temperature rise during the phase change process compared to water. Moreover, higher heating power enables natural convection enhancement of MicroEPCMS in a square cavity. These results underscore the significant potential of MicroEPCMS as a functional liquid cooling medium for applications in thermal energy storage and thermal management.

Simulation of PSDF (Photovoltaic, Storage, Direct Current and Flexibility) Energy System for Rural Buildings

The PSDF (photovoltaic, storage, direct current, and flexibility) energy system represents an innovative approach aimed at achieving carbon neutrality. This study focused on rural buildings and utilized Modelica to develop a dynamic simulation model of the PSDF system. The research introduced a framework for direct current distribution microgrid systems with flexible regulatory mechanisms, employing a virtual inertia control strategy to provide stable adjustments for flexible operations and support integration with local grids. Case simulation results indicated that the system equipped with a water tank saved 3.15 kWh compared to the system without a water tank, resulting in an energy savings rate of 22.14%. Compared to traditional photovoltaic systems, the PSDF system significantly enhanced energy management flexibility and system reliability through the integration of thermal storage and battery management. This research made significant contributions to the fields of renewable energy and building energy systems by offering a scalable and practical solution suitable for rural contexts.

Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.

Post Harvest Management Techniques of Few Selected Crops in Assam, India

Reducing agricultural loss or waste is the ultimate goal of post-harvest management techniques. Post-harvest management has become crucial in the present environment of growing population and declining agricultural land and other resources. Ensuring food security in a manner that is safe for both humans and the environment is currently the world's top priority. The FAO estimates that in order to feed the nine billion people on the planet by 2050, food production would need to rise by seventy percent. Therefore, a deep comprehension of the roles played by agro-ecosystems is necessary. A vast number of agricultural products has been produced in recent years, yet there has been a lack of advancement and uptake of post-harvest technologies, leading to significant post-harvest losses. Agricultural crops have 16–36% post-harvest losses each year due to microbiological, physiological, and mechanical losses. Because most products are highly perishable, they require much more care and strategies to ensure that value addition and processing are encouraged. The value chain for processing has become essential to enhancing national food security and promoting food safety. The value chain in agriculture crops post-harvest management includes pre-harvest variables, harvesting, market preparation (pre-cooling, sorting, grading, packaging, and on-farm storage), transportation, storage, value addition/processing, and by-product waste management. The chapter focuses mostly on few selected fruit, vegetable, and cereal crops while discussing current advancements in post-harvest technology and food crop value addition.

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Thermal performance and characterization of phase change material composites reinforced with nano-particles in thermal energy storage systems: a review

Abstract The energy storage now a days is a cumbersome process to exploit its best use. This review paper discusses the challenges of efficiently utilizing energy storage and proposes phase-change materials (PCMs) with Nano-particle
reinforcement as a solution, particularly for storing solar thermal energy. Various synthesis methods for PCM, including impregnation and encapsulation, are examined, with emphasis on factors like particle size, shape, and solid content. Carbon-based materials, including carbon nanotubes and graphene oxide, emerge as superior options due to their reliability, cost-effectiveness, lightweight nature, and high heat transfer efficiency, with minimal environmental impact. This review highlights the enhanced thermal conductivity of Nano-particle-reinforced PCM composites, emphasizing their thermally stable, durable, and conductive properties. Additionally, it discusses thermal performance through techniques like DSC, TGA, and DTG, along with material characterization methods such as FTIR, SEM, XRD, EDX, and XPS analysis. Overall, the research underscores the promising potential of Nano-particle-reinforced PCM composites for efficient energy storage and thermal management applications.

Location-aware job scheduling for IoT systems using cloud and fog

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An "intelligent gateway" based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

Recent Advances in Intraoral Scanners.

Intraoral scanners (IOSs) have emerged as a cornerstone technology in digital dentistry. This article examines the recent advancements and multifaceted applications of IOSs, highlighting their benefits in patient care and addressing their current limitations. The IOS market has seen a competitive surge. Modern IOSs, featuring continuous image capture and advanced software for seamless image stitching, have made the scanning process more efficient. Patient comfort with IOS procedures is favorable, mitigating the discomfort associated with conventional impression taking. There has been a shift toward open data interfaces, notably enhancing interoperability. However, the integration of IOSs into large dental institutions is slow, facing challenges such as compatibility with existing health record systems and extensive data storage management. IOSs now extend beyond their use in computer-aided design and manufacturing, with software solutions transforming them into platforms for diagnostics, patient communication, and treatment planning. Several IOSs are equipped with tools for caries detection, employing fluorescence technologies or near-infrared imaging to identify carious lesions. IOSs facilitate quantitative monitoring of tooth wear and soft-tissue dimensions. For precise tooth segmentation in intraoral scans, essential for orthodontic applications, developers are leveraging innovative deep neural network-based approaches. The clinical performance of restorations fabricated based on intraoral scans has proven to be comparable to those obtained using conventional impressions, substantiating the reliability of IOSs in restorative dentistry. In oral and maxillofacial surgery, IOSs enhance airway safety during impression taking and aid in treating conditions such as cleft lip and palate, among other congenital craniofacial disorders, across diverse age groups. While IOSs have improved various aspects of dental care, ongoing enhancements in usability, diagnostic accuracy, and image segmentation are crucial to exploit the potential of this technology in optimizing patient care.

ELECTRIC VEHICLE POWERTRAIN DESIGN: INNOVATIONS IN ELECTRICAL ENGINEERING

The electrification of vehicles is reshaping transportation, with electric vehicle (EV) powertrain design playing a key role in this shift. This review focuses on recent innovations in electrical engineering that have enhanced EV powertrains, particularly in areas like electric motors, power electronics, energy storage, and thermal management. Advances in motor technology, such as permanent magnet synchronous motors (PMSM), and the integration of silicon carbide (SiC) and gallium nitride (GaN) semiconductors have improved efficiency and reduced heat generation. Battery innovations, including solid-state technologies and advanced battery management systems, along with regenerative braking, have extended EV range and efficiency. Power electronics, including inverters and onboard chargers, now utilize wide-bandgap semiconductors to minimize energy losses and improve thermal performance. Additionally, novel cooling solutions are addressing thermal challenges in EV systems. Looking forward, modular powertrain architectures and AI-driven control strategies offer promising advancements for various vehicle types. This review provides an overview of how electrical engineering innovations are driving the future of EV powertrain design and sustainable transportation.

Pengelolaan Cold Storage 1.000 Ton Di Muara Baru Jakarta: Perspektif Ekonomi Kelembagaan Baru

Fish cold storage was built by the Ministry of Maritime Affairs and Fisheries in various regions as one of the implementations of the National Fish Logistics System policy, which aims to maintain the availability of fish for industrial raw materials and domestic consumption. The cold storage of 1,000 tons was established to become a role model for the government's buffer stock facility in the Jakarta area. However, its implementation is still experiencing obstacles because its use is not optimal and has not attracted the interest of fisheries players in the broader scope. The research objective is to examine the institutional system implemented in managing cold storage of 1,000 tons from a business perspective. The research uses a case study method in government cold storages located in Muara Baru, North Jakarta. The institutional analysis of cold storage management was analyzed descriptively and qualitatively using the New Institutional Economic Theory approach. The research results show that the institution of cold storage of 1,000 tons still needs to improve its management. The aspects that contribute most to the lack of business performance in cold storage are property rights and, flexibility and adaptability. The implications that can be given are institutional improvements through 1) developing business models that are more tailored to user needs, 2) establishing bodies or institutions that are more authoritative in public services, 3) aligning incentives and building cooperation with a wider range of users, 4) implementation of standard procedures balanced with commitment to compliance from stakeholders and strong controls, 5) increased speed and flexibility of services and 6) competitive pricing.

Wind power forecasting with metaheuristic-based feature selection and neural networks

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

Energy Storage Management Using Artificial Intelligence to Maximize Polish Energy Market Profits

Energy Storage Management Using Artificial Intelligence to Maximize Polish Energy Market Profits

A comparative study of strategic petroleum reserve policies of major countries in the world

Petroleum security is the guarantee of national security that plays an important role in national energy security, while petroleum strategic reserve is the most basic guarantee of national petroleum security. At present, the number of selected countries in the study of petroleum strategic reserves is insufficient, as there are too much vertical comparison and the horizontal comparison analysis is not enough. Based on this, on the basis of comparing the strategic petroleum reserves of major countries in the world in terms of storage methods, address selection, policy formulation, management system and utilization mechanism, this paper conducts longitudinal comparison and horizontal comparative analysis and research, trying to find out their common policy characteristics and advantages from different strategic petroleum reserve policies, modes and practices of various countries, and carries out eliminating the rough and learning from the strong. The experience gained can be used for reference by other countries.

Application of Artificial Intelligent Techniques for Power Quality Improvement in Microgrid System

This paper focuses on modeling, control and power quality improvement of a microgrid connected system. This last was designed as a multi-converter system with Wind Turbine driven Permanent Magnet Synchronous Generator, and lithium ion Battery Storage Energy System. These sources are connected by a continuous bus to a nonlinear load through a DC /AC converter and three-phases multi-functional voltage source inverter. Where, wind systems are considered as primary power resource, and the grid is used for the effect of consumption of the overage power available from sources, when the battery has been fully charged. Multi Functional Voltage Source Inverter is used to ameliorate the performance of the proposed system, guaranteeing both reactive power and harmonic compensation. Moreover an intelligent control by fuzzy logic control algorithm are managed In order to extract the maximum power from wind turbine, to guarantee an effective storage management, this about the DC side. For the AC side a direct power control strategy with fuzzy logic algorithm have been used. The simulation of the system solution is carried out in Matlab/Simulink. The obtained simulation results show that the technique of fuzzy logic furnish the best solution in term of robustness, optimization performance, low THD and fast dynamic response.

Microstructural influence on compressive behavior and strain rate sensitivity of open-cell nickel foam

Open-cell nickel foam (OCNF) is a highly porous, three-dimensional material with potential applications in various fields, such as energy storage, catalysis, and thermal management. However, the microstructural effect of pores per inch (PPI), cell size and strain rate (SR) sensitivity on the mechanical properties of OCNF has not been fully explored. We characterized the samples of OCNFs using digital image correlation (DIC), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) mapping. Our results show that the OCNF exhibits a linear elastic response under quasi-static compression, with high compressive strength and a low compressive modulus. The dynamic compression test results demonstrate that the OCNF has a high energy absorption capacity and good structural stability. We designed and constructed a method to generate the 3D meshes based on the scans of the OCNFs obtained by X-ray micro-CT. The 3D meshes allow the finite element method to capture the key features of the mechanical behaviors with high fidelity. The simulated mechanical behavior of the OCNFs demonstrates strong concordance with the observed experimental findings.

Optimal management of shared energy storage in remote microgrid: A user-satisfaction approach

This study investigates the utilization of renewable energy technologies, such as solar photovoltaics (PV) and energy storage, to reduce reliance on fossil-fuel microgrid generators in remote areas. However, renewable energy alone is not enough to address the existing challenges. Therefore, to optimize microgrid performance, it is crucial to incorporate shared energy storage and demand-response (DR) strategies from the demand side. Additionally, prosumers engaging in DR often encounter user-satisfaction issues. In this study, we propose a shared energy storage model that considers user satisfaction in remote areas. Additionally, we compared three energy storage models: individual, neighbor, and communal, to assess their effectiveness. To validate our findings, we juxtaposed two pricing scenarios: fixed and time-of-use (TOU) rates. The results of our study indicate that using shared energy storage leads to operational cost savings and increases user satisfaction compared to other scenarios. Finally, we employed the social index rate and performed a sensitivity analysis to determine the optimal operation that balances economic and environmental considerations.

A Collaborative Digitisation and Curatorial Project, Highlighting a Unique Collection and the Latimer Core Data Standard

Sir Joseph Banks is remembered for being a long-standing president of the Royal Society, the unofficial first director of Kew gardens and the pioneering naturalist on Captain James Cook’s great voyage onboard the Endeavour, to observe the transit of Venus and search for an undiscovered southern continent (British Museum (Natural History) 1906). Much of Bank’s life is well documented but his surviving entomology collection has never been accurately catalogued. The Banks Collection at the Natural History Museum (NHM) London is an historic assemblage of insect specimens (Fig. 1, British Museum (Natural History) 1906). It includes specimens collected by Banks and others acquired through a world-wide network of collectors. During his lifetime, Banks shared specimens with his associates and gave many specimens to Dr. William Hunter and Johan Christian Fabricius. After his death, the remaining collection was donated to the Linnean Society and later passed to the British Museum in 1863. The Banks Collection has both historical and cultural value and continues to be a relevant research tool. This is largely because Fabricius, a student of Carl Linnaeus, described many new species from the collection. Consequently, the collection contains many taxonomically important type specimens. The number of specimens in the collection is unknown but estimated to be approximately 4000. The NHM is digitising the collection with the generous support of the Charles Hayward Foundation. The collection is housed in 55 entomological glass-topped drawers, albeit not the original drawers. When conserving historical material, there is an argument to leave everything in its original state, but after much consideration, the curatorial team decided—for the long-term preservation of the collection—the specimens should be rehoused into plastazote®-lined drawers keeping the original layout of the specimens and replacing the current cork-lined drawers as part of the digitisation process. High-resolution images are taken of every specimen with its associated labels. The information on the labels is recorded for each specimen and a barcode added. As of August 2024, 3,300 specimens have been digitised and 30 of the 55 drawers recurated. More than 5000 high-resolution images have been taken (Fig. 2) and 438 labels have been transcribed for almost 1200 specimens. This project is using a new Biodiversity Information Standards (TDWG) data standard, Latimer Core, designed to support the representation and discovery of natural science collections (Woodburn et al. 2022). Latimer Core is intended to be complimentary to specimen-level standards such as Darwin Core (Darwin Core Task Group 2009), providing a way to structure and share higher-level information about groups of collection objects, from whole-museum collections through thematic and historic collections, to the contents of a single drawer. This is useful for collections with lots of associated data, fragile specimens or when displacement and disassociation of information is a concern. Unlike modern specimens, with data labels containing information on collecting event and associated persons, specimens in the Banks collection have few original labels. It was common practice that labels were temporary data storage and disposed of once the data was published. A replacement label was provided after publication (Fig. 2). The publication provides species description, geographic origin and often the name of the collection containing the specimen. Additional labels were subsequently added by curators and researchers. Many specimens have no data labels (Fig. 3). Unravelling important information about individual specimens, species and groups of specimens in this collection, including taxonomic, type status and origin will be supported by Latimer Core, as it allows a more holistic approach compared to only using specimen-level data. We can better incorporate data derived from various sources such as published data, illustrations and personal correspondence to learn about the individual and groups of specimens, the reverse of the usual workflow. Digitising this collection will improve access through digital records (specimen/drawer images, transcribed labels, publication references) reducing the need for physical examination and risk of damage. Whole-drawer digitisation in addition to specimen-level imaging provides information on the organisation and display of a collection. A collection of this nature demands minimal handling and the best storage and collections management procedures to ensure its survival for future generations. However, its significance commands a continued interest by a wide and varied audience. By digitising this collection, we will improve its physical housing, increase its accessibility without compromising the specimens for the future, and support the publication of a comprehensive catalogue of the collection.

A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities

The discrete-element method (DEM) has become a pivotal tool for investigating soil–plant interactions in agricultural and environmental engineering. This review examines recent advancements in DEM applications, focusing on both the challenges and opportunities that shape future research in this field. This paper first explores the effectiveness of DEM in simulating soil and plant materials, including seeds, roots, and residues, highlighting its role in understanding interactions that affect agricultural practices. Challenges such as long computation times and the complexity of determining accurate contact parameters are discussed, alongside emerging methods like machine learning that offer potential solutions. Notable advancements include the integration of machine learning algorithms for contact parameter estimation, the use of expanded particle models for dynamic processes, and the development of new techniques for detailed post-processing of DEM simulations. The review also identifies key future research directions, including the incorporation of environmental factors such as air and water, and the exploration of residue management for carbon storage and erosion prevention. By addressing these challenges and seizing these opportunities, future research can enhance the accuracy and applicability of DEM models, advancing our understanding of soil–plant interactions and contributing to more sustainable agricultural and environmental practices.