Storage Process Research Articles

How to Staff When Customers Arrive in Batches

In many different settings, requests for service can arrive in near or true simultaneity with one another. This creates batches of arrivals to the underlying queueing system. In this paper, we study the staffing problem for the batch arrival queue. We show that batches place a dangerous and deceptive stress on services, requiring a high amount of resources and exhibiting a fundamentally larger tail in those demands. This uncovers a service regime in which a system with large batch arrivals may have low utilization but will still have nontrivial waiting. Methodologically, these staffing results follow from novel large batch and large batch-and-rate limits of the multiserver queueing model. In the large batch limit, we establish the first formal connection between general multiserver queues and storage processes, another family of stochastic models. By consequence, we show that the batch scaled queue length process is not asymptotically normal and that, in fact, the fluid- and diffusion-type limits coincide. Hence, the (safety) staffing of this system must be directly proportional to the batch size just to achieve a nondegenerate probability of wait. In exhibition of the existence and insights of this large batch regime, we apply our results to data on Covid-19 contact tracing in New York City. In doing so, we identify significant benefits produced by the tracing agency’s decision to staff above national recommendations, and we also demonstrate that there may have been an opportunity to further improve the operation by optimizing the arrival pattern in the public health data pipeline. This paper was accepted by Baris Ata, stochastic models and simulation. Funding: We acknowledge the generous support of the National Science Foundation (NSF) through Andrew Daw’s Graduate Research Fellowship under grant DGE-1650441, received during the initial phase of this research as part of his doctoral studies at Cornell University. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03979 .

Sustainability assessment in food analysis: the application of Green Analytical Chemistry tools to phthalate residue analysis in edible oils

Edible oils are essential in daily diet due to their high contribution of fatty acids, fat-soluble vitamins, and triglycerides. During the processes of growing, processing, storage, transport, or packaging, they can be contaminated by ubiquitous phthalate esters, considered endocrine disruptors. Thus, analytical methodologies to allow tight control of their presence are mandatory. Several sample treatments have been considered in this study: microwave-assisted extraction (MAE), liquid-liquid extraction (LLE), dispersive solid phase extraction (DSPE), magnetic SPE (MSPE), solid phase microextraction (SPME), Surface-enhanced Raman spectroscopy (SERS) or its combinations. However, the selection of an analytical procedure is usually performed from an Analytical Chemistry perspective, without considering factors such as the sustainability of the selected methodology and/or its impact on the environment. In this sense, the Green Analytical Chemistry strategy can play an important role. To demonstrate this fact, six different analytical procedures have been evaluated in terms of sustainability by using several greenness evaluation tools. Procedures from MAE-gel permeation chromatography (GPC)-SPE till SERS, showing adequate analytical characteristics, have been selected. The application of Analytical GREEnness calculator (AGREE), AGREE preparation (AGREE prep), and blue applicability grade index (BAGI) tools showed that MAE-GPC-SPE was the less green analytical procedure while SERS was the greener one. The BAGI evaluation showed that headspace (HS) and SERS were the most applicable procedures.

Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements.

A Review on Storage Process Models for Improving Water Quality Modeling in Rivers

Water quality is intricately linked to the global water crisis since the availability of safe, clean water is essential for sustaining life and ensuring the well-being of communities worldwide. Pollutants such as industrial chemicals, agricultural runoff, and untreated sewage frequently enter rivers via surface runoff or direct discharges. This study provides an overview of the key mechanisms governing contaminant transport in rivers, with special attention to storage and hyporheic processes. The storage process conceptualizes a ubiquitous reactive boundary between the main channel (mobile zone) and its surrounding slower-flow areas (immobile zone). Research from the last five decades demonstrates the crucial role of storage and hyporheic zones in influencing solute residence time, nutrient cycling, and pollutant degradation. A review of solute transport models highlights significant advancements, including models like the transient storage model (TSM) and multirate mass transport (MRMT) model, which effectively capture complex storage zone dynamics and residence time distributions. However, more widely used models like the classical advection–dispersion equation (ADE) cannot hyporheic exchange, limiting their application in environments with significant storage contributions. Despite these advancements, challenges remain in accurately quantifying the relative contributions of storage zones to solute transport and degradation, especially in smaller streams dominated by hyporheic exchange. Future research should integrate detailed field observations with advanced numerical models to address these gaps and improve water quality predictions across diverse river systems.

Neuromorphic Computing for Smart Agriculture

Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future.

A Design and Safety Analysis of the “Electricity-Hydrogen-Ammonia” Energy Storage System: A Case Study of Haiyang Nuclear Power Plant

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy.

Thermochemical heat storage and material behavior of calcium hydroxide fine powder in a fluidized bed reactor

The Ca(OH)2/CaO reaction has attracted much attention in thermochemical heat storage. However, commercial Ca(OH)2 is usually offered as a fine powder that tends to agglomerate in the fluidized bed and affects heat storage performance. The experimental study on these issues is incomplete. In this paper, a fluidized bed thermochemical heat storage test system is built to study the heat storage and release process of Ca(OH)2 fine powder. The experimental results indicate that the water vapor is the primary factor in agglomeration. The increase in heat storage temperature, fluidization velocity, and inlet water flow rate can all reduce reaction time. However, the effect of heat storage temperature on agglomeration is negligible. While a high fluidization velocity can alleviate the agglomeration, a high inlet water flow rate intensifies the agglomeration. The heat storage density decreases by about 12 % after 5 cycles owing to the material loss, while the agglomeration is trending upward. The specific surface area and specific pore volume decrease by 46.59 %, and 13.89 % respectively, which slows down the heat storage process. This work can serve as a reference for alleviating the agglomeration, as well as the operation and adjustment of the fluidized bed.

Self-limited and reversible surface hydration of Na2Fe(SO4)2 cathodes for long-cycle-life Na-ion batteries

Air stability is a crucial factor in the practical application of a battery material, as it profoundly affects the material's preparation, storage, and electrode fabrication processes. Sodium iron sulfate cathodes, despite their attractive attributes in cost and electrochemical performance, are widely believed to be unstable upon air exposure because of the sulfate group. Here we report remarkable air-stability of the Na2Fe(SO4)2-based (NFS) cathodes (minimal decay in 20 % RH air for 60 days, 91.9 % capacity retention after 3500 cycles in half cells) and their outstanding cycle performance in practically relevant pouch-type full cells (∼100 Wh kg-1 specific energy, >1000 cycle-life). Although the NFS cathodes do react with moisture H2O to produce Na2Fe(SO4)2⋅4H2O but the hydration is spatially confined at the NFS particles’ surface and not propagating into their bulk. Further, the structural changes are reversible when the surface-hydrated NFS particles are heated in the typical electrode vacuum-drying process, avoiding extra treatment and additional cost. This work reveals the promising properties of the NFS cathode materials towards high-performance and sustainable Na-ion batteries.

Thermodynamic, economic and environmental analyzes the compression and storage processes of green hydrogen involved in fueling station for vehicular use

Thermodynamic, economic and environmental analyzes the compression and storage processes of green hydrogen involved in fueling station for vehicular use

Full radionuclide analysis of polymetallic nodules from the Clarion-Clipperton-Fracture Zone in the NE Pacific

Polymetallic Nodules from the deep sea are currently being targeted for mineral exploration as they are a significant resource for various critical metals. In addition to accumulating such metals, nodules are also known to adsorb naturally occurring radioactive nuclides. With the possibility of exploitation of nodules becoming more likely within the next years, it is important to assess the potential radiation exposures resulting from the handling of polymetallic nodules. In this study we present, for the first time, specific activities of all long-lived alpha, beta and gamma emitters from the natural decay chains of Uranium-238, Uranium-235 and Thorium-232, in bulk and surface material of the same nodules obtained from 10 locations within the Clarion-Clipperton Zone. The results show elevated specific activities for the nuclides Th-230, Ra-226, Pb-210, Po-210, as well as Pa-231 and Ac-227 in accordance with prior reported activities. However, in contrast to assumptions made in previous studies, our analyses show that Pa-231 is not in equilibrium with its daughter nuclide Ac-227. The emanation factor of Rn-222 for dry and water-saturated nodules has also been determined and is higher than previously reported. The results presented here are a key ingredient to assess human radiation exposure during the processes of polymetallic nodule mining, storage and beneficiation.

Comprehensive enhancement of melting-solidifying process in latent heat storage based on eccentric fin-foam combination

A novel fin-foam combination, applying upward fins to accelerate natural convection and downward foams to dominate thermal conduction, was established to comprehensively enhance the melting and solidifying performance of horizontal shell-and-tube latent heat storage devices. Enhancement mechanisms of the current design were numerically investigated and compared with the typical eccentric and concentric design the whole melting-solidifying process. Among investigated enhancement designs, the fin-foam combination achieved the best results, reducing the melting time and the solidifying time by 47.9% and 55.4% respectively. Meanwhile, the relative difference between the melting and solidifying time was reduced to 12%, indicating a significant mitigation of the buckets effect of solidifying caused by eccentric designs. Further, the effect of the total enhancement material usage was investigated. Results showed that the marginal effect of the heat transfer enhancement started at 9 fins and 0.91 porosity, but the relative difference between melting and solidifying time then dropped below 0.61%. The economic assessment showed that increasing the amount of enhancement material can significantly improve the storage capacity per unit cost when the price ratio of the enhancement material to PCM is less than 10, indicating a considerable applicability and cost performance of fin-foam combination under different power density demands.

Evaluation of Rijndael Algorithm for Audio Encryption by Brute Force Attack

The use of information transfer is a major reason for the spread of information piracy, especially digital information that includes audio or other information, to preserve data. Encrypting voice messages is considered one of the most secure ways to protect information due to the difficulty of disclosing it. Our algorithm is robust, effective, efficient, and has security capabilities due to the different key lengths and block sizes (128 bits, 192 bits, or 256 bits). The Rijndael algorithm is an effective tool for encoding audio files and has won many awards. The Rijndael algorithm is compatible with many different audio formats. We will use it to encrypt WAVE files. The first step is to generate keys using the proposed method consisting of key lengths and block sizes (128 bits, 192 bits, or 256 bits). We convert the audio data into a 256 sample. After that, the cipher text will be generated for us. Once we encrypt the audio file, we will have several audio files. We will then use a proactive approach to evaluate the success of this initiative through a brute force attack to break the code. The Rijndael algorithm plays a role in this study by demonstrating the effectiveness of encryption, Reijndeal encryption, is designed to withstand brute force attacks, employs a strong key, proper encryption algorithm, secure hardware, and software to prevent side-channel attacks, and thwart cryptanalysis attempts. It also offers a cutting-edge perspective. Its primary goal is to protect data across the digital landscape during transmission, storage, and protection processes.

A molten salt energy storage integrated with combined heat and power system: scheme design and performance analysis

To investigate the flexibility and economic characteristics of a molten salt-combined heat and power (CHP) integrated system under different heat sources, this paper proposes a design scheme for a molten salt-CHP system based on flue gas heat storage, comparing it with main steam heat storage and reheated steam heat source schemes. First, a molten salt heat release sub-loop is designed, where the steam heated by the molten salt can either compensate for heating demands or enter the low-pressure turbine for work, meeting the flexible operation requirements of the unit. Secondly, in response to fluctuations in temperature and pressure parameters of the main steam and reheated steam in the flue gas heat storage scheme, a water-spray desuperheating device is arranged. Finally, a simulation experiment based on a 350 MW CHP was conducted, and the results show that the flue gas molten salt heat storage technology significantly reduces energy loss on the boiler side. Underrated load, compared to the main steam and reheated steam heat storage schemes, the flue gas heat storage scheme has the highest boiler exergy efficiency and total energy utilization efficiency, at 55.8% and 59.1% respectively. During the thermal storage process, the coal consumption index of the flue gas heat storage scheme decreases with increasing load, while conversely, during the heat release process, it increases with the load. The peak-shaving capacity increases with the load, reaching 78.4 MWh under the 75% THA condition. In the steam extraction heating scenario, coupling with the molten salt subsystem can expand the safe heating operation range of the original unit, increasing the maximum peak shaving range by 13.9%, and increasing the maximum heating capacity by 65.4%.

Extending the π‐Conjugation of a Donor‐Acceptor Covalent Organic Framework for High‐Rate and High‐Capacity Lithium‐Ion Batteries

AbstractRealizing high‐rate and high‐capacity features of Lihium‐organic batteries is essential for their practical use but remains a big challenge, which is due to the instrinsic poor conductivity, limited redox kinetics and low utility of organic electrode mateials. This work presents a well‐designed donor‐acceptor Covalent Organic Framework (COFs) with extended conjugation, mesoscale porosity, and dual redox‐active centers to promote fast charge transfer and multi‐electron processes. As anticipated, the prepared cathode with benzo [1,2‐b:3,4‐b′:5,6‐b′′] trithiophene (BTT) as p‐type and pyrene‐4,5,9,10‐tetraone (PTO) as n‐type material (BTT‐PTO‐COF) delivers impressive specific capacity (218 mAh g−1 at 0.2 A g−1 in ether‐based electrolyte and 275 mAh g−1 at 0.2 A g−1 in carbonate‐based electrolyte) and outstanding rate capability (79 mAh g−1 at 50 A g−1 in ether‐based electrolyte and 124 mAh g−1 at 10 A g−1 in carbonate‐based electrolyte). In addition, the potential of BTT‐PTO‐COF electrode for prototype batteries has been demonstrated by full cells of dual‐ion (FDIBs), which attain comparable electrochemical performances to the half cells. Moreover, mechanism studies combining ex situ characterization and theoratical calculations reveal the efficient dual‐ion storage process and facile charge transfer of BTT‐PTO‐COF. This work not only expands the diversity of redox‐active COFs but also provide concept of structure design for high‐rate and high‐capacity organic electrodes.

Vertical bedrock shifts reveal summer water storage in Greenland ice sheet

The Greenland ice sheet (GrIS) is at present the largest single contributor to global-mass-induced sea-level rise, primarily because of Arctic amplification on an increasingly warmer Earth1–5. However, the processes of englacial water accumulation, storage and ultimate release remain poorly constrained. Here we show that a noticeable amount of the summertime meltwater mass is temporally buffered along the entire GrIS periphery, peaking in July and gradually reducing thereafter. Our results arise from quantifying the spatiotemporal behaviour of the total mass of water leaving the GrIS by analysing bedrock elastic deformation measured by Global Navigation Satellite System (GNSS) stations. The buffered meltwater causes a subsidence of the bedrock close to GNSS stations of at most approximately 5 mm during the melt season. Regionally, the duration of meltwater storage ranges from 4.5 weeks in the southeast to 9 weeks elsewhere. We also show that the meltwater runoff modelled from regional climate models may contain systematic errors, requiring further scaling of up to about 20% for the warmest years. These results reveal a high potential for GNSS data to constrain poorly known hydrological processes in Greenland, forming the basis for improved projections of future GrIS melt behaviour and the associated sea-level rise6.

Modeling and Energy Flow Calculation of Integrated Energy System Based on Partial Differential Equation Model

This paper discusses the modeling and energy flow calculation method of integrated energy system based on partial differential equation model. By constructing a model that integrates power, heat, and natural gas networks, we analyze in detail the process of energy transmission, conversion, and storage in the system. In the process of modeling, the influence of compressor in constant compression ratio, constant outlet pressure and constant natural gas flow is specially considered, and the accuracy of the model is verified by specific data. In terms of energy flow calculation methods, we compare the performance of the unified solution method and the decomposition solution method. Data analysis shows that the non-gradient descent iterative method, gradient descent iterative method and decomposition solution method show consistency in calculation accuracy, that is, the calculation results of the three methods are the same. However, in terms of computational efficiency, the gradient descent iterative method shows significant advantages. Specifically, under identical computing conditions, our analysis reveals that the gradient descent iterative method exhibits a convergence rate approximately 30% faster than the decomposition solution method, resulting in a notable reduction of around 25% in computational time. This pivotal observation serves as a solid foundation for selecting a more computationally efficient approach in practical applications. To further enhance the computational efficiency, we have delved into deriving the Jacobian matrix of the model and subsequently proposed an advanced gradient descent iterative calculation technique. Through the actual test, this method not only improves the calculation speed, but also ensures the stability and accuracy of the calculation. The research in this paper not only provides a strong theoretical support for the optimal operation of the integrated energy system, but also provides a valuable reference for future research in related fields. Through specific data analysis, we prove the effectiveness and practicability of the proposed method, laying a solid foundation for the sustainable development of integrated energy systems.

Extending the π-Conjugation of a Donor-Acceptor Covalent Organic Framework for High-Rate and High-Capacity Lithium-Ion Batteries.

Realizing high-rate and high-capacity features of Lihium-organic batteries is essential for their practical use but remains a big challenge, which is due to the instrinsic poor conductivity, limited redox kinetics and low utility of organic electrode mateials. This work presents a well-designed donor-acceptor Covalent Organic Framework (COFs) with extended conjugation, mesoscale porosity, and dual redox-active centers to promote fast charge transfer and multi-electron processes. As anticipated, the prepared cathode with benzo [1,2-b:3,4-b':5,6-b''] trithiophene (BTT) as p-type and pyrene-4,5,9,10-tetraone (PTO) as n-type material (BTT-PTO-COF) delivers impressive specific capacity (218 mAh g-1 at 0.2 A g-1 in ether-based electrolyte and 275 mAh g-1 at 0.2 A g-1 in carbonate-based electrolyte) and outstanding rate capability (79 mAh g-1 at 50 A g-1 in ether-based electrolyte and 124 mAh g-1 at 10 A g-1 in carbonate-based electrolyte). In addition, the potential of BTT-PTO-COF electrode for prototype batteries has been demonstrated by full cells of dual-ion (FDIBs), which attain comparable electrochemical performances to the half cells. Moreover, mechanism studies combining ex situ characterization and theoratical calculations reveal the efficient dual-ion storage process and facile charge transfer of BTT-PTO-COF. This work not only expands the diversity of redox-active COFs but also provide concept of structure design for high-rate and high-capacity organic electrodes.

INTELIGÊNCIA EMOCIONAL NAS ORGANIZAÇÕES: ATIVO OU PASSIVO NA PERSPECTIVA DOS ADMINISTRADORES?

this paper aims to analyze and investigate how supply chain management can represent a competitive advantage for organizations. With the advent of the pandemic caused by the covid-19, there has been an exponential growth in the world e-commerce, and this has required managers and companies to adopt new models of economy capable of anchoring the planning, execution and control of effective actions the management of fundamental assets for the oxygenation of purchasing, logistics, storage, production and delivery processes to consumer markets. The new technologies of communication and telecommunications, which put new tools, such as artificial Inteligence, robotics and Big Data, at the service of this intricate gear of Supply Chain Management, at the same time also pose new challenges to the administrators and the organizations that need to adapt to the new demands and demands of the markets and consequently to the new ways of thinking strategically the delivery of value and the competitive advantage for the stakeholders.

Streamlining logistics: Innovating cross-docking distribution systems for operational excellence

A logistics service company in Batam faces challenges related to warehouse load fulfillment and sorting inaccuracies. This study aims to identify proposed efficiency improvements to the goods distribution system using the cross-docking method. The research method chosen is cross-docking, a technique that eliminates the storage process in the warehouse, thus saving time and cost. The research findings show significant benefits, especially in achieving zero inventory efficiency. Data processing and discussion revealed that efficiencies were apparent by increasing the sorting tables from 1 to 6, with an output of 90,000 kg during aircraft loading and unloading (compared to approximately 77,000 kilograms). This efficiency arises from the larger output of the sorting tables compared to the input, eliminating the need for warehousing and adding ten trucks. As a result, the shipment can be completed in one trip, with no goods stored in the warehouse. The analysis shows that implementing cross-docking in the company increases efficiency in distributing goods to forwarding partners.

Efficient and effective ensemble broad learning system based on structural diversity

Ensemble Broad Learning Systems (ENBLS) have received considerable attention from researchers and experienced rapid development due to its outstanding performance in handling complex data. A study has revealed that when ENBLS is trained with multiple independent storages, the issue of memory consumption is inevitable. To address this issue, this paper proposes an Ensemble Broad Learning Systems based on structural diversity (EBLSSD). This method effectively reduces the model memory consumption while maintaining superior performance. In EBLSSD, the scaling vectors are trained by constraining the scaling vectors of the current model and those of other existing models. These vectors are used to search for hidden paths of subnetworks within the network, extracting different network structures for integration. By preserving the scaling vectors during the storage process, memory pressure is reduced. EBLSSD introduces diversity from the perspective of network structure, which reduces cooperation between nodes and the risk of model overfitting, thereby enhancing model performance. The feasibility and effectiveness of the proposed algorithm is validated by experimental results on public datasets.