Storage Model Research Articles

Study on heat storage characteristics and carbon optimisation in the sintering process

In response to the phenomenon of automatic heat storage during the sintering process of iron ore, heat balance analysis concerning the main chemical reactions and physical heat carried by gas and solid materials was conducted. A unit heat storage model for the sintering process was established. Further, a mathematical model to solve the heat transfer characteristics between different sintering layer units was proposed and established based on temperature–time curves of the gas and solid phases. Using these models, the heat storage of each layer unit was calculated under the condition of uniform carbon allocation. Subsequently, the optimisation of carbon allocation at each layer unit for maximising the utilisation of heat storage was investigated. The heat required for sintering by the layer unit was taken as the constraint, and the maximum utilisation of heat stored in each layer unit was taken as the goal to optimise the carbon allocation in the height direction. The calculation results show that as the unit thickness is 100 mm and the total thickness of the layer is 600 mm, the heat transfer ratio of the upper unit to the lower unit is 34.22%, 30.29%, 14.74%, 9.49%, and 1.17%, respectively. The heat storage and available heat storage rate increase with the increase of layer height, with heat storage ranging from 0.69 × 104 to 5.14 × 104 KJ/t and heat storage rate ranging from 11.63% to 49.37%. After optimising carbon allocation, the unit achieved a maximum carbon reduction of 60.04%, resulting in an overall reduction in carbon allocation of 46.20%. This adjustment aligns more closely with the industrial processes.

Massive Data HBase Storage Method for Electronic Archive Management

ABSTRACTThe acceleration of the digitalization process in enterprise and university education management has generated a massive amount of electronic archive data. In order to improve the intelligence, storage quality, and efficiency of electronic records management and achieve efficient storage and fast retrieval of data storage models, this study proposes a massive data storage model based on HBase and its retrieval optimization scheme design. In addition, HDFS is introduced to construct a two‐level storage structure and optimize values to improve the scalability and load balancing of HBase, and the retrieval efficiency of the HBase storage model is improved through SL‐TCR and BF filters. The results indicated that HDFS could automatically recover data after node, network partition, and NameNode failures. The write time of HBase was 56 s, which was 132 and 246 s less than Cassandra and CockroachDB. The query latency was reduced by 23% and 32%, and the query time was reduced by 9988.51 ms, demonstrating high reliability and efficiency. The delay of BF‐SL‐TCL was 1379.28 s after 1000 searches, which was 224.78 and 212.74 s less than SL‐TCL and Blockchain Retrieval Acceleration and reduced the delay under high search times. In summary, this storage model has obvious advantages in storing massive amounts of electronic archive data and has high security and retrieval efficiency, which provides important reference for the design of storage models for future electronic archive management. The storage model designed by the research institute has obvious advantages in storing massive electronic archive data, solving the problem of lack of scalability in electronic archive management when facing massive data, and has high security and retrieval efficiency. It has important reference for the design of storage models for future electronic archive management.

Coupled hydro-mechanical model for underground hydrogen storage undergoing cyclic injection and production

ABSTRACT Excess renewable energy can be used to generate and store hydrogen underground, offering a secure and econimical solution to the intermittency challenges of renewable energy sources. The success of underground storage relies on understanding and controlling the geo-mechanical integrity of storage sites during cyclic loadings. While geological deformation during natural gas and carbon dioxide storage is well studied, few have investigated pore pressure and in-situ stress variations during cyclic loading, which is unique to underground hydrogen storage sites. This paper presents a predictive analytical model for the pore pressure and stress changes during cyclic loading. The model was derived based on continuous point source injection, a basic hydro-mechanical model while applying the superposition to consider the cyclic loading. The developed model was evaluated for hydrogen and natural gas subjected to cyclic injection and production. Comparing hydrogen and natural gas storage cases can offer valuable insights into the behavior of hydrogen since the deformation of natural gas storage is more studied. Finally, analytical solutions for both storage cases were validated by numerical models with discrepancies between the models amounting to less than 1%. Comparative analysis of the results from 3 to 99 cycles of injection and production for both hydrogen and methane demonstrates that rise in cycle numbers leads to a corresponding rise in stress accumulation in the system. Neglecting the stress accumulation during these cycles can lead to significant geomechanical issues, potentially compromising the storage system’s safety and integrity.

Comparison between the models of potential storage and simulation of the demand for electricity transformer (400/11) in Diyala General Company

Storage models are usually used to determine the optimal order quantity and reorder point by obtaining the lowest total cost of storage to balance the amount of production and the amount of demand for materials, but sometimes these models can not be applied, so the use of simulation is resorted to where accurate and flexible results can be relied upon, to achieve the goal of the study, which is to improve the company's storage system. This research dealt with two cases of the probabilistic storage model, where the first model was applied to the monthly demand data on one of the products of Diyala General Company, which is the distribution transformer (400|11) for the years 2019,2020,2021. The second is the probabilistic storage model by simulating demand and comparing the two models, whichever is better to reach optimal. Where the distribution of demand was found to find the average and standard deviation and apply the model to obtain the indicators of the models. Using the MATLAB program, the statistical analysis of the data was carried out and the indicators of the model were obtained.

Targeting Sodium in Heart Failure.

A dominant event determining the course of heart failure (HF) includes the disruption of the delicate sodium (Na+) and water balance leading to (Na+) and water retention and edema formation. Although incomplete decongestion adversely affects outcomes, it is unknown whether interventions directly targeting (Na+), such as strict dietary (Na+) restriction, intravenous hypertonic saline, and diuretics, reverse this effect. As a result, it is imperative to implement (Na+)-targeting interventions in selected HF patients with established congestion on top of quadruple therapy with angiotensin receptor neprilysin inhibitor, β-adrenergic receptor blocker, mineralocorticoid receptor antagonist, and sodium glucose cotransporter 2 inhibitor, which dramatically improves outcomes. The limited effectiveness of (Na+)-targeting treatments may be partly due to the fact that the current metrics of HF severity have a limited capacity of foreseeing and averting episodes of congestion and guiding (Na+)-targeting treatments, which often leads to dysnatremias, adversely affecting outcomes. Recent evidence suggests that spot urinary sodium measurements may be used as a guide to monitor (Na+)-targeting interventions both in chronic and acute HF. Further, the classical (2)-compartment model of (Na+) storage has been displaced by the (3)-compartment model emphasizing the non-osmotic accumulation of (Na+), chiefly in the skin. 23(Na+) magnetic resonance imaging (MRI) enables the accurate and reliable quantification of tissue (Na+). Another promising approach enabling tissue (Na+) monitoring is based on wearable devices employing ion-selective electrodes for electrolyte detection, including (Na+) and (Cl-). Undoubtably, further studies using 23(Na+)-MRI technology and wearable sensors are required to learn more about the clinical significance of tissue (Na+) storage and (Na+)-related mechanisms of morbidity and mortality in HF.

Grain storage temperature prediction based on chaos and enhanced RBF neural network

Grain storage has very strict temperature requirements. Aiming at the problems of nonlinear characteristics and poor prediction accuracy of temperature parameters in grain storage, a combination of chaos theory and enhanced radial basis neural network is proposed as a temperature prediction model for grain storage (C-ERBF). The model first determines the embedding dimension and time delay of the grain storage temperature sequence using chaos theory. It then calculates the Lyapunov exponent to confirm its chaotic properties and reconstructs the sequence in the phase space to extract the hidden dynamic information and structure behind the sequence. Furthermore, the q-Normalized Least Mean Square Fourth (qXE-NLMF) algorithm is designed to enhance the radial basis function (RBF) neural network model for weight updating, to improve its prediction accuracy, and to accelerate the training speed of the model. As verified by the simulation experiments of Mackey–Glass chaotic time series prediction, the enhanced RBF (ERBF) network has faster convergence speed and lower steady-state error compared to the traditional RBF network. Finally, the optimized dataset from chaos theory is input into the model to achieve accurate predictions of grain storage temperature series. The experimental results show that the proposed C-ERBF model has higher prediction accuracy compared to other time series prediction methods. It can realize the grain pile temperature in advance, and take control measures in advance. This proactive approach significantly reduces the consumption of stored grain and prevents issues before they arise.

Cold Storage Temperature Monitoring with Trilinear Interpolation

The Internet of Things (IoT) is widely applied in agriculture and in-dustry. This article proposes a 3-dimensional temperature monitoring model for cold storage, enhancing the ability to observe and detect abnormal temperature changes within the storage. Using real-time temperature acquisition and applying trilinear interpolation techniques, this model determines temperatures at all loca-tions in the storage. It accepts temperature values and sensor locations, appropri-ately segmenting the cold storage space to apply trilinear interpolation and deter-mine the temperature value at every location in the storage. The model was tested based on different number of sensors and different sensor locations. Based on that, we provide an optimal solution for the number of sensors needed for cold storage.

Blockchain‐IoT: A revolutionary model for secure data storage and fine‐grained access control in internet of things

AbstractWith the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

Dynamic modeling and analysis of compressed air energy storage for multi-scenario regulation requirements

Compressed air energy storage (CAES) technology has received widespread attention due to its advantages of large scale, low cost and less pollution. However, only mechanical and thermal dynamics are considered in the current dynamic models of the CAES system. The modeling approaches are relatively homogeneous. CAES power stations have gradually increased the demand for auxiliary services such as frequency modulation mode and voltage regulation mode in addition to the peak regulation mode. Therefore, it is urgent to pay attention to the dynamic response characteristics at shorter time scales. Due to the lack of an accurate and complete dynamic model of the CAES system for multi-scenario adaptation requirements, the development of system optimization design and control technology has been severely limited. The paper establishes a dynamic model of advanced adiabatic compressed air energy storage (AA-CAES) considering multi-timescale dynamic characteristics, interaction of variable operating conditions and multivariate coordinated control. The simulation data is compared with the measured data of the peak regulation, frequency regulation and voltage regulation scenarios of the Jintan Salt Cave CAES (JTSC-CAES). The results show that the deviation of the dynamic model is <7 %, which verifies the validity and accuracy of the proposed AA-CAES dynamic model. The established model and research results provide the theoretical model foundation for dynamic characteristic research, system design and optimization control of the JTSC-CAES.

Optimal scheduling strategies for electrochemical energy storage power stations in the electricity spot market

IntroductionThis paper constructs a revenue model for an independent electrochemical energy storage (EES) power station with the aim of analyzing its full life-cycle economic benefits under the electricity spot market.MethodsThe model integrates the marginal degradation cost (MDC), energy arbitrage, ancillary services, and annual operation and maintenance (O&amp;amp;M) costs to calculate the net profits of the EES power station. Using an iterative optimization approach, we determine the optimal MDC and analyze the economic end of life (EOL) for different types of EES power stations.ResultsBy examining real-world examples from the California energy market, we find that the full life-cycle benefits of an EES power station peak when its MDC is optimal, at $45/MWh-throughput. Under these conditions, the economic and physical EOL of commercial/industrial EES power station is 9 years, while the economic EOL of residential-grade EES power station is 8 years, which is shorter than their physical EOL of 9 years.DiscussionThe study further indicates that the economic life of an EES power station is influenced by multiple factors, and operators need to determine the optimal economic EOL to maximize revenue based on battery degradation characteristics, market conditions and operational strategy.

Quantifying the Role of Calibration Strategies on Surface‐Subsurface Hydrologic Model Performance

ABSTRACTDistributed, coupled surface‐groundwater hydrologic models are high‐dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high‐dimensional models are naturally ill‐posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA‐UA‐PE) framework on model testing for a surface‐subsurface hydrologic model. In doing so, we address the concepts of ‘over‐parameterisation’ and ‘under‐parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater‐channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA‐UA‐PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface‐subsurface hydrologic models.

A parallel compositional reservoir simulator for large-scale CO2 geological storage modeling and assessment

Storing CO2 in deep aquifers and depleted gas reservoirs is an effective way to achieve carbon neutrality. However, the numerical simulation of CO2 storage in these formations is challenging due to the complexity of gases-brine systems. The number of gas species included in the gases-brine fluid models of existing simulators cannot meet the rapidly evolving CO2 sequestration scenarios. To address this intricate issue, we developed a three-dimensional fully implicit parallel CO2 geological storage simulator (PRSI-CGCS) on distributed-memory computers based on our in-house parallel platform. This simulator uses a compositional fluid model with a diverse range of gas species, including CO2, C1 ~ C3, N2, H2S, as well as newly added gases H2 and O2, which may be encountered in geological CO2 storages. Besides, we provide more suitable scaling factors for different gases in the stability analysis bypassing (SAB) method to accelerate the gases-brine phase equilibrium calculations. PRSI-CGCS does not incorporate energy conservation equations, and salt precipitation or dissolution is also not considered. Numerical experiments show that our simulator is scalable, robust and validated to simulate large-scale CO2 storage problems with hundreds of millions of grid blocks on a parallel supercomputer cluster. Besides, after our modification on scaling factors, the SAB method can reduce the number of stability analyses by 61.39 % to 88.71 %, thereby reducing simulation time. Furthermore, case studies indicate that injecting O2 and H2 along with CO2 reduces the stability or capacity of CO2 storage and increases the pressure required for injection. However, this impact is not significant when the impurity content is less than 10 %.

Bas KiTE PHEB Analysis Performance using MATLAB/SIMULINK

Abstract The performance analysis of Bas KiTE Plug-in Hybrid Electric Bus (PHEB) is crucial for assessing its effectiveness in power system applications to reduce fuel consumption and emission. In this study, MATLAB/SIMULINK is employed as a tool of modelling and simulation to evaluate the performance of Bas KiTE PHEB model. The research focuses on sizing of Bas KiTE PHEB and performance of Bas KiTE PHEB electric machine model, internal combustion engine model, and energy system storage model using MATLAB/SIMULINK in term power, fuel consumption and emission. Kuala Terengganu driving cycle were use as input to see the performance. Through this study, the preliminary of Bas KiTE PHEB model use MATLAB/SIMULINK can be used as reference to build the prototype of PHEB in Kuala Terengganu.

Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load

A two-layer scheduling method of energy storage that considers the uncertainty of both source and load is proposed to coordinate thermal power with composite energy storage to participate in the peak regulation of power systems. Firstly, considering the characteristics of thermal power deep peak regulation, a cost model of thermal power deep peak regulation is constructed and fuzzy parameters are used to manage the uncertainty of wind, photovoltaics, and load. Secondly, based on the peaking characteristics and operating costs of composite energy storage, a two-layer optimal scheduling model of energy storage is constructed. The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. Finally, we verify the effectiveness of the proposed strategy based on an IEEE 39-node system.

Optimization and performance improvement of distributed data storage in hybrid storage systems

With the rapid development of information technology, the storage and processing of massive data has become one of the important challenges facing the current computing field. Traditional centralized storage systems have been unable to meet the needs of big data applications, while distributed storage systems have become the infrastructure of modern data centers because of their high scalability and high availability. However, in practical applications, a single distributed storage model is often difficult to balance cost-effectiveness and performance requirements. Therefore, this paper proposes a hybrid storage system model combining local storage and cloud storage resources and discusses a series of optimization strategies for this model. The focus of the research is to improve the overall performance of the system through the design of intelligent data sharding, redundancy and fault tolerance mechanisms, and the application of effective load balancing technology. The effectiveness and superiority of the proposed method are verified by testing and analyzing several typical application scenarios on the experimental platform. The experimental results show that the optimized hybrid storage system not only significantly improves the speed of data access, but also effectively reduces the storage cost, demonstrating its potential in future large-scale data management.

Flexibility Assessment of Ternary Pumped Storage in Day-Ahead Optimization Scheduling of Power Systems

To explore the advantages in the flexibility of ternary pumped storage units (T-PSUs) compared with fixed-speed pumped storage units (FS-PSUs) and variable-speed pumped storage units (VS-PSUs) in the day-ahead optimal scheduling of power systems, this paper firstly establishes the mathematical model of T-PSUs which is suitable for the target application, and establishes a new hybrid pumped storage model combining both FS-PSUs and T-PSUs for the purpose of improving existing FS-PSUs. Secondly, a day-ahead optimal scheduling model used for a wind-thermal-pumped storage bundled transmission system with the goal of obtaining the lowest operating cost is established. Additionally, various indicators are proposed to comprehensively evaluate the flexibility of different pumped storage unit types on day-ahead optimal scheduling. Finally, a sensitivity analysis is conducted from three aspects: covering the wind power capacity, load rate, and pumped storage capacity. The results indicate that T-PSUs are superior to FS-PSUs and VS-PSUs in maintaining the smooth, economic, and robust operation of thermal power units, as well as in promoting wind power consumption. This provides a significant reference for evaluating the technical and economic benefits of the different types of pumped storage units in applications of future power grids.

Optimization of Parallel Data Transfers in Multi-Tiered Persistent Storage

A logical model of multi-tiered persistent storage provides a view of data where all available storage resources are distributed over a number of levels depending on the data transfer parameters and capacities. The efficient parallelization of data transfers in multi-tiered persistent storage is a significant challenge for a pipelined data processing model. This work examines a category of database applications implemented as sequences of operations that transfer data between the levels of multi-tiered persistent storage. The concept of EPN: Extended Petri Nets represents how database applications can be processed in parallel. A proposed transformation involves converting EPN into sequences of parallel data transfers. Additionally, a method is demonstrated for partitioning these sequences of data transfers, with the goal of reducing the total number of conflicts when data transfers occur between the levels of multi-tiered persistent storage. The paper proposes new rule-based algorithms for scheduling parallel data transfers that minimize total data transfer time. The objectives of the new algorithms are to evenly distribute the workload among the data transfer processes and reduce their idle time. Several experiments have confirmed the effectiveness of the new algorithms in generating parallel data transfer plans.

Using the Ethereum Blockchain in the Internet of Things Network for IT Diagnostics

The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.

Research on the decentralized application of blockchain technology in campus information security

In the digital information age, the traditional centralized storage model is vulnerable to security attacks, which leads to the spread of false information and difficulty in tracing. This study proposes a decentralized campus information security system using blockchain technology and builds a tamper-proof, traceable, and privacy-protected architecture through Ethereum and Inter Planetary File System (IPFS). The system uses zero-knowledge proof and homomorphic encryption technology to ensure privacy and uses IPFS as an off-chain storage mechanism to improve the scalability of the system and data access speed. Experimental results show that compared with traditional digital applications, the system performs well in ensuring the authenticity and security of information and effectively protects user privacy.