A Theoretical Approach to Optimize the Pipeline Data Communication in Oil and Gas Remote Locations Using Sky X Technology

Oil, gas, and water distribution networks in remote locations require optimized data transmission from their sources to prevent or detect leakage or improve production flow in their manufacturing units. Remote oil and gas installations frequently encounter substantial obstacles in terms of data connectivity and transfer. Slow data transmission rates, data loss, and decision-making delays can all be caused by a lack of dependable network infrastructure, restricted bandwidth, and severe climatic conditions. The purpose of this research work is to identify critical concerns concerning data communication and data transfer in oil and gas distant areas and to investigate feasible approaches to these challenges. The survey was carried out to gather feedback from oil and gas experts on issues concerning data transmission in remote locations. This study provides a theoretical approach to optimizing data transmission and communication in remote areas using Sky X technology. This study presents a new theoretical method that improves the performance of IP over satellite using the critical aspects of data transmission issues from experts. This technology's contribution can improve the reliability of all users on a satellite network by delivering all features with a successful data transfer rate discreetly. This attempt may also aid oil and gas companies in optimizing data transmission/communication in remote regions

Optimal Sizing of Grid-Scaled Battery with Consideration of Battery Installation and System Power-Generation Costs

Variable renewable energy (VRE) generation changes the shape of residual demand curves, contributing to the high operating costs of conventional generators. Moreover, the variable characteristics of VRE cause a mismatch between electricity demand and power generation, resulting in a greater expected energy not supplied (EENS) value. EENS involves an expected outage cost, which is one of the important components of power-generation costs. A utility-scale battery energy storage system (BESS) is popularly used to provide ancillary services to mitigate the VRE impact. The general BESS ancillary-service applications are as a spinning reserve, for regulation, and for ramping. A method to determine optimal sizing and the optimal daily-operation schedule of a grid-scale BESS (to compensate for the negative impacts of VRE in terms of operating costs, power-generation-reliability constraints, avoided expected-outage costs, and the installation cost of the BESS) is proposed in this paper. Moreover, the optimal BESS application at a specific time during the day can be selected. The method is based on a multiple-BESS-applications unit-commitment problem (MB-UC), which is solved by mixed-integer programming (MIP). The results show a different period for a BESS to operate at its best value in each application, and more benefits are found when operating the BESS in multiple applications

Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand

Government policies are crucial factors for supporting the growth of the electric vehicle (EV) industry—a growth that can be encouraged, for example, by subsidization designed to reduce the considerable anxiety stemming from the inconvenience of refueling at public charging stations. Subsidizing low priority charging for residential enables cost-effective load management for example controlling of EV charging power for grid reliability at the off-peak rate for 24 h. This solution provides the convenient recharging of EVs at home and prevents an expensive grid upgradation. To advance our understanding of the EV situation, this research used a regression model to forecast the growth rate of the EV market alongside the EV expansion policies in Thailand. The agreement between a policy and forecasting urges the government to prepare power system adequacy for EV loading. The analysis showed that power demand and voltage reduction in a typical low-voltage distribution system that assumes maximum EV loading constitute voltage violations. To address this limitation, this study proposed a rule-based strategy wherein low priority smart EV charging is regulated. The numerical validation of the strategy indicated that the strategy reduced power demand by 25% and 39% compared with that achieved under uncontrolled and time of use (TOU) charging, respectively. The strategy also limited voltage reduction and prolonged battery life. The study presents implications for policymakers and electricity companies with respect to possible technical approaches to stimulating EV penetration

A Techno-Economic Assessment of a Second-Life Battery and Photovoltaics Hybrid Power Source for Sustainable Electric Vehicle Home Charging

This study discusses the use of a retired battery from an electric vehicle for stationary energy storage electric vehicle charging in a residential household. This research provides a novel in-depth examination of the processes that may be necessary to investigate the life loss of a battery, whether new or used. The main contribution is to promote the feasibility of the application from both a technical and economic point of view. The semi-empirical models are then utilized to analyze the life fading that is used in economic studies. In terms of lower initial investment costs for the battery and solar photovoltaics, the numerical calculation demonstrates that the used second-life battery with a DOD of 85% has more advantages over a new battery in the same condition. Additionally, compared to a new battery, a second-life battery gradually loses life and benefits from recycling after a projected 10-year lifespan. These results support the feasibility of the project. A discussion of project hurdles is included in which the hybrid converter modification may be achieved. Policymakers are encouraged to keep this valuable scheme in mind for the sake of margin profit and environmental preservation

Energy Production Analysis of Rooftop PV Systems Equipped with Module-Level Power Electronics under Partial Shading Conditions Based on Mixed-Effects Model

The rooftop photovoltaic (PV) system that uses a power optimization device at the module level (MLPE) has been theoretically proven to have an advantage over other types in case of reducing the effect of partial shading. Unfortunately, there is still a lack of studies about the energy production of such a system in real working conditions with the impact of partial shading conditions (PSC). In this study, we evaluated the electrical energy production of the PV systems which use two typical configurations of power optimization at the PV panel level, a DC optimizer and a microinverter, using their real datasets working under PSC. Firstly, we compared the energy utilization ratio of the monthly energy production of these systems to the reference ones generated from PVWatt software to evaluate the effect of PSC on energy production. Secondly, we conducted a linear decline model to estimate the annual degradation rate of PV systems during a 6-year period to evaluate the effect of PSC on the PV’s degradation rate. In order to perform these evaluations, we utilized a mixed-effects model, a practical approach for studying time series data. The findings showed that the energy utilization ratio of PVs with MLPE was reduced by about 14.7% (95% confidence interval: −27.3% to −2.0%) under PSC, compared to that under nonshading conditions (NSC). Another finding was that the PSC did not significantly impact the PV’s annual energy degradation rate, which was about −50 (Wh/kW) per year. Our finding could therefore be used by homeowners to help make their decision, as a recommendation to select the gained energy production under PSC or the cost of a rooftop PV system using MLPE for their investment. Our finding also suggested that in the area where partial shading rarely happened, the rooftop PV system using a string or centralized inverter configuration was a more appropriate option than MLPE. Finally, our study provides an understanding about the ability of MLPE to reduce the effect of PSC in real working conditions

Artificial Neural Network Modeling and Optimiztion of Thermophysical Behavior of 1 MXene Ionanofluids for Hybrid Solar Photovoltaic and Thermal Systems

Newly developed MXene materials are excellent contender for improving thermal systems' high energy and power density. MXene Ionanofluids are novel materials; their optimum thermophysical behavior at various synthesis conditions has not been addressed yet. The aim of this study is to investigate the effect of synthesis conditions (temperature 303–343 K and nanofluids concentration 0.1–0.4 wt%) on the thermophysical properties (thermal conductivity, specific heat capacity, thermal stability, and viscosity) of MXene Ionanofluids. Levenberg Marquardt based Artificial Neural Network (ANN) model and Response Surface Methodology (RSM) based optimization techniques have been adopted for systematic parametric analysis of MXene Ionanofluids thermophysical properties using experimental data. ANN and RSM have predicted the thermophysical behavior of MXene ionanofluids at optimized conditions. The experimental data were used to train, test, and validate the ANN model. The neural network could correctly predict the outcomes for the four properties based on the numerical performance with R2 values close to 1, and a prediction error is 2%. The performance of the proposed LM-based back-propagation algorithm demonstrates that the error involved has been minimal and acceptable. RSM has developed correction among input parameters and thermophysical properties of MXene Ionanofluids. The comparison between experimental results and the proposed correlations revealed excellent practical compatibility. Optimized thermophysical properties of MXene Ionanofluids thermal conductivity of 0.776 W/m.K, specific heat capacity of 2.5 J/g.K, thermal stability of 0.33931 wt loss %, and viscosity of 11.696 mPa.s were obtained at a temperature of 343 K and nanofluids concentration of 0.3 wt%. MXene Ionanofluids with optimal thermophysical properties could be used for the greatest performance of hybrid solar photovoltaic and thermal system applications.Peer reviewe

Deep Learning Approach for Age-related Macular Degeneration Detection Using Retinal Images: Efficacy Evaluation of Different Deep Learning Models

Age-related macular degeneration (AMD) is a typical fundus disease that affects the central vision of elderly people. It causes difficulties in everyday activities such as reading and recognizing faces. AMD can progress slowly or rapidly, and it leads to severe vision loss if left untreated. Therefore, early detection and diagnosis of AMD are crucial to prevent or delay vision impairment in the elderly. To handle this requirement, researchers are exploring deep learning-based models as an AI tool to assist ophthalmologist in AMD diagnosis. However, conducting an appropriate deep learning model for the AMD classification is challenging and cost-intensive. This research aims to evaluate the efficacy of various deep learning models for obtaining the best performance results when identifying AMD disease using retinal images. To meet this objective, the retinal images from the Department of Ophthalmology, the King Chulalongkorn Memorial Hospital, Thailand were collected for transfer learning and other publicly available datasets for testing. Then, seven deep learning models VGG19, Xception, DenseNet201, EfficientNetB7, InceptionV3, NASNetLarge, and ResNet152V2 were chosen to training for the 2-labels (Normal vs. AMD) and the 3-labels (Normal vs. Dry AMD vs. Wet AMD) classifications. From the experimental results, the DenseNet201 model with Dense block in its structure showed the best efficacy in both 2-labels and 3-labels AMD classifications since its performance always include in the Top-3 models accuracy and generalization performance measured by total accuracy and total F1-Score, respectively. Furthermore, the accuracy performance of deep learning models in Top-3 are comparable with the performance of retinal specialist. These results contribute consolidated knowledge to the process of implementation effective deep learning as production that detects AMD automatically in the clinical and enhance the quality of healthcare service

Deployment of Smart Specimen Transport System Using RFID and NB-IoT Technologies for Hospital Laboratory

In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box’s GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital