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

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning.

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects. The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording

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

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

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