Parameters Tailoring on the Deposition of Hydroxyapatite by Pulsed Electrical Discharge.

The creation of strong adhesive layers of hydroxyapatite-based bioceramics (with or without bioinert metals, such as Ta, Ag, and Ti) on biocompatible metallic supports enhances the local biofunctionalization of surfaces. The processing of electroconductive materials using electrical impulse discharges is versatile, enabling precise coating of selected areas with perfectly adherent layers of varying thicknesses. This study aims to quantify the effects of varying the electrical power from the source generating the impulse discharge and the specific processing time per unit area of the cathode (made of titanium alloy) on the relative mass increase of the cathode. The anode comprised a mixture of hydroxyapatite powder and a self-polymerizing electroconductive acrylic resin in a tantalum sheath. The effects of the parameter adjustments on single-layer deposition adherence were quantified using a central composite design to build a second-order orthogonal model. The most significant difference in relative mass was observed with a low-power source (5 W) ensuring the electrical discharge impulse, combined with the longest specified surface treatment time (17.5 s/cm2 on a 4 cm2 surface) for a single layer presenting the largest mass increase of 0.153% of the original mass. This study aimed to enhance the performance of medical implants by optimizing surface biofunctionalization through robust hydroxyapatite-based bioceramic adhesive layers on metallic supports, determining the optimal electrical power and processing time for cathode mass increase during deposition processes, and analyzing parameter adjustments using second-order statistical orthogonal central composite programming, with a focus on single-layer deposition to identify significant differences in relative mass under specific conditions.

Anodic oxidation by electrical power pulses for alachlor degradation.

This article explores the benefits of electrochemical oxidation in pulsed mode, using potential, current, and power pulses. While potential and current pulse electrochemical technology has been previously studied for wastewater treatment, no study has included power pulses until now. The objective of this work is to highlight the advantages of power pulses by applying this pulse type to the electrochemical oxidation of a probe molecule, alachlor. For this aim, the influence of operating parameters and the comparison of the different pulse modes were investigated and compared to the results obtained with the electrochemical oxidation of alachlor in continuous mode. The study shows that the best results were obtained with the power pulse electrochemical oxidation with 100% alachlor degradation after 180 min and a mineralisation yield of 38.3% after 240 min. These results were better than those reported in the literature for treatments with continuous current input using platinum electrodes. This new technique could be an effective and efficient way to treat contaminated water and reduce the pressure on freshwater reserves.

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Group Method of Data Handling Using Christiano-Fitzgerald Random Walk Filter for Insulator Fault Prediction.

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano-Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10-12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.

Innovative study in renewable energy source through mixed surfactant system for eco-friendly environment.

Actual plan of research work was proposed for systematic investigating in the field of photogalvanic (PG) cells for solar energy transformation. It was necessary and proposed to carry out experimental work under the solar parameters for PG cells. The object of the research work is to enhance the solar energy conversion into electricity and store it through PG cells. Various parameters were studied in a PG cell having D-Xylose + MB + Brij-35 + NaLS system (mixed surfactants). In this study, the observed optimum results in terms of the open circuit voltage, photopotential, maximum photocurrent, and short circuit current are 921.00 mV, 698.00 mV, 311 uA, and 245.0 uA, respectively. The observed equilibrium photocurrent, current at power point, fill factor, and conversion efficiency were 243.0 uA and 142.0 uA, 0.4521, and 0.6769%, respectively. For individual surfactants, the observed results in terms of the open circuit voltage, photopotential, maximum photocurrent, and short circuit current are 870.00 mV, 635.00 mV, 175 uA, and 90.0 uA, respectively. For individual surfactant system, the observed equilibrium photocurrent, current at power point, fill factor, and conversion efficiency were 84.0 uA and 55.0 uA, 0.3630, and 0.3100%, respectively. The impact of solar energy was studied by varying the various parameters in PG cells. On the basis of above obtained values, the mixed surfactants (NaLS + Brij-35) have experimentally proved the efficient system as the desired object of research with special reference to enhance electrical out and storage of solar energy.

An Efficient Ambient-Moisture-Driven Wearable Electrical Power Generator.

Existing devices for generating electrical power from water vapor in ambient air require high levels of relative humidity (RH), cannot operate for prolonged periods, and provide insufficient output for most practical applications. Here a heterogeneous moisture-driven electrical power generator (MODEG) is developed in the form of a free-standing bilayer of polyelectrolyte films, one consisting of a hygroscopic matrix of graphene oxide(GO)/polyaniline(PANI) [(GO)PANI] and the other consisting of poly(diallyldimethylammonium chloride)(PDDA)-modified fluorinated Nafion (F-Nafion (PDDA)). One MODEG unit (1 cm2 ) can deliver a stable open-circuit output of 0.9 V at 8 µA for more than 10 h with a matching external load. The device works over a wide range of temperature (-20 to +50 °C) and relative humidity (30% to 95% RH). It is shown that series and parallel combinations of MODEG units can directly supply sufficient power to drive commercial electronic devices such as light bulbs, supercapacitors, circuit boards, and screen displays. The (GO)PANI:F-Nafion (PDDA) hybrid film is embedded in a mask to harvest the energy from exhaled water vapor in human breath under real-life conditions. The device could consistently generate 450-600 mV during usual breathing, and provides sufficient power to drive medical devices, wearables, and emergency communication.

Wavelet LSTM for Fault Forecasting in Electrical Power Grids.

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.

On-orbit electrical power system dataset of 1U CubeSat constellation.

This article presents a database containing on-orbit data samples of the Electrical Power System (EPS) from 4 different 1U CubeSats belonging to the BIRDS constellation. The EPS is responsible for providing uninterrupted power to overall satellite both during sunlight and eclipse. The satellites are based on the BIRDS open-source standardized bus designed by Kyutech for research and education. BIRDS bus was used for six satellites that were delivered to ISS on board the Cygnus re-supply spacecraft launched by Antares rocket and released from International Space Station (ISS) into ISS orbit (altitude 400 km, inclination: 51.6°, duration: 92.6 min). The dataset contains the data of voltage (mV), current (mA) and temperature (Celsius) of the battery and solar panels attached to 5 sides of the satellite. This data is collected by the on-board computer every 90 seconds in nominal operation or every 10 seconds in fast sampling mode. The data is downloaded from the satellite memory by the ground station operators. Next, space engineering experts from Kyushu Institute of Technology have analysed the dataset to classify each data sample into normal or anomaly classes. This paper provides one datafile per satellite, that includes data from solar panels and battery since their deployment into orbit until the end of its life for the UGUISU, RAAVANA, and NEPALISAT satellites, first two showing a failure in one of their panels during more than two years of operation on-orbit. The TSURU satellite dataset includes data since its deployment into orbit and will continue to be collected until the end of its life. The dataset generated will be useful for 1U CubeSat, such as BIRDS platform, users, and satellite developers by using it as a reference to compare the behaviour of their Electric Power System under different operating scenarios and align their missions according to the available power on-orbit. At the same time, the dataset can help computer science researchers to build and validate new models for fault diagnosis and outlier detection.

A novel methodology for optimal location of reactive compensation through deep neural networks.

The present investigation proposes a methodology for the optimal location of reactive compensation in an electrical power system (EPS) through deep neural networks for voltage profile improvement. One of the main parameters to consider regarding EPS reliability is the voltage profile, a parameter that can be affected due to unexpected increases in impedance and loads in the system that translate as overloads in the system and an increase in the number of users. A voltage profile below the minimum or above the maximum accepted in the regulations of each country puts at risk the correct operation of equipment connected to the electrical network and, in turn, can cause economic losses and human lives (e.g by not guaranteeing reliability for hospitals and similar institutions). Economically, one of the most viable alternatives for improving voltage profiles is reactive compensation which in itself is carried out through capacitor banks. Therefore, this work proposes to find the correct location of capacitor banks in an electrical power system (using IEEE 14, 30 and 118 bus-bars systems as cases of study). In each system, the highest reactive load is identified, thus three values for reactive compensation are established as 80%, 50% and 25% of this maximum. Then, with these values, power flows are generated by locating each one of the reactive compensators' possible values in each one of the bars of the system, hence generating a large number of training data so that finally the neural network is capable of providing a quantitative classification highlighting which compensation and in which bus-bar produces the best result. The result is assessed by applying a modified standard deviation which evaluates the separation of the voltage profiles from the ideal desired value of 1pu.

Study on Electrical Pitting Prevention Device of a Rotating Shaft Using Automatic Control Potential Balancing.

A rotating body consisting of a rotating shaft and bearings inevitably generates voltage and current. The potential difference between the bearing and the shaft is the main cause of electrical corrosion, which causes motor failure, shortened bearing life, and many safety issues. To prevent corrosion, passive shaft-grounding devices use conductive materials and brushes; however, these devices cannot be completely grounded, so there is a difference in local potential, and brush friction generates a shaft current. The cumulative effect causes electrical corrosion; therefore, in this study, an electrical corrosion protection device for the rotating power supply shaft was developed. It detected current and potential difference and established a feedback system on the rotating shaft. It also energized the rotating shaft using an external power supply to eliminate the potential difference on the shaft and reduce electrical corrosion. The result was prolonged motor life and improved stability, operating efficiency, and operability of related equipment. In this study, a rotating-shaft test rig was set up, and a constant current was applied to simulate the potential difference and verify the performance of the anti-corrosion device. Gradually, the design scheme was optimized; the potential difference on the rotating shaft was accurately quantified; and the goal of controlling the potential difference within 2 mV was achieved. Finally, the electrical corrosion protection device was applied to the rotating shaft of a merchant ship, and the current and potential difference on the rotating shaft were monitored for 30 days. The results showed that the device had excellent performance in reducing the potential difference on the rotating shaft and preventing electrical corrosion.

Design of a Smart IoT-Based Control System for Remotely Managing Cold Storage Facilities.

Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.

Exploration and prediction of wet season municipal solid waste for power generation in Ilorin metropolis, Nigeria.

The wet season municipal solid waste (MSW) was characterized on Lasoju/Eyenkorin dumpsite for May to August 2020. The aggregate of waste generated was estimated to be 135,882 tons, while the aggregate characterized was estimated to be 80,700 tons. There are thirty-two samples of 240 L (bin of MSW) per sample considered in this investigation. There are twenty-one waste components categorized altogether, with packaging box having the highest proportion of 10.04%, followed by food residue of 9.64%, nylon 9.51%, and leather with the least fraction (0.75%) of the weight basis. Experimental investigations were performed on fourteen combustible fractions of the waste to determine the moisture content, elemental contents, and high heating value. The laboratory analysis reveals that the average carbon content available is 55%, 7% hydrogen, 1.35% nitrogen, 0.44% sulphur, and 30% oxygen; the low heating value of the waste was determined to be 23 MJ/kg. About 672 tons of MSW were investigated for energy production to give an energy and power potentials of 4.2 GWh and 53 MW discretely. The estimated electrical power potential for the wet season MSW is capable of meeting about 59% of the power demand for the Ilorin metropolis.

Microbial fuel cell compared to a chemostat.

Microbial Fuel Cells (MFCs) represent a green and sustainable energy conversion system that integrate bacterial biofilms within an electrochemical two-electrode set-up to produce electricity from organic waste. In this review, we focus on a novel exploratory model, regarding "thin" biofilms forming on highly perfusable (non-diffusible) anodes in small-scale, continuous flow MFCs due to the unique properties of the electroactive biofilm. We discuss how this type of MFC can behave as a chemostat in fulfilling common properties including steady state growth and multiple steady states within the limit of biological physicochemical conditions imposed by the external environment. With continuous steady state growth, there is also continuous metabolic rate and continuous electrical power production, which like the chemostat can be controlled. The model suggests that in addition to controlling growth rate and power output by changing the external resistive load, it will be possible instead to change the flow rate/dilution rate.

A data set of a Norwegian energy community.

This paper presents a data set designed to represent Norwegian energy communities. As such it includes household consumption data collected from smart meter measurements and divided into consumer groups, appliance consumption data collected from Norwegian households, electric vehicle data regarding charging patterns, simulated photovoltaic power generation data based on temperature and irradiance data sets and wholesale electricity prices. All data sets are further filtered by season, weekday/weekend and time segment, and then fitted to either a normal, exponential or log-normal distribution. The reason for this specific segmentation is the intention to provide a suitable data set for case studies and experiments on energy communities that consider uncertainty, a main challenge to be overcome in the practical implementation of energy community projects. In addition to this filtered version, the previously unpublished raw data sets on household consumption and photovoltaic power generation are also provided.

Data on the life cycle carbon emission in Kenyan, Rwandan, and Tanzanian grid electricity generation and transmission systems.

This paper presents data for the estimation of the life cycle carbon emission in Kenyan, Rwandan, and Tanzanian grid electricity generation and transmission systems. Data was collected and estimated using the developed life-cycle carbon emission inventory (LCCEI) algorithm implemented through Excel tabs (LCCEI Excel worksheets). The data acquired through the LCCEI modelled parameters (Chambile et al., 2021). The presented dataset shows the results of the developed data collection model. The activity data were obtained from specialized data sources. Some information was obtained through meetings with relevant institutional actors and experts of national and regional power institutions as well as expert judgement. However, most of the data were also obtained from the reviewed published reputable sources, such as the scientifically indexed conference proceedings and journals. The obtained data are presented in this article and in a Mendeley data repository. The compiled data can also be customised and coded to commonly used evaluation software to enhance its open use by scientists, practitioners, and policymakers at national, regional and global levels.

Characterization and projection of dry season municipal solid waste for energy production in Ilorin metropolis, Nigeria.

This research investigates the quantity of municipal solid waste produced during the dry season, and its characterization at Eyenkorin dumpsite of Ilorin metropolis, along the Lagos-Ilorin express way. The physicochemical and thermal compositions of the combustible fractions of municipal solid waste were analysed, to ascertain the available calorific value. In this research, the quantity (tonnes) of waste generated, the rate of generation (kg per capita per day), its sustainability and the likely energy and power potentials in the dry season, were essentially predicted. The population responsible for municipal solid waste generation during this study was 1,120,834 people. During the characterization study from November 2018 to February 2019, it was established that 203,831 tonnes of municipal solid waste was produced during the four months of the dry season, at the rate of 1.12 kg per capita per day. It was found that 280 tonnes/day of municipal solid waste with low heating value of 19 MJ kg-1, would generate 1478 MWh of heat energy and 18 MW of electrical energy potentials discretely, and grid of 13 kW.

Thermal stability of cellulose insulation in electrical power transformers - A review.

Cellulosic pulp has been processed into insulation paper since the earliest days of electrical engineering. This polymer synthetized by nature has proved to be competitive to man-made plastics throughout the last century and is still widely used in electrical power transformers. The high working temperatures prevailing in such apparatuses and the desired lifespans of up to 40 years shifted the thermal stability of cellulose to the center of attention of many researchers. In this literature review, a summary of theories and recent insights regarding the processes upon thermal degradation of cellulose in the temperature range relevant for electrical power transformers is given, followed by an overview of strategies to improve the thermal stability of cellulosic insulators. Special emphasis is placed on the discussion of additives and modification agents and their action modes, and on the understanding how successful upgrading of cellulose towards high thermal stability is achieved.

Sustainability of agricultural and wild cereals to aerotechnogenic exposure.

In recent decades, the problem of the constantly increasin level of anthropogenic load on the environment is becoming more and more acute. Some of the most dangerous pollutants entering the environment from industrial emissions are heavy metals. These pollutants are not susceptible to biodegradation over time, which leads to their accumulation in the environment in dangerous concentrations. The purpose of this work is to study the sustainability of cultivated and wild plants of the Poaceae family to aerotechnogenic pollution in the soil. The content of heavy metals in couch grass (Elytrigia repens (L.) Nevski), meadow bluegrass (Poa pratensis L.) and soft wheat (Triticum aestivum) plants grown in the impact zone of Novocherkassk Power Station has been analyzed. Contamination of cultivated and wild cereals with Pb, Zn, Ni and Cd has been established. It has been shown that the accumulation of heavy metals is individual for each plant species. An average and close correlation have been established between the total HM content and the content of their mobile forms in the soil and their content in plants. For the plants studied, the translocation factor (TF) and the distribution coefficient (DC) of HM have been calculated. The TF is formed by the ratio of the concentration of an element in the root plant dry weight to the content of its mobile compounds in the soil. The DC value makes it possible to estimate the capacity of the aboveground parts of plants to absorb and accumulate elements under soil pollution conditions and is determined as the ratio of the metal content in the aboveground biomass to its concentration in the roots. TF and DC values have shown a significant accumulation of elements by plants from the soil, as well as their translocation from the root system to the aboveground part. It has been revealed that even within the same Poaceae family, cultural species are more sensitive to man-made pollution than wild-growing ones.

Electrical Power Generation Using Dynamic Piezoelectric Shear Deformation Under Friction.

A new electrical power generation device based on high-frequency dynamic piezoelectric shear deformation under friction is developed. During the operation of a moving plate compressed and sliding on the top of a piezoelectric patch with constant velocity, dynamic shear deformation of the elastic piezoelectric patch is excited by periodic friction force and status (sliding and stick) variation. The dynamic piezoelectric shear strain can then generate continuous electrical power for energy absorbing and harvesting applications. The design of the piezoelectric couple device is first provided, and its mechanism, dynamic response and electric power generation under friction are described by a detailed iteration model. By comparing with previous experimental results, the accuracy of the proposed model is proven. Through numerical studies, the influences of the equivalent mass of the system, the velocity of the sliding object, the static friction coefficient and its lower limit, as well as the friction force delay rate on the power generation are obtained and discussed. The numerical results show that with the proposed design, up to 50-Watt maximum electrical power could be generated by a piezoelectric patch with a dimension of 20 × 2 × 6 cm under continuous friction with the moving plate at the velocity of 15 m/s. The possible bi-linear elastic stiffness variation of the system is also introduced, and the threshold of bi-linear elastic deformation, where the system stiffness changes, can be optimized for obtaining the highest power generation.

Geometry Optimization of Thermoelectric Modules: Deviation of Optimum Power Output and Conversion Efficiency.

Besides the material research in the field of thermoelectrics, the way from a material to a functional thermoelectric (TE) module comes alongside additional challenges. Thus, comprehension and optimization of the properties and the design of a TE module are important tasks. In this work, different geometry optimization strategies to reach maximum power output or maximum conversion efficiency are applied and the resulting performances of various modules and respective materials are analyzed. A Bi2Te3-based module, a half-Heusler-based module, and an oxide-based module are characterized via FEM simulations. By this, a deviation of optimum power output and optimum conversion efficiency in dependence of the diversity of thermoelectric materials is found. Additionally, for all modules, the respective fluxes of entropy and charge as well as the corresponding fluxes of thermal and electrical energy within the thermolegs are shown. The full understanding and enhancement of the performance of a TE module may be further improved.