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Abstract The reverse flat-plate collector is a non-concentrating collector. It can collect solar heat at high temperatures which cannot be achieved by conventional non-concentrating collectors. In this paper, the authors have proposed a number of modified versions of the originally proposed reverse flat-plate collector. The new designs are of single, as well as double, absorber type. The thermal performance of these modified reverse flat-plate collectors is compared with that of a single absorber reverse flat-plate collector, as well as with the corresponding normal flat-plate collector. It is found that the new design having two absorbers gives the best thermal performance as compared with other configurations. The analytical models presented in this paper very well describe the experimental results.

Drying experiments have been performed with potato cylinders and slices using a laboratory scale designed natural convection mixed-mode solar dryer. The drying data were fitted to eight different mathematical models to predict the drying kinetics, and the validity of these models were evaluated statistically through coefficient of determination (R(2)), root mean square error (RMSE) and reduced chi-square (χ (2)). The present investigation showed that amongst all the mathematical models studied, the Modified Page model was in good agreement with the experimental drying data for both potato cylinders and slices. A mathematical framework has been proposed to estimate the performance of the food dryer in terms of net CO2 emissions mitigation potential along with unit cost of CO2 mitigation arising because of replacement of different fossil fuels by renewable solar energy. For each fossil fuel replaced, the gross annual amount of CO2 as well as net amount of annual CO2 emissions mitigation potential considering CO2 emissions embodied in the manufacture of mixed-mode solar dryer has been estimated. The CO2 mitigation potential and amount of fossil fuels saved while drying potato samples were found to be the maximum for coal followed by light diesel oil and natural gas. It was inferred from the present study that by the year 2020, 23 % of CO2 emissions can be mitigated by the use of mixed-mode solar dryer for drying of agricultural products.

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons with an optimized virtual inertia PI controller which is tuned utilizing MATLAB internal model control methodology and with H∞-based virtual inertia control. The results show the effectiveness of the proposed controller against different operating conditions and system disturbances and uncertainties.

Abstract In this work addition of ethanol to high viscosity jatropha methyl ester (JME) through port injection is investigated in order to determine its effect fuel viscosity reduction on diesel engine performance. In addition to viscosity alteration, the impact of ethanol addition on combustion characteristics such as combustion duration, ignition delay and emissions levels from diesel engines fuelled with blends of ethanol, diesel and JME is studied in particular. It is found that blending of oxygenated fuels with diesel modifies the chemical structure and physical properties which again alter the engine operating conditions, combustion parameters and emissions levels. However, the injection of only 5% ethanol through port injection allows for a total of 25% blending of biofuels into diesel yet keeping the fuel characteristics close to that of conventional diesel. However, both experimental and numerical results show that ethanol addition in JME blended diesel results in a slight increase in fuel consumption and thermal efficiency for the same power outputs as that of conventional diesel fuel. Also, the combustion characteristics with ethanol addition include improved maximum in-cylinder peak pressure, cumulative heat release (CHR) rate of heat release (ROHR), in-cylinder peak temperature and combustion duration. Regarding emission characteristics the experimental results show significant reduction in smoke, carbon monoxide (CO) and total hydrocarbon (THC) emissions with extended oxygen mass percentage in the fuel at higher engine loads. However, oxides of nitrogen (NOx) emissions are found to increase at high loads although the common tradeoff between smoke and NOx is found to be more prominent for the oxygenated fuels.

In this study, levulinic acid (LA) was produced from rice straw biomass in co-solvent biphasic reactor system consisting of hydrochloric acid and dichloromethane organic solvent. The modified protocol achieved a 15% wt LA yield through the synergistic effect of acid and acidic products (auto-catalysis) and the designed system allowed facile recovery of LA to the organic phase. Further purification of the resulting extractant was achieved through traditional column chromatography, which yielded a high purity LA product while recovering ∼85% wt. Upon charcoal treatment of the resultant fraction generated an industrial grade target molecule of ∼99% purity with ∼95% wt recovery. The system allows the solvent to be easily recovered, in excess of 90%, which was shown to be able to be recycled up to 5 runs without significant loss of final product concentrations. Overall, this system points to a method to significantly reduce manufacturing cost during large-scale LA preparation.

In the present study, we explored the temperature evolution and hydrogen desorption properties of the Mg50Ni50 alloy through both numerical simulation and experimental analyses. Desorption kinetics characterization was carried out using the volumetric method, specifically employing a Sievert's-type apparatus to investigate solid-gas reactions. The experiments covered a temperature range from 313 K to 353 K, with an initial hydrogen pressure of 12 bar. Simultaneously, a mathematical approach was employed to numerically investigate the temperature evolution within the hydride bed. Using COMSOL Multiphysics as a simulator, a numerical simulation was conducted based on experimental data. The study examined the impact of cooling temperature on hydride temperature evolution. Results revealed that hydrogen desorption kinetics of the amorphous Mg50Ni50 alloy are more significant compared to those of Mg2Ni compounds. Moreover, the effect of the warming temperature on the equilibrium pressure can also be observed in the hydrogen desorption isotherm curves. The experimental study of the Mg50Ni50 alloy provided activation energy data, along with determination of hydride formation enthalpy and entropy. On the other hand, we showed that the hydride temperature is maximum at the hydride-hydrogen interface within the hydride center.

Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

In this paper, the mathematical analysis of the robot effective mass is presented. The calculation of this effective mass and its ellipsoid are included. The relationship between the robot effective mass and the external force (collision) affecting the robot end-effector is investigated. The effective mass is analyzed using different robot configurations and different end-effector positions. This analysis is conducted using 2-DOF and 3-DOF planar robots and executed using MATLAB. The results from this analysis prove that the robot effective mass depends on the its configurations and end-effector position. Effective mass can thus be considered as one of the criteria in optimizing robot kinematics and configuration.