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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.

8 p.

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.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

Abstract The combination of biomass gasification with fuel cells, especially high temperature Solid Oxide Fuel Cells (SOFCs) promises sustainable and highly efficient (decentralized and modular) energy conversion systems. This review encompasses the components of biomass integrated gasification–SOFC technology including biomass characteristics, the thermochemical conversion in gasifiers and the factors affecting the gasification process, the cleaning technologies for raw producer gas and its conditioning and finally the integration of gasifier with SOFCs. The influence of impurities present in biomass producer gas such as particulates, tar, H 2 S, HCl and alkali compounds based on recent experimental studies and their tolerance limits towards SOFCs are presented. Even though analysis based on the probable tolerance limits of impurities towards SOFCs and a comprehensive overview of the cleaning technologies for producer gas impurities indicate that producer gas cleaning at various temperatures using current technologies to meet SOFC requirements is possible, more experimental studies are still needed to acquire the detailed information on the tolerance limits of impurities for SOFCs. The recent theoretical modeling and experimental studies of biomass integrated gasification–SOFC systems are also presented.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

The effect of low concentrations of ethanol on Na+,K(+)-ATPase activity, defined as ouabain-inhibitable 86Rb+ (K+) uptake, was investigated in a crude synaptosome preparation which was subject to minimal subcellular fractionation procedures. Moderate (20-30%) but potent (EC50 = 3.8 mM) stimulation of total ouabain (1 mM)-inhibitable K+ uptake by ethanol was observed following incubation periods of up to 20 min. The activity of the ethanol-induced component of K+ uptake was antagonized by nanomolar concentrations of ouabain. Thus, the moderate stimulation of total ouabain-inhibitable K+ uptake by ethanol was attributable to the activation of a component of K+ uptake which was very sensitive (VS; IC50 = 2.8 x 10(-10) M) to inhibition by ouabain. Slightly higher concentrations of ouabain (10(-9) - 10(-6.6) M) stimulated K+ uptake above control (no ethanol or ouabain) in both the absence and presence of ethanol. The selectivity of the VS-ethanol interaction was demonstrated by the lack of any ethanol effect on two other components of ouabain-inhibitable K+ uptake which accounted for inhibition of K+ uptake by concentrations of ouabain above 10(-6.6) M and were defined as sensitive (S; IC50 = 10(-6) M) and insensitive (I; IC50 = 10(-4) M) to ouabain. These results define the ethanol-inducible component of ouabain-inhibitable Na+,K(+)-ATPase activity and promote the view that changes in Na+,K(+)-ATPase-dependent ion translocation may contribute to ethanol intoxication in vivo.

64 p.

In the backdrop of COVID19 recovery, Pakistan is still struggling to cope with the economic challenges and disruptions caused in the energy supply chain. On one hand where COVID has brought serious socio-economic costs and prolonged delays, it has also provided opportunity for developing countries such as Pakistan to “build-forward-better” their economies in a more sustainable and climate friendly manner. This study particularly highlights the impact of COVID on energy supply and demand sectors of Pakistan, its near- and long-term impacts, and what policy interventions can be adopted to put Pakistan on-track to achieve its Nationally Determined Contributions (NDCs). The economic focus in on “Green Recovery” and what key interventions will foster a rapid transition towards decarbonization in Pakistan. Low Emission Analysis Platform (LEAP) model is used to provide energy sector outlook (2020-2040) of Pakistan under different scenario i.e., Pre COVID growth, Business-as-Usual, Slow Recovery, and Green Recovery from COVID. The results obtained from the model depicts that following a green recovery scenario, Pakistan can reduce around 10 Mtoe (9%) of its total energy use, 53 TWh of electricity, 19 Mt of emissions from demand sectors, and 11 Mt of emissions from the power sector by 2030. For total levelized cost of the power sector, the green recovery scenario represents a generation cost of $13 billion by 2030 which further highlights that energy efficiency could lead to cost savings of approximately $3 billion each year by 2030. Green recovery is however still a daunting task as it would require economic stimulus of $8 billion only to recover to its pre COVID scenario and total investments of $120 billion by 2030.