Proximate composition and mineral profile of eight different unstudied date (Phoenix dactylifera L.) varieties from Pakistan

With the aim of extending the knowledge on chemical composition of dates (Phoenix dactylifera L.), eight different sun dried date varieties; (1) Daki, (2) Aseel, (3) Coconut, (4) Khuzravi, (5) Halavi, (6) Zahidi, (7) Deglet Noor and (8) Barkavi were examined to determine their proximate composition and mineral profile. All the date varieties were found to be rich in proteins, fiber, carbohydrates and net gross energy (352.329 Kcal/100 g in Aseel to 425.147 Kcal/100 g in Khuzravi) having suitable levels of lipids and low values of ash, moisture and oxalates. Na, K and Li were found as macrominerals whereas Cr, Cu, Ca, Mg, Ni, Zn and Mn were found as microminerals. The results suggest that all the studied dates serve as good source of vital nutrients and can be considered as premium quality having significantly higher energy values than the earlier reported values for dates

Comparing the Reaction Rates of Plasmonic (Gold) and Non-Plasmonic (Palladium) Metal Particles in Photocatalytic Hydrogen Production

Both Pd and Au metal particles are used in photocatalytic hydrogen generation. Yet while both act as electron sink only gold is poised to respond to visible light due to its plasmonic response. In order to quantitatively gauge their relative contribution into the reaction, the photocatalytic H2 production, from Au/TiO2 and Pd/TiO2 catalysts was studied under UV and UV–Vis light. While under UV light excitation, a weak dependence on the work function of the metal is observed, under UV–Vis light, Au is found to be twice more active than Pd. Under identical UV–Vis light irradiation, the turn over frequency calculated from XPS at.% is found to be 2.8 and 1.8 s−1 for Au and Pd, respectively. The effect is far more pronounced when the rates are normalized to the number of particles of each metal. Both the semiconductor TiO2 (UV light) and the plasmonic metal (visible light) need to be excited for the enhancement to occur; visible light alone causes a negligible reaction rate. Photocurrent measurements further confirmed the difference in the photocatalytic activity under UV and UV–Vis light excitation. Moreover, because of the presence of Au particles responding to visible light the reaction rate is enhanced due to “light penetration depth” effect

Polypogon monspeliensis waste biomass: A potential biosorbent for Cd (II)

Polypogon monspeliensis a globally available natural waste material was used for uptake of Cd (II) from aqueous solutions in this study. The results clearly demonstrate the effect of important experimental parameters on the biosorption process in batch experiments. The evaluated pH, biosorbent dose, size and initial metal concentration for Cd (II) uptake by P. monspeliensis waste biomass were 6, 0.05 g, 0.10 mm and 100 mg/L respectively. The Cd (II) sorption process by P. monspeliensis waste biomass was described well by pseudo second order kinetic model and Langumir sorption isotherm model. Metal equilibrium was reached in 120 min. A further increase in incubation time had no significant effect on the biosorption of the metal. FTIR spectroscopic results pointed out the involvement of hydroxyl and amine groups in the Cd (II) sorption by P. monspeliensis waste biomass

Kinetic and equilibrium modeling of Cu(II) and Ni(II) sorption onto physically pretreated Rosa centifolia distillation waste biomass

The removal of Cu(II) and Ni(II) from aqueous solution by physically pretreated (boiled, heated and autoclaved) Rosa centifolia distillation waste biomass was conducted in batch conditions. The obtained results revealed that initial metal ion concentration, kinetics, and temperature affected the adsorption capacity of the physically pretreated R. centifolia distillation waste biomass. The Cu(II) and Ni(II) equilibrium sorption data agreed well to Langmuir isotherm model and the sorption kinetics were accurately described by pseudo second order kinetic model. The Cu(II) and Ni(II) uptake capacities (mg g-1) of physical pretreated R. centifolia distillation waste biomass were in following order: boiled (66.91) > heated (52.51) > autoclaved (49.82) > native (42.68) and boiled (67.55) > heated (65.19) > autoclaved (58.09) > native (45.19), respectively. The nature of R. centifolia distillation waste biomass surface functionalities was analyzed by FTIR spectroscopy.Keywords: Cu(II), Ni(II), isotherms, kinetics, pretreatment, Rosa centifolia

Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing ETD methods cannot efficiently handle the sheer volume of data now available, being limited by issues such as missing values, high variance and non-linearity. An integrated infrastructure is also required for synchronizing diverse procedures in electricity theft classification. To help address such problems, a novel ETD framework is proposed that combines three distinct modules. The first module handles missing values, outliers, and unstandardized electricity consumption data. The second module employs a newly proposed hybrid class balancing approach to deal with highly imbalanced datasets. The third module utilises an improved artificial neural network (iANN) based classification engine, to predict electricity theft cases accurately and efficiently. We propose three distinctive mechanisms, including hyper-parameters tuning, regularization and skip connections, to improve the performance of standard ANN to handle more complex classification tasks using smart meter (SM) data. Furthermore, various structures of iANN are investigated to improve the generalization and function fitting capabilities of the final classification. Numerical results from real-world energy usage datasets confirm that the proposed ETD model has superior performance compared to existing machine learning and deep learning methods, and can effectively be applied to industrial applications

2-(Methoxy­carbon­yl)anilinium dihydrogen phosphate

The title compound, C8H10NO2 +·H2PO4 −, is a derivative of the naturally occurring compound methyl­anthranilate. The asymmetric unit comprises the 2-(methoxy­carbon­yl)anilinium cation and the dihydrogen phosphate anion. In the cation, the dihedral angle between the benzene ring plane and that through the methyl ester substituent is 22.94 (9)°. In the crystal, adjacent cations and anions form dimers through N—H⋯O and O—H⋯O hydrogen bonds, respectively. Additional N—H⋯O and C—H⋯O contacts result in a network of cation and anion dimers stacked down the b axis

Effects of the electrodeposition time in the synthesis of carbon-supported Pt(Cu) and Pt-Ru(Cu) core-shell electrocatalysts for polymer electrolyte fuel cells

Pt(Cu)/C and Pt-Ru(Cu)/C electrocatalysts with core-shell structure supported on Vulcan Carbon XC72R have been synthesized by potentiostatic deposition of Cu nanoparticles on the support, galvanic exchange with Pt and spontaneous deposition of Ru species. The duration of the electrodeposition time of the different species has been modified and the obtained electrocatalysts have been characterized using electrochemical and structural techniques. The High Resolution Transmission Electron Microscopy (HRTEM), Fast Fourier Transform (FFT) and Energy Dispersive X-ray (EDX) microanalyses allowed the determining of the effects of the electrodeposition time on the nanoparticle size and composition. The best conditions identified from Cyclic Voltammetry (CV) corresponded to onset potentials for CO and methanol oxidation on Pt-Ru(Cu)/C of 0.41 and 0.32 V vs. the Reversible Hydrogen Electrode (RHE), respectively, which were smaller by about 0.05 V than those determined for Ru-decorated commercial Pt/C. The CO oxidation peak potentials were about 0.1 V smaller when compared to commercial Pt/C and Pt-Ru/C. The positive effect of Cu was related to its electronic effect on the Pt shells and also to the generation of new active sites for CO oxidation. The synthesis conditions to obtain the best performance for CO and methanol oxidation on the core-shell Pt-Ru(Cu)/C electrocatalysts were identified. When compared to previous results in literature for methanol, ethanol and formic acid oxidation on Pt(Cu)/C catalysts, the present results suggest an additional positive effect of the deposited Ru species due to the introduction of the bifunctional mechanism for CO oxidatio

A Stacked Machine and Deep Learning-based Approach for Analysing Electricity Theft in Smart Grids

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for theft detection can struggle to handle large electricity consumption datasets because of missing values, data variance and nonlinear data relationship problems, and there is a lack of integrated infrastructure for coordinating electricity load data analysis procedures. To help address these problems, a simple yet effective ETD model is developed. Three modules are combined into the proposed model. The first module deploys a combination of data imputation, outlier handling, normalization and class balancing algorithms, to enhance the time series characteristics and generate better quality data for improved training and learning by the classifiers. Three different machine learning (ML) methods, which are uncorrelated and skillful on the problem in different ways, are employed as the base learning model. Finally, a recently developed deep learning approach, namely a temporal convolutional network (TCN), is used to ensemble the outputs of the ML algorithms for improved classification accuracy. Experimental results confirm that the proposed framework yields a highly-accurate, robust classification performance, in comparison to other well-established machine and deep learning models and thus can be a practical tool for electricity theft detection in industrial applications

Big Data Analytics for Electricity Theft Detection in Smart Grids

In Smart Grids (SG), Electricity Theft Detection (ETD) is of great importance because it makes the SG cost-efficient. Existing methods for ETD cannot efficiently handle data imbalance, missing values, variance and non-linear data problems in the smart meter data. Therefore, an effective integrated strategy is required to address underlying issues and accurately detect electricity theft using big data. In this work, a simple yet effective approach is proposed by integrating two different modules, such as data pre-processing and classification, in a single framework. The first module involves data imputation, outliers handling, standardization and class balancing steps to generate quality data for classifier training. The second module classifies honest and dishonest users with a Support Vector Machine (SVM) classifier. To improve the classifier’s learning trend and accuracy, a Bayesian optimization algorithm is used to tune SVM’s hyperparameters. Simulation results confirm that the proposed framework for ETD significantly outperforms previous machine learning approaches such as random forest, logistic regression and SVM in terms of accuracy

Optimal power flow solution with uncertain RES using augmented grey wolf optimization

This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions