A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

Physicochemical and technological properties of beef burger as influenced by the addition of pea fibre

This study aimed to evaluate the physicochemical characteristics and sensory attributes of beef burgers with the addition of pea fibre as a partial substitute of meat or fat. Three formulations were prepared: control (CON) – similar to the commercial formulation; fibre/less meat (FLM)—5% meat reduction and addition of 1% pea fibre; fibre/less fat (FLF)—7% fat reduction and addition of 1% pea fibre. Non-significant differences were obtained for pH, colour parameters (L* and b*), texture profile, cooking loss and size reduction among formulations. Moreover, sensory analysis with consumers of beef burgers did not indicate differences among the formulations for all the analysed attributes. Therefore, pea fibre is a promising partial replacer for meat and fat in beef burgers due to the preservation of technological parameters and sensory acceptance.     

Boosting the photoconversion efficiency of TiO2 nanotubes using UV radiation-assisted anodization as a prospective method: An efficient photocatalyst for eliminating resistant organic pollutants

1D TiO₂ nanotube arrays (TNTs), as versatile nanostructures, have attracted a considerable amount of scientific attention, particularly in photocatalytic applications. In the present study, UV radiation-assisted anodization method with various irradiation times (30–120 min) was employed as a preferable approach to fabricating TNTs with remarkable optical property and photocatalytic activity. The results revealed that in situ irradiation not only improved the surface area (from 30.10 to 48.5 m²), but also increased the roughness factor (from 77.27 to 124.73). Furthermore, UV radiation had a significant impact on optical property and by altering elemental composition, led to a red shift in absorption edge (from 3.2 to 1.4eV). Meanwhile, voltammetric experiments showed that 120 min UV radiation during anodization was able to substantially cause a surge of the photocurrent density and the photoconversion efficiency of TNTs from 0.15 to 0.55 mA cm⁻² and from 13% to 40%, respectively. As a consequence of the improvement in optical property and photochemical features, anodic TNTs fabricated under 120 min UV radiation could increase the photocatalytic degradation of 2,4-DCP from 75% to 100%. Moreover, the kinetics study showed that all photocatalytic reactions followed zero-order kinetics which rate constant over the synthesized TNTs under 120 min UV radiation was about 5.1 times greater than that of conventionally fabricated TNTs. Likewise, the pathway of photocatalytic degradation and the proportion of reactive species in this process were assessed by scavenging tests. The results confirmed that holes (h⁺) play the main role that 53% of photocatalytic degradation occurred via both direct and indirect reactions with h⁺ species. The rest of the degradation pathways were also allocated to e⁻ and ^•O₂⁻ species by accounting for 37% and 10%, respectively.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

Temperature adaptability of the soluble lead flow battery using different solutions of fluoborate,perchlorate, methanesulfonate and trifluoromethanesulfonate

The evaluation of cell's weatherability is of practical interest. To further improve the soluble lead flow battery's weatherability, physiochemical properties of electrolytes containing fluoborate, perchlorate, methanesulfonate and trifluoromethanesulfonate are investigated from ?60 to 50 °C. Activities of CF₃SO₃H and HClO₄ are poor in trifluoromethanesulfonate and perchlorate solutions due to common anion effect. The solubility of lead salt can be improved by increasing temperature, but worsened by increasing acid's content. With the temperature increasing, the conductivity is enhanced, and the viscosity is lowered for four solutions. The same results have been found by increasing acid's content except for CF₃SO₃H. The high energy efficiency can be achieved for cells over ?40–0 °C using fluoborate and perchlorate solutions, 73.2% at ?40 °C and 78.1% at ?30 °C respectively. Over the temperature range of 20–50 °C, the cells with methanesulfonate and trifluoromethanesulfonate solutions have good performance, 77.4% and 73.7% at 50 °C respectively.     

Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

Synthesis and Characterization of Co Nanocomposite Based on Poly(benzimidazole-amide) Matrix and Their Behavior as Catalyst in Oxidation Reaction

2,6-Bis(5-amino-1H-benzimidazol-2-yl)pyridine was prepared and characterized by Fourier transform infrared spectroscopy, elemental analysis, ¹H-NMR, and ¹³C-NMR spectroscopic methods. Then a new poly(benzimidazole-amide) was synthesized by polymerization of the corresponding diamine and isophthalic acid. The obtained poly(benzimidazole-amide) exhibited good yield and high thermal stability. Due to the existence of benzimidazole moieties in polymer’s structure, it has the tendency to form complexes with metal ions. So, a new poly(benzimidazole-amide)/Co nanocomposite was prepared. Morphological studies revealed that metal nanoparticles were dispersed in the polymer matrix without any aggregation. poly(benzimidazole-amide)/Co nanocomposite was used as a catalyst in the oxidation of ethyl benzene to acetophenone with tert-butyl hydroperoxide.     

Sensitivity analysis of Johnson-Cook material parameters on adiabatic shear in different directions

ABSTRACT

This study investigates the effects of strain, strain rates, and forming directions (RD-rolling direction, TD-transverse direction, and ND-normal direction) on adiabatic shear, via dynamic impact compression tests using the Split Hopkinson Pressure Bar (SHPB) apparatus. A modified Johnson-Cook (J-C) constitutive model is proposed, which used to analyse the influence of the constitutive parameters on the sensitivity of adiabatic shear, employing a finite element software. The different sensitivities of adiabatic shear under different directions are explained by combining microscopic analysis and results from mechanical responses. The results show that the sensitivity of adiabatic shear can be related to the time of stress collapse in the following trend: ND?>?TD?>?RD; the sensitivities of these constitutive parameters on adiabatic shear are calculated and compared.