The removal of cationic dyes from aqueous solutions by adsorption onto pistachio hull waste

The efficacy of pistachio hull powder (PHP) prepared from agricultural waste was investigated in this study as a novel adsorbent for the elimination of dye molecules from contaminated streams. Removal of methylene blue (MB) as a cationic model dye by PHP from aqueous solution was studied under different experimental conditions. The selected parameters were solution pH (2–10), PHP dosage (0.5–3 g/L), MB concentrations (100–400 mg/L), contact time (1–70), and solution temperature (20–50 °C). The experimental results indicated that the maximum MB removal could be attained at a solution pH of 8. The dosage of PHP was also found to be an important variable influencing the MB removal percentage. The removal efficiency of MB improved from 94.6 to 99.7% at 70 min contact time when the MB concentration was decreased from 300 to 100 mg/L at a pH and PHP dosage of 8 and 1.5 g/L, respectively. The kinetic analysis showed that the pseudo-second-order model had the best fit to the experimental data. The Langmuir equation provided the best fit for the experimental data of the equilibrium adsorption of MB onto PHP at different temperatures. In addition, the maximum adsorption capacity increased from 389 to 602 mg/g when the temperature was increased from 20 to 50 °C. The thermodynamic evaluation of MB adsorption on PHP revealed that the adsorption phenomenon under the selected conditions was a spontaneous physical process. Accordingly, pistachio hull waste was shown to be a very efficient and low-cost adsorbent, and a promising alternative for eliminating dyes from industrial wastewaters.     

Identifying cost-optimal options for a typical residential nearly zero energy building’s design in developing countries

Clean Technologies and Environmental Policy - High population growth rate and the limited non-renewable energy sources such as fossil fuels have made different challenges for the energy supply in...     

Perceived demands and musculoskeletal symptoms among employees of an Iranian petrochemical industry

As a part of a comprehensive ergonomics program, this study was conducted among employees of an Iranian petrochemical industry to determine the prevalence of musculoskeletal symptoms and to examine the relationship between perceived demands and reported symptoms. In this cross-sectional study, 928 randomly selected employees, corresponding to nearly 40% of all employees participated. Nordic Musculoskeletal Disorder Questionnaire and Job Content Questionnaire were used as collecting data tools. The results showed that 73% of the study population had experienced some form of symptoms from the musculoskeletal system during the last 12 months. Knees and lower back symptoms were the most prevalent problem among the employees studied. The results revealed that perceived physical demands were significantly associated with musculoskeletal symptoms (OR ranged from 1.45 to 2.33). Among the perceived physical demands, awkward working postures were most frequently associated with reported musculoskeletal symptoms. Association was also found between perceived psychological demands and reported symptoms. Conflicting demands, waiting on work from other people or departments, interruption that other make, working very fast and time pressure were psychological factors retained in the regression models with OR ≥ 1.49. Based on the findings, it could be concluded that any interventional program for preventing or reducing musculoskeletal symptoms among the petrochemical employees studied had to focus on reducing physical demands, particularly awkward working postures as well as psychological aspect of working environment.

Relevance to industry

In petrochemical industry where employees are involved in both static and dynamic activities, determination of musculoskeletal symptoms contributing factors can be considered as a basis for planning and implementing interventional ergonomics program for preventing musculoskeletal symptoms and improving working conditions.     

On State-dependent dynamic graphs and their controllability properties

We consider distributed dynamic systems operating over a graph or a network. The geometry of the network is assumed to be a function of the underling system's states-giving it a unique dynamic character. Certain aspects of the resulting abstract structure, having a mixture of combinatorial and system theoretic features, are then studied. In this venue, we will explore an interplay between notions from extremal graph theory and system theory by considering a controllability framework for such state-dependent dynamic graphs.     

Biological removal of phenol from strong wastewaters using a novel MSBR

In this study, the performance of a moving-bed sequencing batch reactor (MSBR) that removes phenol from wastewater is presented. The effects of phenol concentration (50-3325 mg L⁻¹), filling time (0-4 h) and aerating time (4-18 h) on the performance of the MSBR are given in terms of phenol and COD removal efficiencies. Moreover, the effect of the moving media on the overall performance of the reactor is also determined. The reactor can completely remove phenol and COD at inlet concentrations up to 3000 mg phenol L⁻¹ (6780 mg COD L⁻¹), which was the inhibition concentration, and with a 24-h cycle time. The filling time range tested here did not significantly affect phenol or COD removal. The optimum hydraulic retention time (HRT) for the MSBR is 40 h and the critical phenol loading rate is 83.4 g phenol m⁻³ h⁻¹, which gives a phenol removal efficiency of 99%. The reactor can also withstand shock loads from slug feeding. The moving bed contribution to phenol and COD removal efficiencies was up to 28.1 and 34.7%, respectively, at the phenol loading rate of 83.4 g m⁻³ h⁻¹. The findings of this investigation suggest that MSBR can be a robust and promising process for effectively treating wastewaters containing inhibitor or recalcitrant compounds in industrial settings.     

Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Chromium carbonitride coatings were formed on plain carbon and alloy steels by pre-nitrocarburizing, followed by thermoreactive deposition and diffusion in a salt bath below 700 °C. In the present study, an artificial neural network-based model (ANNs) was developed to predict the layer thickness of pre-nitrided steels. Seventeen parameters affecting the layer thickness were considered as inputs, including the pre-nitriding time, salt bath compositions ratio, salt bath aging time, ferrochromium particle size, ferrochromium weight percent, salt bath temperature, coating time, and different chemical compositions of steels. The network was then trained to predict the layer thickness amounts as outputs. A 2-feed-forward back-propagation network was developed and trained using experimental data form literatures. Five steels were investigated. The effects of coating parameters on the layer thickness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the layer thickness.     

ANN-based prediction of ferrite fraction in continuous cooling of microalloyed steels

In the present study, an artificial neural networks-based model was developed to predict the ferrite fraction of microalloyed steels during continuous cooling. Fourteen parameters affecting the ferrite fraction were considered as inputs, including the cooling rate, initial austenite grain size, and different chemical compositions. The network was then trained to predict the ferrite fraction amounts as outputs. A multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the ferrite fraction.     

Nigella sativa L. improves lipid profile and prevents atherosclerosis: Evidence from an experimental study on hypercholesterolemic rabbits

The effects of Nigella sativa (NS) on lipid profile, C-reactive protein, oxidized LDL, antioxidant capacity, Apo A and Apo B and fatty streak formation in hypercholesterolemic male rabbits were examined. Fifteen rabbits were distributed into three groups of five each [normal diet, hypercholesterolemic diet (1% cholesterol) and hypercholesterolemic diet (1% cholesterol) supplemented with 5% NS]. They received rabbit laboratory chow diet for a period of 8 weeks. At the start and end of the study, fasting blood was taken from all animals. NS significantly decreased fatty streak formation as well as total cholesterol (TC) and low-density lipoprotein (LDL) in hypercholesterolemic male rabbits. NS showed positive effects on other factors (triacylglycerol, high-density lipoprotein, C-reactive protein, oxidized LDL, antioxidant capacity, Apo A and Apo B), but these were insignificant. Based on favorable efficiency of NS on TC and LDL reduction and its anti-atherogenic cardioprotective properties, it is suggested using this plant with fat-rich diets simultaneously may reduce their adverse health effects.     

Thermal analysis,design, and implementation of an L-band high-power stepped impedance low-pass filter

This paper presents high power, thermal analyses, and implementation of a stepped impedance high-power low-pass filter (LPF). A comprehensive model and analysis have been developed for the design and simulation of the LPF. In this analysis, power handling capacity and breakdown-voltage are discussed, and the effects of critical points are considered. The attenuation due to conductor and dielectric losses is also studied. The novelty of our approach lies in employing theoretical analysis to estimate the power-dissipation of the filter based on the proposed equivalent circuit. An accurate method is also introduced to calculate attenuation in the filter's elements. Thermal analysis to obtain accurate temperature profiles is done for the first time based on the electro-thermal simulation. Consequently, an effective cooling method is used to spread heat across the entire filter. Finally, the filter was implemented and tested to operate at L-band with handling 8 kW peak and 800 W average power. The insertion-loss is less than 0.27 dB, the stop-band attenuation is more than 60 dB, and the return-loss is better than 15.7 dB. The filter is capable of tolerating produced heat without any destructive effects at a maximum temperature of about 200°C above ambient. The theoretical analysis and experimental results show that the LPF is suitable for high-power microwave applications.