Application of mechanical shear in an internal-recycle for the enhancement of mesophilic anaerobic digestion.

A combination of bench- and full-scale studies were conducted to determine the effectiveness of high-intensity mechanical shear in an internal recycle loop to enhance mesophilic anaerobic digestion and the implications of this process for routine operations of a digestion system. During short-term batch digestion (56 hours), a 46% increase in biogas production was observed. However, it was found that the degree of digestion enhancement was sludge-specific, with increases in volatile solids destruction ranging from 16.6 to 110%. A full-scale demonstration showed increased total and volatile solids destruction of 22 and 21% for the primary digester and 17.2 and 11% for the secondary digester, respectively. The data also suggest that increased protein degradation is one of the major mechanisms associated with the observed increases in volatile solids destruction. The full-scale demonstration also determined that shear enhanced digestion can be operated without process upset, based on volatile fatty acid profile and headspace biogas composition (methane and carbon dioxide). Dewatering properties, as measured by polymer demand, deteriorated in the primary digester, but there was improvement in the secondary digester. High-intensity shear does not appear to enhance pathogen reduction based on total and fecal coliform bacterial enumeration.     

Distribution of heavy metals in plants and fish of the Yamuna river (India)

The distribution of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in the plants and fish of Yamuna river from Delhi to Allahabad, a distance of about 840 km, at five sampling stations was determined in the year 1981. The results have shown wide variations in the heavy metal levels from one sampling station to the other. The concentrations of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn in the plants (Eicchornia crassipes) were found to be 0.02–0.12, 2.7–21.3, 4.6–64.8, 9.8–114.0, 193.0–1835.0, 380.0–1443.0, 4.4–83.0, 4.8–30.2, and 22.1–356.5 g g^-1 respectively whereas in the fish (Heteropnuestes fossilis) were found to be ND-0.40, 2.3–13.7, 3.7–26.9, 8.33–58.1, 278.3–1108.0, 81.3–213.8, 2.8–32.7, 1.4–12.8 and 101.8–364.8 gg^-1 respectively on dry weight basis.     

Analyzing the factors affecting environmental risks of projects using a hybrid approach of DEMATEL‐ANP,artificial neural network: A case study

The increasing growth of the economy in each country necessitates a great amount of investment in infrastructure. The belief that projects involve various uncertainties, such as technical skills, management quality, and the like, indicates that most projects fail to achieve their aims, interests, costs, as well as their timeframes and space requirements. As the environment can pose significant uncertainty to any project, environmental risks should be deeply studied by project management departments. This study intends to analyze as a case the environmental risk management system within a consulting firm. From this analysis, each aspect of a project's environmental risk management is ranked using a fuzzy analytical network process (ANP), a neural network algorithm, and a decision‐making trial and evaluation laboratory (DEMATEL) methodology. From the organizational aspect, budget risk is the most significant. From the technical aspect, the risk of regulations is the most important one. Finally, the risk of project failure from poor communication is another identified main risk in this research. By studying high‐ranking items in this hierarchy, it can be understood that these criteria exist in different aspects; therefore, all aspects of the risk should be taken into account to cover and assess risk. A neural network algorithm for validating and reassessment of ranking is employed. Results of this application showed that, based on Spearman's rank correlation method, two different approaches resulted in similar rankings. Finally, some practical implications for responding to the most highly ranked risks are proposed.     

Relationships between certain individual characteristics and occupational accidents

Background. Work accidents and injuries are an occupational health and safety problem. Methods. This cross-sectional study was conducted for 404 frontline workers who were randomly selected from the Arya Sasol Petrochemical Company in Bushehr, Iran, during 2016. A statistical analysis was performed using the χ² test and the logistic regression model with SPSS version 18. Results. The overall occupational accident rate among the participants was found to be 9.2%. Statistically significant differences were observed for body mass index, education, job experience, smoking habit, consumption of sedative pills and presence of sleep disorders. Three factors had significant adjusted odds ratios (ORs): body mass index (OR 2.41, 95% confidence interval [CI] [1.16, 5.04]), education (OR 0.25, 95% CI [0.07, 0.85]) and job experience (OR 0.29, 95% CI [0.15, 0.57]). Conclusions. Preventive programs should be implemented for industrial workers, especially young workers, workers who smoke, overweight workers and workers with psychosomatic disease.     

Conceptual compression discussion on a multi-linear (FTA) and systematic (FRAM) method in an offshore operation’s accident modeling

Risk assessment can be classified into two broad categories: traditional and modern. This paper is aimed at contrasting the functional resonance analysis method (FRAM) as a modern approach with the fault tree analysis (FTA) as a traditional method, regarding assessing the risks of a complex system. Applied methodology by which the risk assessment is carried out, is presented in each approach. Also, FRAM network is executed with regard to nonlinear interaction of human and organizational levels to assess the safety of technological systems. The methodology is implemented for lifting structures deep offshore. The main finding of this paper is that the combined application of FTA and FRAM during risk assessment, could provide complementary perspectives and may contribute to a more comprehensive understanding of an incident. Finally, it is shown that coupling a FRAM network with a suitable quantitative method will result in a plausible outcome for a predefined accident scenario.     

The potential use of ultrasound-assisted bleaching in removing heavy metals and pigments from soybean oil using kinetic,thermodynamic and equilibrium modeling

Environmental Science and Pollution Research - In this research, the sorption behavior (kinetic, isotherm, and thermodynamic modeling) of heavy metals (Cu (II) and Fe (II)) and pigments (carotenoid...     

Effect of Nano-SiO<Subscript>2</Subscript> and Bark Flour Content on the Physical and Mechanical Properties of Wood–Plastic Composites

The aim of this study is to evaluate the impact of nano-SiO₂ and bark flour (BF) on the natural fiber–plastic composites engineering properties made from high density polyethylene (HDPE) and beech wood flour (WF). For this purpose, WF and BF in 60 mesh size and weight ratio of (50, 0 %), (30, 20 %), (10, 40 %) and (0, 50 %) respectively were mixed with HDPE. In order to increase the interfacial adhesion between the filler and the matrix, the maleic anhydride grafted polyethylene was constantly used at 3 wt% for all formulations as a coupling agent. The nano-SiO₂ particles with weight ratio of 0, 1, 2, and 4 % were also utilized to enhance the composites properties. The materials were mixed in an internal mixer (HAAKE) and then the bark and/or wood–plastic composite samples were made utilizing an injection molding machine. The physical tests including water absorption and thickness swelling, and mechanical tests including bending characteristics and un-notched impact strength were carried out on the samples based on ASTM standard. The results indicated that as the BF content increased in the composite, mechanical and physical properties were reduced, but the given properties were increased with the addition of nano-SiO₂. The addition of nano-SiO₂ had a negative impact on the physical properties, but when it was up to 2 %, it increased the impact strength.     

Minimum quantity lubrication in grinding: effects of abrasive and coolant–lubricant types

This research has been conducted to study the influence of the abrasive and coolant–lubricant types on the minimum quantity lubrication (MQL) grinding performance. One type of CBN and three types of conventional wheels (corundum) have been tested. The tests have been performed in presence of fluid, air jet and eleven types of coolant–lubricants, as well as, in dry condition. The results indicate that the finest surface quality and lower grinding forces could be obtained while grinding with CBN wheel. In case of conventional wheels, the coarser and high porosity wheels induce much proper grinding results. Furthermore, grinding when utilizing MQL with oil results in higher grinding performance.