Application of SHERPA to Identify and Prevent Human Errors in Control Units of Petrochemical Industry

Introduction. Studying human errors as a risk factor in the occurrence of accidents is necessary. Thus, the aim of this study was to identify, predict and control human errors in industrial control units. Method. This is a case study carried out using SHERPA in the first unit of Zagros Methanol of Asalooyeh, Iran, and its subunits. To collect the required data, various methods were used: observing, interviewing processing specialists and control unit operators, and studying technical documents and records. Results. In total, 222 human errors were identified in various occupational tasks. This study showed that 48.62% of them were action errors, 31.97% were checking errors, 6.75% were retrieval errors, 11.70% were communication errors and 0.90% were selection errors. Conclusion. It can be inferred that this method is appropriate for different industries, and it is useful for identifying human errors leading to hazardous accidents.     

Non-conforming fibre-reinforced green polypropylene composite panels: a case study

Journal of Material Cycles and Waste Management - Coal Bottom Ash (CBA) is one of the byproducts of the coal combustion process in power plants that accumulates in landfills due to its porous,...     

Higher efficiency and lower environmental impact of membrane separation for carbon dioxide capture in coal power plants

Environmental Chemistry Letters - Global warming may be slowed down by carbon capture and storage systems that allow to sequester carbon dioxide from large fixed point sources such as power plants...     

Simultaneous determination of binary solution of triphenylmethane dyes in complex matrices onto magnetic amino-rich SWCNT using second-order calibration method

This study suggested a new method for simultaneous quantification of two dyes in complex matrices using second-order data by spectrophotometry. Second-order data was generated simply without any expensive instrument using two independent variables including wavelength and the monotonic addition of sodium dodecyl sulfate. Solid-phase extraction (SPE) based on amino-rich magnetic single-walled carbon nanotube as an adsorbent was employed prior to second-order data generation. SPE optimization was performed by Box-Behnken design, and parameters and their interaction which were dependent on the simultaneous extraction of dyes were examined. Competitive Langmuir and Freundlich isotherms for a binary system and individual dyes could all represent the equilibrium data well. The second-order data was processed by parallel factor analysis (PARAFAC and PARAFAC2) and multivariate curve resolution-alternating least squares (MCR-ALS). Figures of merit of the model including a limit of detection of 3.0 and 2.5 ng mL^?1 for crystal violet and malachite green, respectively, were estimated using the MCR-ALS method. The combination of the second-order calibration and SPE presents an easy and versatile method for determination of the mixture of two dyes in the presence of uncalibrated interferences in environmental water, synthetic, and fish samples with the recoveries of 94–104.     

Factorial analysis of diesel engine performance using different types of biofuels

In this study, several bio-source-fuels like fresh and waste vegetable oil and waste animal fat were tested at different injector pressures (120, 140, 190, 210 bar) in a direct-injection, naturally aspirated, single-cylinder diesel engine with a design injection pressure of 190 bar. Using 2k factorial analysis, the effect of injection pressure (Pi) and fuel type on three engine parameters, namely, combustion efficiency (etac), mass fuel consumption (mf), and engine speed (N) was examined. It was found that Pi and fuel type significantly affected both etac and mf while they had a slight effect on engine speed. Moreover, with diesel and biodiesels, the etac increased to a maximum at 190 bar but declined at the higher Pi value. In contrast, higher Pi had a favorable effect on etac over the whole Pi range with all the other more viscous fuels tested. In addition, the mass fuel consumption consistently decreased with an increase in Pi for all the fuels including the baseline diesel fuel, with which the engine consistently attained higher etac and higher rpm compared to all the other fuels tested.     

Faecal sterols as sewage markers in the Langat River, Malaysia: Integration of biomarker and multivariate statistical approaches

The Langat River in Malaysia has been experiencing anthropogenic input from urban, rural and industrial activities for many years. Sewage contamination, possibly originating from the greater than three million inhabitants of the Langat River Basin, were examined. Sediment samples from 22 stations (SL01-SL22) along the Langat River were collected, extracted and analysed by GC-MS. Six different sterols were identified and quantified. The highest sterol concentration was found at station SL02 (618.29 ng/g dry weight), which situated in the Balak River whereas the other sediment samples ranged between 11.60 and 446.52 ng/g dry weight. Sterol ratios were used to identify sources, occurrence and partitioning of faecal matter in sediments and majority of the ratios clearly demonstrated that sewage contamination was occurring at most stations in the Langat River. A multivariate statistical analysis was used in conjunction with a combination of biomarkers to better understand the data that clearly separated the compounds. Most sediments of the Langat River were found to contain low to mid-range sewage contamination with some containing `significant' levels of contamination. This is the first report on sewage pollution in the Langat River based on a combination of biomarker and multivariate statistical approaches that will establish a new standard for sewage detection using faecal sterols.     

Integrating local knowledge with science and technology in management of soil,water and nutrients: implications for management of natural capital for sustainable rural livelihoods

Harnessing local knowledge and practices for sustainable management of soil, water and nutrients is important for sustainable development. This work aims to understand soil productivity in traditional open-ridging practices and integrate scientific ideas and technologies into traditional practices for enhancing management of soil productivity. Participatory assessments, household surveys, field experiments, observations and analyses of outcomes were used. Participatory assessments revealed declining natural soil fertility and maize productivity over time, a concern reiterated by 82.5% of respondents. Evaluation of ridge tillage practices showed that bean yields were significantly (p?<?0.05) higher by 19–35% in traditional open ridges and increased by 23–38% when ties were integrated. Integration of fertilizers resulted in significantly (p?<?0.05) higher subsequent maize yields in tied ridges than in traditional open ridge and flat cultivation practices. Improved integrated tillage practices resulted in increased soil and water conservation, soil fertility and crop tolerance to prolonged no-rainfall spells. National development policies should create enabling environments for smallholder farmers to integrate modern knowledge and technologies with their traditional knowledge and practices in land management for high and sustained quality of natural capital for secure and sustainable livelihoods.     

Influence of seafloor topography on the depositional behavior of bi-disperse turbidity currents: a three-dimensional,depth-resolved numerical investigation

We discuss the results of direct numerical simulations of bi-disperse turbidity currents interacting with a flat bottom wall and a Gaussian bump, respectively, with a focus on the final deposit profiles of the coarse and fine particles. We identify regions of reduced and enhanced deposition, as a result of the presence of the bump. Coarse particles are predominantly deposited towards the sides of the bump, as a result of the bi-section and lateral deflection of the current by the bump. In contrast, for fine particles the influence of the bump is felt more in its far wake. We furthermore employ Lagrangian markers in order to track the coarse and fine particles in the current, and to investigate their deposit locations as function of their location of origin. By comparing the final deposit maps, we observe that the bump has the strongest influence on those particles originating in the central lock sections.     

Mixing dynamics of turbidity currents interacting with complex seafloor topography

Direct Numerical Simulations are employed to investigate the mixing dynamics of turbidity currents interacting with seamounts of various heights. The mixing properties are found to be governed by the competing effects of turbulence amplification and enhanced dissipation due to the three-dimensional topography. In addition, particle settling is seen to play an important role as well, as it affects the local density stratification, and hence the stability, of the current. The interplay of these different mechanisms results in the non-monotonic dependence of the mixing behavior on the height of the seamount. Regions of dilute lock fluid concentration generally mix more intensely as a result of the seafloor topography, while concentrated lock fluid remains relatively unaffected. For long times, the strongest mixing occurs for intermediate bump heights. Particle settling is seen to cause turbidity currents to mix more intensely with the ambient than gravity currents.     

Spatial analysis of chromium in southwestern part of Iran: probabilistic health risk and multivariate global sensitivity analysis

This study was concerned with chromium as a potential carcinogenic contaminant in 64 wells located in five aquifers, southwest of Iran. A probabilistic health risk assessment indicated a high risk to the local residents including adults and children in the study area. A sequential sensitivity analysis and a novel approach known as multivariate global sensitivity analysis using both principal component analysis and B-spline were applied to investigate the behavior of health risk model along time considering four independent input parameters in the risk equation. In this context, based on the results of sensitivity analysis, concentration of chromium in drinking water (C_w) and body weight (W) were the most influential parameters. Random forest (RF) was used as a variable selection method to choose the most influential parameters for the prediction of chromium. Five parameters, among 13 water quality variables, including phosphate, nitrate, fluoride, manganese and iron were selected by RF as the most important parameters for spatial prediction. Hybrid methods of RF and ordinary kriging (RFOK) and RF and inverse distance weighting (RFIDW) were then applied for spatial prediction of Cr using the secondary variables. The RFOK and RFIDW were more efficient than that of ordinary kriging (OK) with respect to a cross-validation algorithm. For instance, in terms of relative root mean squared error, the performance of OK was improved from 31.72 to 23.21 and 23.61 for RFOK and RFIDW, respectively.