Journal of Health and Safety at Work

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Introduction: Noise is one of hazardous agents of workplaces in industries. Electric industries and power plants are among industries in which noise pollution should be taken into account. This study aimed to develop and verify a statistical model for acoustic wave propagation in an indoor environment for noise related risk reduction management.

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Material and Method: This theoretical-experimental study was performed in a gas power plant with nominal capacity of 648 MW including four gas turbine with nominal capacity of 162 MW. The relations between sound power level and sound pressure level in indoor environments were used for developing the statistical model. Moreover, verification of the obtained data was done by a field inside a turbine hall study using ISO9612 standard method.

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Result: Comparison of results from the field and the theoretical study showed no significant differences. The differences between predicted value and field measurements was, in average, less than 0.5 db. This shows the acceptable accuracy of the presented model in estimating the workplace noise level, according to the real functional conditions of the noise sources.

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Conclusion: The presented model is easy and practical and allows managers to model scenarios of noise pollution reduction in indoor environments, before huge expenses of actual control measures. This method is faster comparing to numerical modeling methods. Furthermore, its accuracy is also acceptable.

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Introduction: The Power plants are as the major industries that have a large number of workers, providing they health is important. Exposure to occupational noise is the pervasive physical agent in industries like power plants and may impact health and for safety status for people. Determination of the sources of noise in workplaces is important step in noise control plans. This study aimed to assess noise pollution and determine the main sources of noise in a power plant for.

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Material and Method: This descriptive cross-sectional study was conducted on the ground floor of the turbine unit of a thermal power plant. Measurements was done based on the ISO-9612 and ISO-11200 standard using by the calibrated sound level meter model TES-1389 on “A” frequency weighting and “Slow” mode for time response. Surfer V.10 software was used for interpolation and noise maps producing. Based on grid method measuring for indicating of noise propagation, we set buffer of danger areas to determine main sources of noise. Also, we used the minimized grid method for measuring and study of noise denotation in vertical surface in each main sources.

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Result: The results showed that the measured indoor sound pressure level in all areas where at the risk category of the units. The highest average sound pressure level was belonged to the unit 2, with 93.1 dB(A). We find the feed water pumps were the main sources in all of the studied units. Among the four main components of the main sources, the highest noise levels associated with the main pump and the gearbox section with about 100 9B(L) and dominant frequency of 2000 Hz.

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Conclusion: Considering that feed water pumps had major sources in indoor power plant noise pollution, engineering noise control such as providing enclosures is necessary to reduce noise pollution to safe levels in the studied plant it is necessary.

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Introduction: The weaknesses of traditional hazard analysis methods lead to their inefficiency to utilization for modern socio-technical systems. System Theoretic Process Analysis (STPA), which is in the category of systematic analysis methods, has a powerful logic to identify hazards in such systems,as a suitable alternative method. This study aimed to analyze hazards associated with extinguishing systems of steam unit of a power plant, using STPA method.

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Material and Method: The present research is a qualitative case study. The related hazards were defined using STPA method.Following, the safety control structure diagrams in different parts were plotted and inadequate control measures and its causal factors were identified.

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Result: For steam unit of power plant, the most tragic incidents wererelated to hazards and risks of turbine device (switch the turbine cycle). Then, according to the plotted diagram for structure of safety control extinguishing systems associated with switching the turbine cycle, PLC system was determined as the most important part of the control system and operator was identified as the strategic and effective part of a control system. Following, more than 54 causal factors were identified, considering the relevant details about the risks analysis of the turbine.

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Conclusion: Due to its systematic structure,STPA method can be effective for a more complete identification of risks and causal factors which causing hazards in the emergency extinguishing systems. Therefore, development of such tools for those operators involved in safety-critical systems will be useful in terms of safety.

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Introduction: Each year tens of millions of labors are victims of occupational incidents leading to the disablement or death of many of them. The present study has been conducted with the aim of predicting the performance monitoring indicators of incident after implementation of the integrated management system in order to reduce number of incidents, safety monitoring, and giving priority to safety programs in the organization policies.

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Matherial and Methods: Using safety performance indicators, this descriptive-analytical study has been done in two stages in a combined cycle power plant on 254 incidents before the implementation (2004) and the years after the implementation of integrated management systems(a seven year period). The required data was gathered through checklist and interview with the injured workers. Kolmogorov-Smirnov, Chi Square, and Cubic regression tests were utilized for data analysis at significance level of 0.05.

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Results: Of the 1131 labors during a seven year period, 254 occupational incidents were recorded. The highest value of accident frequency rate was in 2004 (32.65) while the highest accident severity rate was in the year 2008 (209). Moreover, the lowest frequency rate and severity rate were related to the year 2011 with value of 9.75 and 29.26, respectively. Regression graphs, between the observed values and the estimated values, showed that the coefficients of all the indicators, except for β1of ASR,are significant(P< 0.05) as expected. This result implies the improvement in the safety performance and integrated management systems.

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Conclusion: Findings show that Cubic regression can be an appropriate to olforinvestigating the indicators trends and for their predictionin planning and monitoring the performance ofsafety unitso that the decision-making for determining the priority of organizations’ safety programs would be facilitated.

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Introduction: Occupational accidents are of the main issues in industries. It is necessary to identify the main root causes of accidents for their control. Several models have been proposed for determining the accidents root causes. FTA is one of the most widely used models which could graphically establish the root causes of accidents. The non-linear function is one of the main challenges in FTA compliance and in order to obtain the exact number, the meta-heuristic algorithms can be used.

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Material and Method: The present research was done in power plant industries in construction phase. In this study, a pattern for the analysis of human error in work-related accidents was provided by combination of neural network algorithms and FTA analytical model. Finally, using this pattern, the potential rate of all causes was determined.

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Result: The results showed that training, age, and non-compliance with safety principals in the workplace were the most important factors influencing human error in the occupational accident.

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Conclusion: According to the obtained results, it can be concluded that human errors can be greatly reduced by training, right choice of workers with regard to the type of occupations, and provision of appropriate safety conditions in the work place.

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Introduction: Cognitive emotion regulation (CER) strategies define as cognition-based responses according to emotion-eliciting experiences that can change the type and severity of individuals’ reactions and behaviors. This modification may positively or negatively affect cognitive performance and therefore, it is a defining issue in the workplace. Notably, industries such as combined cycle power plants need to hire staff with a high cognitive ability to perform their duties in a highly efficient way. Since CER is of great importance for overall health and cognitive performance, we aimed to evaluate the state of CER among control room operators (CROs) in the Fars combined cycle power plant.
Material and Methods: The CER questionnaire (CERQ) measures nine cognitive coping strategies (i.e., self-blame, other-blame, rumination, catastrophizing, positive refocusing, planning, positive reappraisal, putting into perspective, and acceptance) that are followed when an individual confronts negative events. The CERQ (short version) was administered to 57 male CROs at the Fars combined cycle power plant. The questionnaire also collected demographic data. Statistical analyses were performed using SPSS Version 25.0. For the normality test, Shapiro-Wilk was the method of choice.
Results: Results of the CERQ scoring showed that the median with interquartile range (IQR) in appropriate and inappropriate categories were 3.50 (3.30-4.00) and 2.62 (2.25-3.06), respectively. Of note, age (39.07 ±6.19) and work experience (14.49±6.26) were not significantly correlated with the results of CERQ.
Conclusion: Taken together, adaptive cognitive strategies (acceptance, positive refocusing, planning, positive reappraisal, and putting into perspective) were reported to be used more often than less adaptive strategies. In general, staff strategies were appropriate while facing a negative event. Although assessing CER in high-demand workplaces is necessary, factors such as managerial styles, job engagement, job satisfaction, and larger sample size should be further studied.