Evaluation of the relationship between the uses of safety procedures in the rate of human error in Yazd Combined Cycle Power Plant

Introduction: About 60 to 90 percent of an accident in the industry is caused by human error. This study aimed to assess the effectiveness of safety procedures in reducing human error in Yazd Combined Cycle Power Plant employees. Materials and Methods: The present study is a quasi-experimental intervention that was conducted aimed to measure the human error of 121 employees of Yazd Combined Cycle Power Plant in 5 occupational groups (safety and firefighting, generator operation, generator operation, chemical refinery, warehouse keeping, and generator repair) in 2020. First, job safety analysis (JSA) was performed. Safety procedures related to each job were prepared, and with the help of their items and observing the process of doing work, the percentage of human error before and after the investigation was calculated. The data were analyzed using descriptive statistical indicators including mean, standard deviation, median, interquartile range, frequency and frequency percentage, and Wilcoxon nonparametric test by SPSS software version 24. The significance level was considered 0.05. Results: A study on five main jobs, according to Wilcoxon test results showed, the highest and lowest human error percent before the intervention was related to generator repair occupational groups (58.59%) and warehouse keeping (4.16%). A significant difference was between human error percent before (433.41 ± 35.14) and after (440.21 ± 29.29) the intervention (p <0.001). Conclusion: This study showed that performing job safety analysis and implementing safety procedures related to each job will decrease people's human error percent. Therefore, the implementation of safety procedures related to each job was suggested to the industry. This procedure must be obtained by careful job analysis, and an appropriate procedure should be developed using a panel of experts.

Optimization of planning radionuclide diagnostic tests in osteoscintigraphy

Purpose of the study. Determining maximum possible number of high-quality radionuclide studies by days of the generator operation.Materials and methods. We studied the factors influencing the capacity of radionuclide diagnostic tests in osteoscintigraphy (OSG) by days of service life of a 99mTc generator GT-4K. The OSG capacity, the required resource of 99mTc and its efficiency in OSG were calculated. The optimal days for the generator delivery were determined.The Pirfotech 99mTc radiopharmaceutical (RFP) prepared with the generator was used for OSG.Scanning, data collection and processing of results were carried out on gamma-cameras of the systems Signature Series, Symbia T16 Siemens, and syngo M1 Applications VB10 Siemens.Parameters of the radioactivity of 99mTc were processed by mathematical methods using the Excel program.Results. We revealed specific factors influencing the OSG capacity: 1) three-hour accumulation of RFP after its administration to the patient; 2) generator activity by days of its operation; 3) the day of the week of the generator delivery. We calculated quantitative indicators of the maximum possible OSG capacity during the generator operation (maximum number of OSG procedures by days of operation, total capacity, preferred day of the week for the generator delivery).Conclusion. The most significant factors in optimal OSG planning by days of the generator operation are the generator specifications, quantity and frequency of generator supply, provision of gamma-cameras.The described technique for scheduling diagnostic procedures can be useful for the efficient use of the generator system which ensures the maximum amount of high-quality RFP from the generator eluate and contributes to the objectification of the cancer process in order to choose the treatment tactics.

Virtual Visualization of Generator Operation Condition through Generator Capability Curve

Besides achieving an optimal scheduling generator, the operation safety of the generator itself needs to be focused on. The development of the virtual visualization of a generator capability curve simulation to visualize the operation condition of a generator is proposed in this paper. In this paper, a neural network is applied to redraw the original generator’s capability curve. The virtual visualization of a generator’s capability curve can simulate the generator’s operating condition considering the limitation of the constraints on the various elements of the generator. Furthermore, it is able to show the various possibilities that occur in the operation of a generator in reality, and it can even simulate special conditions which are based on various conditions.