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EN
Purpose: Determination of such an interval of workplace environmental physical factors control, which would ensure high monitoring reliability and the minimum data collection and processing duration (or cost). Design/methodology/approach: To achieve the goal were applied: analysis and synthesis of known scientific results on the topic of research, statistical analysis, mathematical modelling. Statistical data for determining the interval of control was recorded at regular intervals. Findings: A methodology has been developed for determining the interval of workplace environmental physical factors values control. It is based on the identification of patterns of change in the physical factors values. The algorithm of workplace environmental physical factors values control is proposed, which helps to identify cases when the actual values of the factors exceed the limit values. The practical application of theoretical propositions showed that the correlation coefficient between the factual sample and the sample formed using the determined control interval is within 0.74…0.88, which satisfies the condition R > 0.5 as intended. Research limitations/implications: The mechanism for workplace environmental physical factors values monitoring was further developed on the basis of forecasting changes in the physical factors values and determining the duration of the excess of the factors values over the limit. In this study on stationary and conditional stationary processes was the focus. Practical implications: The use of the algorithm that is based on the methodology for determining the interval of workplace environmental physical factors values control contributes to more effective monitoring of working safety. Originality/value: For the first time justified by the choice of the control interval of workplace environmental physical factors values with acceptable accuracy of the forecast that allow to quickly establish working conditions hazard class.
EN
Purpose: To develop a mathematical model for predicting the workplace environmental physical factors values. Design/methodology/approach: Experimental measurements of the harmful and dangerous physical factors values of workplace environmental were carried out using special certified equipments. For each physical factor, 200 measurements were carried out. The workplace choice is justified by the employees’ survey and specialists’ expert evaluation results. Prediction methods that can be used to predict the workplace environmental physical factors values have been analyzed analytically. Working conditions assessment was carried out in accordance with the classification of working conditions for workplace harmfulness and danger, which function in Ukraine. Findings: For a preliminary assessment of the impact of environmental physical factors on workers, it is proposed to use the strict ranking method. It has been established that the proposed mathematical models for predicting the workplace environmental physical factors values (noise, dust, vibration, relative humidity) have an accuracy of more than 90% and can be used for planning measures to working conditions improve. Research limitations/implications: The results of a study of modelling and forecasting the workplace environmental physical factors values at the enterprise for the manufacture of glass and glass products at workplaces of transportation, preparation and mixing of materials are shown. Mathematical models for four physical factors are presented: noise, dustiness of air, vibration, relative humidity. Practical implications: Mathematical models make it possible to predict the environmental physical factors values (noise, vibration, dust, humidity) taking into account the specifics of the production process, assess the hazard class and harmfulness of working conditions at workplaces and justify the measures at labour protection. Originality/value: For the first time proposed by the mathematical models for predict the environmental physical factors values (noise, vibration, dust, humidity) taking into account the specifics of the production process.
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