Control of the workplace environment by
physical factors and SMART monitoring

Kruzhilko, O.; Polukarov, O.; Vambol, Sergij; Vambol, Viola; Khan, Nadeem Ahmad; Maystrenko, V.; Kalinchyk, Vasyl; Khan, Afzal Husain

doi:10.5604/01.3001.0014.1770

Cited by 16 publications

(8 citation statements)

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“…A study presenting the measurement of harmful and hazardous factor values of noise, air dust in the production environment, vibration, and humidity in the workplace in glass product manufacturing was shown in [18]. A set of data collected from sensors was used as an input for an algorithm for substantiating managerial decisions to minimize the negative impact of workplace physical factors.…”

Section: Related Workmentioning

confidence: 99%

A Model for Working Environment Monitoring in Smart Manufacturing

et al. 2021

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The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

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Section: Related Workmentioning

confidence: 99%

A Model for Working Environment Monitoring in Smart Manufacturing

et al. 2021

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“…It is becoming evident that the vapours emitted by the exhaust gases of cars, with their pungent smell of gas or steam, are the cause of a high percentage of accidental poisoning with toxic gases (Rouleau et al, 2013;Cobb & Etzel, 1991). Consequently, the working conditions of this category of workers require continuous monitoring, similar to research (Kruzhilko et al, 2020).…”

Section: Review Of the Consequences Of Chemical Constituents Impact Of Emissions From Vehicle On Human Healthmentioning

confidence: 99%

Social Consequences of Vehicle Exhaust: A Case Study in Poltava, Ukraine

Stepova

Hanoshenko

Serha

et al. 2021

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Intensive urbanization processes, an increase in the number of urban population and transport have led to an increase in the technogenic load on the environment, in particular on the atmospheric air. Among a significant number of sources of environmental pollution, roads and vehicles pose a significant threat. Motor vehicles cause irreparable harm to human health and the environment. Regular assessment of the quality of atmospheric air in residential areas of cities with heavy traffic to establish the level of potential risk to public health is an urgent task. The study purpose is the social consequences of transport emissions; determination of the anthropogenic load on the air and of the indicator of public health risk from the vehicle exhaust impact. The study was carried out on the example of the residential and the most loaded transport area of Poltava (Ukraine). The study demonstrates that the overwhelming majority of the urban population lives in areas where health risk indicators exceed permissible norms. Since the elderly, children and students are most often present on the street and near houses, they are at the greatest risk. The same applies to vehicle drivers who are often idle in "traffic jams". In turn, such high levels of air pollution pose even greater risks to public health, in particular to the development of respiratory diseases. In the study of the peculiarities of Ukrainian legislation in this area, it was established that the mechanism for collecting information about the quality of the environment, however, there is no requirement to inform the population about it.

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“…These days are accompanied by significant changes in the socio-economic organization of public life, which contribute to the formation of the market and market relations, rapid industrial progress 1,2 and the emergence of new risks in the workplace. 3 The transition to the active implementation of a risk-based approach is defined by the Concept of reforming the labor protection management system (OSMS) in Ukraine. The construction of such systems, the phased introduction of which will ensure that occupational risk is taken into account when planning preventive measures, provides for a set of theoretical and experimental studies aimed at scientific substantiation of the identification of hazards existing in production and the assessment of occupational risks.…”

Section: Introductionmentioning

confidence: 99%

Scientific Support of Occupational Risk Management Decisions in Industrial Sectors in Case of Uncertainty

Kruzhilko

Mahmoud

Майстренко

et al. 2023

Int J Occup Saf Health

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Introduction: One of the most important steps in risk assessment is the selection of assessment methods. Traditionally, when developing measures to reduce the level of occupational morbidity and industrial injuries, the results of an analysis of the causes, types of events and other factors that led to accidents are used. But such an approach does not meet modern requirements. For an adequate assessment of occupational risks, it is necessary to have objective data from different time periods: the onset of traumatic events in the past, the current state of threats to life and health of people, and the future state of threats in industrial sectors. Methods: Mathematical modeling remains the main means of scientific support for occupational risk management. The Elmeri system was chosen for occupational risk assessment in this study, which can be easily and quickly used in any industry and in enterprises of all sizes. A critical analysis of various approaches to managing occupational risks in enterprises was applied to identify the strengths and weaknesses of these approaches. The method of generalizing the most effective approaches to occupational risk management was applied to develop the algorithm of occupational risk management decisions in industrial sectors in case of uncertainty. Results: An occupational risk management algorithm has been developed to substantiate management decisions on planning measures to reduce risk, the implementation of which ensures the effectiveness of measures aimed at reducing risk. Research has shown that if the decision-making situation is characterized by conditions of uncertainty (it is impossible to obtain mathematical models of acceptable accuracy), the assessment of the predictive values of occupational risk is carried out exclusively by an expert. Thus, the occupational risk indicator used at the final stage of planning activities determines the degree of achievement of the result of solving the task. Conclusion: It has been established that in conditions of uncertainty (lack of necessary data or available data are incomplete or unreliable), experts involved in solving the problem of risk management use their own knowledge and experience in solving similar problems. As a promising direction for further research, it should be noted the development of a methodology for a comprehensive assessment of the effectiveness of operational management decisions for planning and implementing measures to reduce risks

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Control of the workplace environment by physical factors and SMART monitoring

Cited by 16 publications

References 0 publications

A Model for Working Environment Monitoring in Smart Manufacturing

A Model for Working Environment Monitoring in Smart Manufacturing

Social Consequences of Vehicle Exhaust: A Case Study in Poltava, Ukraine

Scientific Support of Occupational Risk Management Decisions in Industrial Sectors in Case of Uncertainty

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