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Industrial Safety and Occupational Health Engineering
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Industrial Safety and Occupational Health Engineering

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1481

Special Issue Editor

Dr. Ming Li

E-Mail Website
Guest Editor
Associate Professor, School of Resource & Safety Engineering, Central South University, Changsha 410083, China
Interests: dust control; mine ventilation; emergency management

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Industrial Safety and Occupational Health Engineering.

Industrial Safety and Occupational Health are central issues in current research on safety production, spanning industries such as mining, metallurgy, petrochemicals, construction, and transportation. The Special Issue focuses on academic exchanges in areas such as ventilation and dust prevention, fire and heat hazard prevention, occupational hazards prevention, industrial waste disposal, safety information and intelligence, risk assessment, and emergency management. It aims to enhance the level and capacity of safety production by addressing these factors of occupational hazards, environmental hazards and accident risk in production activities.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in Industrial Safety and Occupational Health Engineering. Both theoretical and experimental studies and comprehensive reviews and survey papers are welcome.

Dr. Ming Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • health engineering
  • industrial safety
  • dust prevention

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Published Papers (2 papers)

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Research

16 pages, 2365 KiB  
Article
Natural Language Processing Risk Assessment Application Developed for Marble Quarries
by Hasan Eker
Appl. Sci. 2024, 14(19), 9045; https://doi.org/10.3390/app14199045 - 7 Oct 2024
Viewed by 567
Abstract
In this study, by using the texts describing the hazards and precautions taken during text mining, the necessary processes were carried out to first estimate the probability value and severity value of the risk and then calculate the risk values by Natural Language [...] Read more.
In this study, by using the texts describing the hazards and precautions taken during text mining, the necessary processes were carried out to first estimate the probability value and severity value of the risk and then calculate the risk values by Natural Language Processing analysis. In order to be used within the scope of the study, two data sets were generated from the data in the risk assessment report prepared by applying the L-type matrix risk assessment in marble quarries between 2015 and 2021. Stochastic Gradient Descent (SGD) was used for classification and prediction by analyzing text data. One data set was used to analyze the probability value of the risk and the other was used to analyze the severity value of the risk. In light of the results, when a text containing hazard and precaution information was entered, a system was developed that analyzed this text, estimated the probability and severity values, and calculated the risk assessment score. The application of the SGD algorithm to learning models developed on text data yielded an accuracy rate of 91.2% in the risk probability data set and 97.5% in the risk severity data set. The results indicated that the models were capable of conducting automatic risk assessment on text data and of effectively predicting the requisite probability and severity values. Due to the high accuracy rates obtained during the study, this risk assessment software was recommended for use in marble quarries. Full article
(This article belongs to the Special Issue Industrial Safety and Occupational Health Engineering)
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14 pages, 4703 KiB  
Article
Research on Intelligent Ventilation System of Metal Mine Based on Real-Time Sensing Airflow Parameters with a Global Scheme
by Yin Chen, Zijun Li, Xin Liu, Wenxuan Tang, Qilong Zhang, Haining Wang and Wei Huang
Appl. Sci. 2024, 14(17), 7602; https://doi.org/10.3390/app14177602 - 28 Aug 2024
Viewed by 544
Abstract
In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation [...] Read more.
In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety. Full article
(This article belongs to the Special Issue Industrial Safety and Occupational Health Engineering)
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