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Modeling Exposure Risk Dynamics in Fertilizer Production Plants Using Multi-Parameter Surveillance Frameworks
Subject area: Science,Engineering and Technology · Area of research: Multi-Parameter Surveillance Frameworks
Abstract
Modeling exposure risk dynamics in fertilizer production plants is critical for ensuring worker safety and operational sustainability, given the inherent hazards associated with the handling of chemicals such as ammonia, urea, and nitrates. This study proposes a comprehensive multi-parameter surveillance framework that integrates real-time sensor data, environmental monitoring, and human activity patterns to assess and predict exposure risk with high spatial and temporal resolution. The framework leverages data from gas detectors, temperature and humidity sensors, ventilation systems, and wearable biometric devices to quantify risk factors dynamically across various zones of the production facility. A hybrid modeling approach combining statistical analysis, machine learning, and systems dynamics is employed to simulate exposure scenarios and identify high-risk conditions. Key variables include chemical concentration thresholds, duration of exposure, frequency of worker presence, and ventilation efficacy. These variables are modeled using time-series forecasting and anomaly detection to pinpoint potential risk escalation in near real-time. The framework also incorporates historical incident data and maintenance logs to improve predictive accuracy and to contextualize sensor outputs within broader operational trends. Visualization tools enable safety managers to monitor risk zones, receive automated alerts, and evaluate mitigation effectiveness post-intervention. Case studies from operational fertilizer plants demonstrate the model's capacity to reduce exposure incidents through proactive interventions and informed decision-making. The proposed surveillance framework enhances traditional occupational health assessments by providing a dynamic, data-driven approach to risk management in high-hazard industrial environments. Ultimately, this model supports a shift from reactive to preventive safety practices, enabling timely responses to emerging hazards and fostering a culture of continuous improvement in industrial hygiene. The methodology outlined here is scalable and adaptable, offering significant potential for application in other process-intensive industries beyond fertilizer manufacturing.
Keywords
Modeling, Exposure risk dynamics, Fertilizer, Production plants, Multi-parameter surveillance, Frameworks
References
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How to cite this paper
@article{1709262,
author = {Cynthia Obianuju Ozobu},
title = {Modeling Exposure Risk Dynamics in Fertilizer Production Plants Using Multi-Parameter Surveillance Frameworks},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {2},
pages = {227-239},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709262.pdf},
abstract = {Modeling exposure risk dynamics in fertilizer production plants is critical for ensuring worker safety and operational sustainability, given the inherent hazards associated with the handling of chemicals such as ammonia, urea, and nitrates. This study proposes a comprehensive multi-parameter surveillance framework that integrates real-time sensor data, environmental monitoring, and human activity patterns to assess and predict exposure risk with high spatial and temporal resolution. The framework leverages data from gas detectors, temperature and humidity sensors, ventilation systems, and wearable biometric devices to quantify risk factors dynamically across various zones of the production facility. A hybrid modeling approach combining statistical analysis, machine learning, and systems dynamics is employed to simulate exposure scenarios and identify high-risk conditions. Key variables include chemical concentration thresholds, duration of exposure, frequency of worker presence, and ventilation efficacy. These variables are modeled using time-series forecasting and anomaly detection to pinpoint potential risk escalation in near real-time. The framework also incorporates historical incident data and maintenance logs to improve predictive accuracy and to contextualize sensor outputs within broader operational trends. Visualization tools enable safety managers to monitor risk zones, receive automated alerts, and evaluate mitigation effectiveness post-intervention. Case studies from operational fertilizer plants demonstrate the model's capacity to reduce exposure incidents through proactive interventions and informed decision-making. The proposed surveillance framework enhances traditional occupational health assessments by providing a dynamic, data-driven approach to risk management in high-hazard industrial environments. Ultimately, this model supports a shift from reactive to preventive safety practices, enabling timely responses to emerging hazards and fostering a culture of continuous improvement in industrial hygiene. The methodology outlined here is scalable and adaptable, offering significant potential for application in other process-intensive industries beyond fertilizer manufacturing.},
keywords = {Modeling, Exposure risk dynamics, Fertilizer, Production plants, Multi-parameter surveillance, Frameworks},
month = {August},
}