Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach. (1st July 2022)

• Record Type: Journal Article
• Authors: Nallathambi, Indumathi; Ramar, Ramalakshmi; Pustokhin, Denis A.; Pustokhina, Irina V.; Sharma, Dilip Kumar; Sengan, Sudhakar
• Is Part Of: Environmental pollution. Volume 304(2022)
• Publication Date: 2022-07-01
• Subjects: Fireworks industry -- Atmospheric conditions -- Artificial neural network -- Feed forward back propagation -- Hidden layer; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2022.119182
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009–2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions. Highlights: The chemical composition is used for cracker manufacturing in firework industries. Chemicals reacting with atmospheric conditions lead to an accident. Chemical composition and atmospheric based firework accident prediction is proposed. Back Propagation Neural Network is implemented through hidden layer 5 and 10. Hidden layer 5 is providing high performance accuracy on accidental predictions.