Assessment of pulmonary function and respiratory symptoms among INDIAN textile sizing mill workers.

BACKGROUND: Textile-sizing mill workers are exposed to various hazards in the sizing units during their working hours and are at risk of acquiring lung impairments due to the usage of sizing chemicals in the sizing process. OBJECTIVE: The main aim of this study is to assess the influence of cotton dust and sizing agents on lung function and breathing difficulties among Indian textile sizing mill workers. METHODS: This cross-sectional study was carried out at a textile-sizing mill from August 2022 to September 2022. A modified questionnaire based American Thoracic Society's standard was used to assess respiratory symptoms among sizing mill workers and the pulmonary function test was conducted Spirometry. The chi-square test was used to find the difference between respiratory symptoms and the t-test was used to find the difference between spirometric parameters. RESULTS: Textile sizing mill workers showed significant (P <  0.0001) decline in peak expiratory flow rate, forced vital capacity (FVC), ratio of FEV1 and forced vital capacity, and forced expiratory volume in 1 s (FEV1). There was an association between symptoms and duration of exposure to pulmonary abnormality. Sizing mill workers showed a significant decline in lung functions and an increase in pulmonary symptoms. As the service duration of exposure in terms of years increased, respiratory symptoms increased and spirometric abnormality also increased. CONCLUSION: This study confirms that sizing agents such as polyvinyl alcohol (PVA), emulsifier, wax, carboxymethyl cellulose (CMC), and starch used in sizing mills are also responsible for respiratory illness and lung impairment among textile workers.

Experimental analysis and parameter optimization on the reduction of NOx from diesel engine using RSM and ANN Model.

The major emission sources of NOX are from automobiles, trucks, and various non-road vehicles, power plants, coal fired boilers, cement kilns, turbines, etc. Plasma reactor technology is widely used in gas conversion applications, such as NOx conversion into useful chemical by-product. Among the plasma treatment techniques, nonthermal plasma (NTP) is widely used because it does not cause any damage to the surfaces of the reacting chamber. In this proposed work, the feasibility of Dielectric Barrier Discharge (DBD) reactor-based nonthermal plasma (NTP) process is examined based on four operating parameters including NOx concentration (300-400 ppm), gas flow rate (2-6 lpm), applied plasma voltage (20-30 kVpp), and electrode gap (3-5 mm) for removing NOx gas from diesel engine exhaust. Optimization of NTP process parameters has been carried out using response surface-based Box-Behnken design (BBD) method and artificial neural network (ANN) method and compared with the performance measures such as R2, MSE (mean square error), RMSE (root mean square error), and MAPE (mean absolute percentage error). Two kinds of analysis were carried out based on (1) NOx removal efficiency and (2) energy efficiency. Based on the simulation studies carried out for Nox removal efficiency, the RSM methodology produces the performance measures, 0.98 for R2, 1.274 for MSE, 1.128 for RMSE, and 2.053 for MAPE, and for ANN analysis method, 0.99 for R2, 2.167 for MSE, 1.472 for RMSE, and 1.276 for MAPE. These results shows that ANN method is having enhanced performance measures. For the second case, based on the energy efficiency study, the R2, MSE, RMSE, and MAPE values from the RSM model are 0.97, 2.230, 1.493, and 2.903 respectively. Similarly based on ANN model, the R2, MSE, RMSE, and MAPE values are 0.99, 0.246, 0.46, and 0.615, respectively. From the performance measures, it is found that the ANN model is accurate than the RSM model in predicting the NOx removal/reduction and efficiency. These models demonstrate that they have strong agreement with the experimental results. The experimental results are indicated that optimum conditions arrived based on the RSM model resulted in a maximum NOx reduction of 60.5% and an energy efficiency of 66.24 g/J. The comparison between the two models confirmed the findings, whereas this ANN model displayed a stronger correlation to the experimental evidence.