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.

Optimization of incorporating κ-Carrageenan-based gels on improving cloud stability, physical stability, and viscosity of ready-to-drink mango juice.

κ-Carrageenan gels were explored for improving the stability of ready-to-drink (RTD) mango juice.RTD mango juice with an acidity of 0.3% and a Brix of 18° was prepared. Two gels, bi-gel and hydrogel, were incorporated in RTD mango juice to study the effect of gel dosage, resting time, and homogenizing time on selected attributes (cloud and physical stability, and viscosity), determined using second-order Box-Behnken design, in combination with response surface methodology. The coefficient of determination values for all models was found to be higher than 90%. The fluid behavior of RTD mango juice after the addition of gels tends to fit Herschel-Bulkley's model. The behavior of RTD mango juice's fluid was found to change from shear thickening to shear thinning after the addition of gels. For hydrogel-based RTD mango juice, maximum cloud stability (3.012 abs), physical stability (66.49%) with minimum viscosity (4120 cP) resulted from optimized conditions of gel dosage (9 mL), resting time (1 h), and homogenizing time (33 s). For RTD mango juice, hydrogel can be preferred over bi-gel due to its melt-in-your-mouth sensation with high physical and cloud stability. PRACTICAL APPLICATION: Ready-to-drink mango juice is consumed by a large number of people worldwide. However, an increase in the storage period causes coagulation of the pulp particles, resulting in undesired distinct layers of pulp and water content. In this study, κ-Carrageenan gels were added to RTD mango juices to avoid such separation and improve cloud and physical stability. The findings from this study might serve as a roadmap for developing high-quality, stable RTD products.