Dielectric barrier discharge (DBD) plasma induced flavonoid degradation kinetics and mechanism in water

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

The major emission sources of NO_X 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.

Degradation of different pesticides in water by microplasma: the roles of individual radicals and degradation pathways

Pesticides are emergent toxins often identified in aquatic environments. In the present study, microplasma was employed to reduce the pesticide content in water. The degradation efficacy, rate, and pathways of standard organophosphorus pesticides (namely, chlorpyrifos, chlorpyrifos oxone, and diazinone) and an organochlorine pesticide (namely, DDT solution) were evaluated using microplasma. High-performance liquid chromatography (HPLC) analysis was performed to elucidate the degradation efficiency of pesticides as a function of plasma-produced substances that originally contributed to the main reduction procedure. Microplasma produces several types of radicals or reactive substances, for instance dissolved ozone (O₃), nitrogen oxides, hydroxyl radicals (OH radicals), and hydrogen peroxide (H₂O₂). The removal potential differs due to the existence or absence of varieties of plasma-produced substances. The functions of major plasma-produced species on pesticide removal were determined by a passive technique. Nitrogen oxides showed a key role in organophosphorus pesticide removal, whereas dissolved ozone and OH radicals played major roles in DDT degradation. HPLC data showed that plasma-induced pesticide removal showed first-order reaction kinetics. The pesticide removal pathways through microplasma were validated by investigating the achieved data from LC-MS and GC-MS.