Simultaneous removal of NO and SO2 with a micro-bubble gas-liquid dispersion system based on air/H2O2/Na2S2O8

Comparative study on direct and indirect methods for wet desulphurisation and denitrification based on micro-nano bubbles

The wet desulphurisation and denitrification technique based on micro-nano bubbles, which is available by either D-method or I-method, is a promising novel process. By employing piped water, Na2SO3 aqueous solution and HA-Na aqueous solution as the absorption liquids, a comparative study was conducted in this article on D-method and I-method to analyze their performance, advantages and disadvantages. It was accompanied by an investigation of how initial pH and initial temperature values of the absorption liquids affected the removal efficiency. The results suggested a positive correlation between NO/SO2 removal efficiencies and pH values but a little improvement in the removal efficiency under alkaline conditions. Furthermore, heating the absorption liquids inhibited the removal of NO and SO2. When manipulated in the same experimental environment, D-method and I-method did not present a significant difference in the SO2 removal efficiency, while the former was remarkably more effective than the latter in removing NO. To put together, D-method had higher removal efficiency, but required a large-scale micro-nano bubble generator to process a large quantity of flue gas as the micro-nano bubble generator was subject to a limited inlet flow rate. Consequently, an increase in investment and operating costs was incurred, while this issue could be avoided by I-method.

Modeling of NO mass transfer characteristics absorbed in sodium persulfate solution with a bubble reactor

Sodium persulfate solution is considered as an effective wet denitrification medium, however, it is unclear that the influence of the operating conditions on mass transfer characteristics parameters during the absorption of NO with sodium persulfate solution. To determine the key mass transfer characteristics parameters, the specific interfacial area a and the mass transfer coefficients kL,  kG, were determined based on the Danckwerts method during CO₂ absorption in a bubble column. kL, kG  and a were calculated by correlations between the mass transfer coefficients of NO and CO₂. Results showed that the specific interfacial area increased 77.64 m^-1, the liquid phase mass transfer coefficient increased 2.49 × 10^-4 m·s^-1, and the gas phase mass transfer coefficient increased 0.71 × 10^-5mol·Pa^-1·s^-1·m^-2 with superficial gas velocity increasing from 0.6 to 1.4 L·min^-1. With the temperature of sodium persulfate solution increasing from 293 to 333 K, the specific interfacial area decreased 42.66 m^-1, while the liquid phase mass transfer coefficient and the gas phase mass transfer coefficient increased 3.89 × 10^-4 m·s^-1 and 1.18 × 10^-5mol·Pa^-1·s^-1·m^-2, respectively. The experiments results determined the correlations of a, kL, and kG  with the temperature of the absorption phase and the superficial velocity of the gas. It can serve as a guide to the enhancement of the sodium persulfate wet denitrification process.

Paradox of 'ideal correlations': improved model for air half-life of persistent organic pollutants

The persistence of organic pollutants is an important environmental property due to the extended possibility to have an impact of corresponding substances. In many cases, the experimental values of the thousands of contaminants are missing. The object of the study is novel computational modelling for air pollutions. Quantitative structure-property relationship (QSPR) for air half-life has been built using the Monte Carlo method with applying the index of ideality of correlation (IIC). The basis of the predictive model of air half-life is the representation of the molecular structure by simplifying molecular input-line entry system (SMILES) and numerical data on the above endpoint (expressed by hours) converted to a decimal logarithm. The statistical quality of the model has been checked up with different validation metrics and is quite good. Paradoxically, the improvement of the statistical quality via the IIC for the validation set is done in detriment to the training set. The new model has performed better than those obtained previously on the same set of compounds, for the prediction of new compounds in the validation set. Some semi-quantitative indicators for the mechanistic interpretation of the model are suggested.