Identification and removal of polycyclic aromatic hydrocarbons (PAHs) were investigated at two coke plants located in Shaoguan, Guangdong Province of China. Samples of raw coking wastewaters and wastewaters from subunits of a coke production plant were analyzed using gas chromatography-mass spectrometry (GC/MS) to provide a detailed chemical characterization of PAHs. The identification and characterization of PAH isomers was based on a positive match of mass spectral data of sample peaks with those for PAH isomers in mass spectra databases with electron impact ionization mass spectra and retention times of internal reference compounds. In total, 270 PAH compounds including numerous nitrogen, oxygen, and sulfur heteroatomic derivatives were positively identified for the first time. Quantitative analysis of target PAHs revealed that total PAH concentrations in coking wastewaters were in the range of 98.5±8.9 to 216±20.2 μg/L, with 3-4-ring PAHs as dominant compounds. Calculation of daily PAH output from four plant subunits indicated that PAHs in the coking wastewater came mainly from ammonia stripping wastewater.Coking wastewater treatment processes played an important role in removing PAHs in coking wastewater, successfully removing 92 % of the target compounds. However, 69 weakly polar compounds, including PAH isomers, were still discharged in the final effluent, producing 8.8±2.7 to 31.9±6.8 g/day of PAHs with potential toxicity to environmental waters. The study of coking wastewater herein proposed can be used to better predict improvement of coke production facilities and treatment conditions according to the identification and removal of PAHs in the coke plant as well as to assess risks associated with continuous discharge of these contaminants to receiving waters.
Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to feature selection, and each of them owned the unique advantages of optimizing feature selection algorithm. Quantum computing had a good balance ability when exploring feature sets between global and local searches. Grey wolf optimizer could effectively explore all possible feature subsets, and uncertain symmetry rough set theory could accurately evaluate the correlation of potential feature subsets. QCGWORS intelligent algorithm could minimize the number of features while maximizing classification performance. In the experimental stage, k nearest neighbors (KNN) classifier and random forest (RF) classifier guided the machine learning process of the proposed algorithm, and 13 datasets were compared for testing experiments. Experimental results showed that compared with other feature selection methods, QCGWORS improved the classification accuracy on 12 datasets, among which the best accuracy was increased by 20.91%. In attribute reduction, each dataset had a benefit of the reduction effect of the minimum feature number.
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