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The chemical industrial park is an important source of volatile organic compounds (VOCs) emissions which is difficult to monitor due to its diversity and low concentration. In this work, a hybrid sensor array consisting of six gas sensors (four semiconductor sensors, one electrochemical sensor, and one photo ionization detector) was constructed to measure VOCs. Three most frequently detected VOCs in a Chinese fine chemical industrial park, namely toluene, dichloromethane, ethyl acetate, and their mixtures, were tested all below 5 ppm (even below 1 ppm in many cases). Support vector machine (SVM) and back-propagation artificial neural network (BP-ANN) based on different input features, including steady-state responses (SS), the first four components preprocessed by principal component analysis (PCA) and linear discriminant analysis (LDA), were trained and evaluated. The 10-fold cross validation results indicate that SS-SVM and SS-ANN are two best models with accuracy and $\text{R}^{{2}}$ of about 94% and 0.95 for the validation set, respectively. Compared with LDA, PCA is more suitable for dimension reduction in this study, since the performance of models with the first 4 PCs are closer to that of models with SS. The results demonstrate that the proposed hybrid sensor array system with SVM and ANN models is able to identify and quantify low-concentration VOCs mixtures. This method is promising to be used for low-cost and online monitoring of VOCs emissions in chemical industrial parks. [ABSTRACT FROM AUTHOR]

Aiming at enhanced particulate matter (PM) removal of electrostatic precipitators (ESPs) in coal-fired power plants, a pulse energization power supply that generates high-voltage pulses superimposed on a high-voltage dc was designed. The integrated theories of corona discharge, particle charging, and removal were presented, and the pulse energization strategy was studied through fractional particle removal simulation. The conclusion of simulation stated that pulse energization could significantly improve particle charge with a maximum enhancement at a particle diameter of $20~\mu \text{m}$ , while the fractional particle removal efficiency shows a different distribution that removal of particles less than $2.5~\mu \text{m}$ had the most significant enhancement. Based on the design and simulations, a full-scale comparative operation study testified the potential of dc + pulse energization to enhance particle removal with a limited power consumption increase. Pulse energization was applied to different industrial ESPs with various design and operation parameters and shown multicondition adaptability in PM removal enhancement, with the emission reduction ranging from 45.48% to 58.00% and outlet concentration from 8.60 to 47.48 mg/Nm3, proving it a convenient and economical method in the performance boost of industrial ESPs. [ABSTRACT FROM AUTHOR]

This paper investigated the enhancement of plasma on dimethyl sulfide (DMS) removal over commercial V-W/Ti catalysts. The effects of catalyst composition, discharge power, and gas temperature on DMS removal in the plasma-based system were analyzed. The results showed that the V0.9–W/Ti catalyst exhibited the best performance toward DMS removal due to its highest reducibility among the employed samples. The introduction of plasma exhibited a synergistic effect on catalysts and significantly enhanced the removal of DMS over the studied temperature range, especially in high discharge power cases. The activation energy Ea of DMS removal in the presence of plasma was dramatically reduced to less than 10% of the catalysis only case, indicating different DMS removal pathways in plasma-activated catalysis system. The reaction mechanisms of DMS were also discussed based on the reaction products. [ABSTRACT FROM PUBLISHER]