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Sulfur dioxide (SO2) is one of the most important air pollutants emitted by power plants and ironworks. To effectively remove SO2 from flue gas, wet flue gas desulfurization (WFGD) technology is widely adopted by most of China’s coal-fired power plants. However, one of the most important issues of WFGD is the relatively high energy consumption, with the main contribution from the power consumed by circulating pumps in the absorber. In this paper, hybrid modeling and real-time predictive scheduling of a wet flue gas desulfurization (WFGD) system for energy saving and life extension are proposed. The hybrid model is based on the mechanism of the SO2 absorption process and modified based on the operation data using a particle swarm optimization (PSO) algorithm. The prediction model can describe the SO2 absorption process well, and the root-mean-square error (RMSE) of the model is 2.20 mg/m3. Effects of parameters such as the inlet SO2 concentration, pH, flue gas flow rate, and combinations of circulating pumps on the outlet SO2 concentration are further explored. The optimal combination of circulating pumps under different conditions is calculated. Finally, a real-time predictive scheduling strategy is proposed and evaluated under different parameter settings. The system shows the best performance when the upper limit, the target of outlet SO2 concentration, and the lower limit are set to 30, 15, and 10 mg/m3, respectively. The energy consumption of the system is 2564.6 kW, which is 10.6% lower than that before optimization.

The electrodeposition of Ni–nano-Cr2O3 composite coatings was studied in electrolyte containing different contents of Cr2O3 nanoparticles (Cr2O3 NPs) on mild steel surfaces. Some techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness, the potentiodynamic polarization curves (Tafel) and electrochemical impedance spectroscopy (EIS) were used to compare pure Ni coatings and Ni–nano-Cr2O3 composite coatings. The results show that the incorporation of Cr2O3 NPs resulted in an increase of hardness and corrosion resistance, and the maximum microhardness of Ni-nano-Cr2O3 composite coatings reaches about 495 HV. The coatings exhibit an active-passive transition and relatively large impedance values. Moreover, the effect of Cr2O3 NPs on Ni electrocrystallization is also investigated by cyclic voltammetry (CV) and EIS spectroscopy, which demonstrates that the nature of Ni-based composite coatings changes attributes to Cr2O3 NPs by offering more nucleation sites and less charge transfer resistance.

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Abstract:In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by mathematical morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Thirdly, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, since the grey relation degree has good classified capacity for small sample pattern identification, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. It's suitable for on-line monitoring and fault diagnosis of gearbox.