No abstract

Condensable particulate matter (CPM) belongs to PM25. In addition, it is one of PM2.5 main contributors. But CPM emissions are often overlooked in coal-fired power plants. As a result, this paper studies the removal characteristics of (CPM) from air pollution control devices (APCDs) in a Chinese ultra-low emission coal-fired generating unit. The quality and content of flue gas particles, including CPM and filterable particulate matter (FPM), were measured at four points: the selective catalytic reduction (SCR) input, air preheater (APH) inlet, electric precipitator (ESP) outlet, and wet flue gas desulphurization (WFGD) outlet. The removal characteristics of each APCD were studied. Within the scope of this study, it is found that: ® CPM emission concentration accounts for 94.9% of total particle emission concentration, and is higher than the ultra-low particulate matter outflow guidelines for coal-fired power plants in China; ©WFGD has the highest CPM inorganic component removal radio of 97.02%, and ESP has the highest CPM organic component removal radio of 58.46%, among APCDs; ©SO4 2−, NH4 +, F−, and Cl− are the most abundant water-soluble ions in CPM.

Based on the operational data of the circulating water system in a thermal power plant, BP neural network and support vector machine regression were used to establish the prediction model of circulation water system evaporation and wind blow loss. The trail method was used to improve the BP neural network prediction model, and for the prediction model of support vector machine regression, the kernel function and the corresponding parameters were selected through optimization. The results showed that the mean square error of the simulation results of the two models were 0.071 and 0.070 respectively in summer and 0.046 and 0.047 respectively in winter, this meets the prediction requirements of project and demonstrates high prediction accuracy. With the evaluation index of neural network model, the simulation and prediction results of the two models were compared and analysed. The results showed that the simulation results of two model were basically the same, but the support vector machine model training sample time is shorter, the convergence speed is faster, and the overall network model performance is better.

Take PERC P-type monocrystalline silicon and PERC P-type polycrystalline black silicon modules as examples to establish a life cycle carbon emission inventory of crystalline silicon modules. Study the contribution of five recycling technologies to the life cycle carbon emission reduction of two crystalline silicon modules, and the impact of the life cycle of 1MWP PERC P-type monocrystalline silicon and PERC P-type polycrystalline black silicon module systems on the environment. The results show that the carbon emissions of the two photovoltaic systems are concentrated in the production stage, and the carbon emissions of the crystalline silicon purification process are the highest, accounting for more than 41%. Through green low-carbon recycling technology to process waste photovoltaic modules to obtain glass, aluminum, silicon and other materials to achieve resource recycling, it can reduce the energy consumption of the module production process and life cycle carbon emissions. According to calculations, 1 MWp PERC P-type monocrystalline silicon system has 10% higher life cycle carbon emissions than PERC P-type polycrystalline black silicon system.