When the sintering temperature was °C, the sintering time was 30 min, the initial pH of the simulated wastewater was 7, and the amount of filler was 5 g, the removal effect of the micro-electrolytic fillers prepared under the conditions of the iron-carbon ratios of 1:1, 1:1.5, 1:2, 1:2.5, and 1:3 on the methyl orange in wastewater was investigated. The experimental results are shown in Figure 5
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From Figure 5 , it can be seen that the iron-carbon ratio of the micro-electrolytic filler has a certain influence on the simulated wastewater treatment performance of the filler. When the iron-carbon ratio was 1:1 and the treatment time was 20 min, the removal rate of methyl orange reached more than 95%. When the treatment time was 30 min, the removal rate of methyl orange reached more than 99%. The treatment effect of an iron-carbon ratio of 1:1 was relatively good. The treatment effect of an iron-carbon ratio of 1:1.5 was similar to that of an iron-carbon ratio of 1:1, but the iron-carbon ratio of 1:1.5 was slightly better. When the iron-carbon ratio of the filler was reduced to 1:2, the removal rate of methyl orange reached 98.8% at 20 min, 99% at 30 min, and 99.9% at 40 min. It can be seen that its effect of removing methyl orange was excellent and significantly better than other fillers. Further reducing the iron-carbon ratio to 1:2.5 and 1:3, the methyl orange removal rate was 91.7% and 83.2% at the treatment time of 20 min, respectively. The treatment effect was worse than the filler with a 1:2 iron-carbon ratio. From the above results, it can be concluded that with the decrease in the iron-carbon ratio, the removal effect of methyl orange-simulated wastewater of fillers first became better and then worse, and the micro-electrolysis filler with an iron-carbon ratio of 1:2 had the best effect on methyl orange wastewater treatment.
Understanding the mineral phase of the iron-carbon micro-electrolytic filler is of great significance for studying its mechanism of treating simulated wastewater. The prepared micro-electrolysis filler was analyzed by XRD to further reveal its influence mechanism. The micro-electrolytic fillers prepared under five conditions of iron-carbon ratios of 1:1, 1:1.5, 1:2, 1:2.5, and 1:3 were characterized. The obtained XRD patterns are shown in Figure 6
It can be seen from the figure that the fillers with different ratios all generated the diffraction peaks of zero-valent iron at 44.52°, 65.14°, and 82.32°, and the diffraction peak of SiO2 at 26.61°, and there were no other obvious impurity peaks. It can be seen that the iron ore powder in the filler has been basically reduced to metallic iron, and Zn and Pb were removed by evaporation during roasting. When the iron-carbon ratio was reduced to 1:2.5 and 1:3, there was an obvious 002-wide peak on the XRD curve, which corresponded to the graphite peak of coal tar. The reason for the poor treatment efficiency may be due to the high carbon content of the filler, and the number of primary cells generated decreased, thus reducing the efficiency of wastewater treatment.
Fe2O3 + 3C == 3CO&#; + 2Fe
(6)
3Fe2O3 + CO == 2CO2&#; + 2Fe3O4
(7)
2Fe3O4 + 2CO == 2CO2&#; + 6FeO
(8)
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FeO + CO == CO2&#; + Fe
(9)
SEM images of iron-carbon micro-electrolytic fillers with different ratios are shown in Figure 7 . Many pores can be observed on the surface of the filler, and the boundary between iron and carbon is also very clear. After sintering, the metal iron particles produced by reduction had an oolitic structure. The filler with an iron-carbon ratio of 1:1 had a larger particle size of iron particles, but its carbon content was the lowest among all fillers. The low carbon content will affect the reduction of iron ore. Therefore, it can be seen from the figure that the number of iron particles on the surface was obviously small, and there were many iron minerals that have not been reduced. It can be seen from the electron microscope image of the filler with the iron-carbon ratio of 1:2 that it had more iron particles that have been reduced, and the iron particles and the carbon matrix were evenly distributed, and the reduction degree was relatively high. The reaction chemical formula is as follows:
Therefore, in the process of treating wastewater, the filler with an iron-carbon ratio of 1:2 had a better treatment effect.
2.1
&#;Activity ratesIn this study, we developed a unit- and technology-based methodology for SO2, NOx, CO, CO2, PM2.5, and PM10 emissions in the cement industry for the &#; period. We calculated only the direct emissions from cement production; indirect emissions such as fuel use in the power plants due to electricity consumption and fuel use by vehicles for material transportation were not included.
Cement production involves a series of complex processes, including three basic stages: raw material preparation, clinker calcination, and cement grinding (Cao et al., ). CO, SO2, and NOx are only emitted from fuel combustion during the clinker calcination process; thus, we estimated the emissions of these pollutants by the amount of coal consumed in the cement kilns, and the coal use was calculated as the product of clinker production and annual energy intensity for the clinker production process. CO2 is primarily emitted from two sources: fuel combustion and the calcination of calcium carbonates which we treated separately in the emission calculation. The emission of PM is more complex, involving the entire process of cement production, including both organized and fugitive emissions. Following our previous study, we applied a similar model framework with a dynamic methodology to consider the transition of various PM control technologies in different cement kilns under a series of emission standards and control policies (Lei et al., a, b). The equations used to calculate various pollutants are summarized in Table 1.
Detailed unit-level data from to were obtained from the China Ministry of Ecology and Environment (unpublished data, hereafter referred to as the MEE database), including clinker and cement production, production capacity, operating and retiring dates, PM and NOx control technologies, and the coordinates of each unit. Overall, the database consists of clinker production lines and cement grinding stations of which 665 clinker production lines and 783 cement grinding stations have been retired since . Based on the MEE database for &#;, we derived the unit-level activity rates for the period &#; with a combination of data from statistics and the literature. We first calculated the provincial clinker and cement output from the existing data sources and then distributed the yearly provincial output among the cement production lines in each province by considering the age, kiln type, and capacity of each production line. In detail, we obtained the national and provincial cement output during &#; from China Statistical Yearbook (National Bureau of Statistics, &#;a) and China Industry Economy Statistical Yearbook (National Bureau of Statistics, &#;b) and collected the national (&#;) and provincial (&#;) clinker output from China Cement Almanac (China Cement Association, &#;). Additional data on provincial clinker output for some distinct years (such as , and ) before were obtained from China Industry Economy Statistical Yearbook (National Bureau of Statistics, &#;b). The data on national clinker to cement ratio during &#; were adopted from the literature (Xu et al., , ; Gao et al., ). To derive the clinker output for the early years on a national scale, we calculated the clinker output as the product of clinker to cement ratio and the cement output for the years of &#;. On a provincial scale, we derived the clinker to cement ratio for each year of &#; based on a linear interpolation with the available year-specific provincial clinker to cement ratio from statistics and calculated the provincial clinker output as the product of provincial clinker to cement ratio and the provincial cement output using the national clinker output as a constrain. Therefore, in the emission database, the data on national and provincial clinker and cement output are consistent with existing data from statistics and the literature, but unit-level activity prior to is more uncertain because it is extrapolated based on the information of the age, kiln type, and capacity of each production line.
The energy efficiency of clinker production in China's cement industry has improved markedly over the past 25 years. The average energy intensity of clinker production has decreased from 5.41&#;GJ&#;t&#;clinker&#;1 in to 3.73&#;GJ&#;t&#;clinker&#;1 in (National Bureau of Statistics, ). The historical energy intensities of different kiln types were not available from statistics but have been reported in several studies (Lei et al., a; Xu et al., ; Shen et al., ; Zhang et al., ; Hua et al., ). Originally, such information in a certain year was reported by the authority or research institutes, such as National Development and Reform Commission and China Academy of Building Research, and then was interpolated between years or averaged among different studies to derive the historical trend. There were discrepancies in the historical energy intensities because the data sources and calculation methods were varied among different studies. For example, Lei et al. (a) estimated the average coal intensity of precalciner kilns in was 4.07&#;GJ&#;t&#;clinker&#;1, whereas it was 3.66&#;GJ&#;t&#;clinker&#;1 from the estimation of Xu et al. (). To avoid the bias introduced by one particular study, we collected all the available data and generated a linear regression between the logarithm of energy intensity (GJ&#;t&#;clinker&#;1) and time in years to predict the energy intensity in each year (Fig. 1), which enabled the calculation of coal consumption for each production line. According to the model regression, the energy efficiency of precalciner kilns (PCs) is distinctly higher than that of shaft kilns (SKs) and the other rotary kilns (ORs). For example, the average energy intensity of PC, SK, and OR kilns in was 3.39, 4.21, and 4.84&#;MJ&#;t&#;clinker&#;1, respectively. Besides the linear model, we tried the nonlinear regression with the generalized additive model (GAM) as a sensitivity test and finally decided to present the results by linear regression since there were no significant differences between the two models and the linear regression has simple explicit expressions. The details on the comparison are discussed in the Supplement.
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