Effectiveness of Predicting The Spatial Distributions of Target Contaminants of a Coking Plant Based On Their Related Pollutants

The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction rate of the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging were 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (3) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (4) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.

Low-cost airbox network and Gaussian plume modelling to assist air quality policy-making at the local scale: Shexian County, Hebei Province, China

<p>High air pollution levels pose a threat to both human health and ecosystem vitality in Hebei Province, NE China. Although air quality changes are monitored hourly with high-end equipment at the provincial scale (197 stations for 187,693 km<sup>2</sup>) it is difficult for individual counties or cities to improve local air quality based on regional-scale information. The Sino-Dutch Technology Transfer & Training Project established a monitoring network of 43 low-cost air-boxes and 11 standard meteorological stations in Shexian county, Handan city (~ 1500 km<sup>2</sup>) to measure atmospheric concentrations of PM<sub>10</sub>, PM<sub>2.5</sub>, CO, SO<sub>2</sub>, NO<sub>2</sub> and O<sub>3</sub> at 1-min intervals from January 2020 onwards. Data from these stations were evaluated in real time using the TNO Gaussian plume model. The model provides point emission levels of PM<sub>10</sub>, PM<sub>2.5 </sub>and CO at 10-min intervals after calibration against measured concentrations. Based on a 2019 pollution source inventory, 21 major source areas were identified and used to derive an optimized source map for model input – including a large steel company, a coal-fueled power plant, different industrial complexes (cement, coking plant for ore smelting), as well as the densely populated city centre, rural residential areas, and a busy highway. The model performs source optimization using concentration data for all 43 stations and subsequently calculates the contributions of individual sources for each monitoring station to see to what extent the source map explained observed concentrations. Full network operation started in July 2020. Based on a one-month test period (August 2020), the steel company and coking plant were estimated to contribute ~25% of the total area’s PM-emissions. The central city area contributed ~10% and 17% of total PM- and CO-emissions, respectively, mostly due to construction activity and traffic. Repeating the exercise for the two provincial monitoring stations that also had high-end equipment in place in the downtown area gave inferred average urban contributions to measured concentrations as high as 60–62.5% for PM<sub>10</sub> and PM<sub>2.5</sub> versus 48% for CO. The steel factory contributed an estimated 9–11% for PM<sub>10</sub> and PM<sub>2.5</sub> at these locations and a cement factory 13% for CO. The combined results underline the importance of taking spatial variability of emission sources into explicit account in complex industrialized cities. Moreover, the combination of a low-cost airbox real-time monitoring network with emission apportionment modeling will allow local policy-makers to take proper actions towards reducing air pollution levels at the local scale.</p>

The Analysis of the Structure of the Coke oven Chamber of a Delayed Coking Unit

In this article a literature review of existing delayed coking plants is carried out, and the design features of domestic and foreign coke chambers are revealed. With the help of the final engineering analysis program Ansys, models of reaction apparatuses of these installations are constructed for various material designs. It is revealed that it is impossible to increase the efficiency of operation of the current devices without a significant change in the technological regime, and technological solutions are proposed for the design of new reactors of delayed coking plants. Models of reactors of the modern delayed coking plant and the Soviet type 21–10/700 with minimization of the number of horizontal welds are constructed, and economic calculations are made for the operation of coke chambers with various designs.