RESUMO
Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.
RESUMO
The fine chemical industry is currently facing challenges in energy saving, material conservation, and pollution reduction due to the dual policy pressure of precise system management and collaborative pollution and carbon reduction. However, the interweaving of materials and energy input-output was not well understood due to the incomplete coverage and the lack of a generic framework. Therefore, a methodology based on the energy-material-pollution (E-M-P) coupling nexus was proposed to quantitatively assess multi-level coupling. According to the selected generic 32 coupling units, two representative glyphosate (PMG) production processes were taken as case studies. Quantification results showed that the solvent element and the material system had a higher priority. Moreover, Process 2 owned a greater optimization potential as the coupling relationship pairs were 2.55 compared to 2.32 for Process 1, and the correlation proportions of material systems reached 69.26 % and 56.92 %, respectively. In addition, assessment results indicated that Process 2 was more environmentally friendly because of the lower ecological indexes (9.7 GPt vs. 15.8 GPt) and weaker carbon footprint (CF) (1.16E+08 vs. 2.32E+08). Combined coupling nexus and environmental assessment organically, methanol had the most optimization potential and was beneficial for the measures such as solvent substitution. This work offered theory and practice guidance with demonstrative value to support the sustainable development of precise system management.
RESUMO
Organic modified kaolinite-urea intercalation complex (KUIC) was prepared using dimethyl sulfoxide (DMSO) as the precursor of kaolinite intercalation. Its structure was characterized by Fourier transform infrared (FTIR) and X-ray diffraction (XRD). Subsequently, as a synergistic agent, KUIC was combined with flame retardant ammonium polyphosphate (APP) to improve the flame retardant and smoke suppression performance of unsaturated polyester (UP) resin. A cone calorimeter (CONE) was used to study its flame retardancy and smoke suppression, and a scanning electron microscope (SEM) and thermogravimetry (TG) were used to study the micro morphology of the char and flame retardant mechanism. The results show that 12 phr of APP and 3 phr of KUIC were doped into UP to obtain a 28.0% limiting oxygen index (LOI) value. Compared with UP, the heat release rate and smoke production of UP/APP/KUIC composites were greatly decreased. Meanwhile, KUIC indeed enhanced the mechanical properties of UP.
