Classification and comparison of municipal solid waste based on thermochemical characteristics

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1 Journal of the Air & Waste Management Association ISSN: (Print) (Online) Journal homepage: Classification and comparison of municipal solid waste based on thermochemical characteristics Hui Zhou, Aihong Meng, Yanqiu Long, Qinghai Li & Yanguo Zhang To cite this article: Hui Zhou, Aihong Meng, Yanqiu Long, Qinghai Li & Yanguo Zhang (2014) Classification and comparison of municipal solid waste based on thermochemical characteristics, Journal of the Air & Waste Management Association, 64:5, , DOI: / To link to this article: Accepted author version posted online: 07 Mar Published online: 07 Mar Submit your article to this journal Article views: 1317 View related articles View Crossmark data Citing articles: 12 View citing articles Full Terms & Conditions of access and use can be found at

2 TECHNICAL PAPER Classification and comparison of municipal solid waste based on thermochemical characteristics Hui Zhou, 1 Aihong Meng, 1,2 Yanqiu Long, 1 Qinghai Li, 1 and Yanguo Zhang 1, 1 Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Thermal Engineering, Tsinghua University, Beijing, China 2 School of Environment, Tsinghua University, Beijing, China Please address correspondence to: Yanguo Zhang, Department of Thermal Engineering, Tsinghua University, Beijing , China; zhangyg@tsinghua.edu.cn Municipal solid waste (MSW) has been normally sorted into six categories, namely, food residue, wood waste, paper, textiles, plastics, and rubber. In each category, materials could be classified further into subgroups. Based on proximate and ultimate analysis and heating value, statistical methods such as analysis of variance (ANOVA) and cluster analysis were applied to analyze the characteristics of MSW in every subgroup and to try to distinguish their relative properties. The chemical characteristics analysis of MSW showed that polyethylene (PE), polypropylene (PP), and polystyrene (PS) had the highest volatile matter content, with almost no ash and fixed carbon, while polyethylene terephthalate (PET) had high carbon content but low hydrogen content. Bones and vegetables had the highest ash content, while nutshells and rubber had the highest fixed carbon content. Paper and starch food had the highest oxygen content, and wool and bones had the highest nitrogen and sulfur content. Polyvinyl chloride (PVC) had the highest chlorine content at about 55%. PE, PP, and PS had the highest heating value, followed by chemical products such as rubber and chemical fiber. Conversely, paper, vegetables and bones had the lowest heating value. The results of cluster analysis of MSW components showed that fruit peel, weeds, wood, bamboo, leaves and nutshells could be classified as the lignocellulose category; starch food, cotton, toilet paper, printing paper and cardboard could be classified as the glucose monomer category; wood and chemical fiber could be classified as the high nitrogen and sulfur category; and PE, PP, and PS could be cluster as the polyolefin category. Implications: The yield of municipal solid waste (MSW) is constantly increasing and waste to energy (WTE) has been used extensively all over the world. During the processes of incineration, pyrolysis, or gasification, the impact of physical and chemical properties of MSW is of great significance. However, the traditional classification of MSW is too general to provide more detailed information in many investigations. It is necessary to perform the investigation of characteristics of combustible MSW to distinguish different categories of MSW and find out their subclassification. Introduction Municipal solid waste (MSW) generated in China has grown from million tons in 1996 to million tons in 2011 (National Bureau of Statistics of China [NBSC], 2012). Severe environmental problems will occur if the MSW cannot be disposed of properly (Tai et al., 2011). The traditional landfill method is facing a land shortage crisis (Dong et al., 2003), and waste to energy (WTE) methods such as incineration, pyrolysis, and gasification are drawing increasingly global concern (Liu and Liu, 2005). Traditionally, MSW combustible fractions are divided to food residue, wood waste, paper, textiles, plastics, and rubber, six groups (MHUDC, 2009). Figure 1 shows the mean physical compositions of MSW in Chinese cities. The average physical combustible and noncombustible fractions of the MSW were 81.64% and 18.36%, respectively. In combustible MSW, the contents of food residue, plastics, paper, textiles, wood waste, and rubber, in decreasing order, were 55.86%, 11.15%, 8.52%, 3.16%, 2.94%, and 0.84%. However, the classification of MSW is too general to provide more detailed information in many investigations. Many research studies were carried out using samples called food residue or plastics (Guo et al., 2001; Watanabe et al., 2004; Luo et al., 2010), while the groups themselves are very complex and may include subgroups of totally different properties. Plastics, for example, include polyethylene (PE), polystyrene (PS), and polyvinyl chloride (PVC), whose characteristics are completely different. Therefore, it is necessary to perform an investigation on characteristics of combustible MSW to distinguish different categories of MSW and find out their subclassification. Journal of the Air & Waste Management Association, 64(5): , Copyright 2014 A&WMA. ISSN: print DOI: / Submitted September 15, 2013; final version submitted November 30, 2013; accepted December 4,

3 598 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Figure 1. The mean physical compositions of MSW in China. The proximate and ultimate analysis and heating value of MSW are fundamental parameters for incineration, pyrolysis, and gasification (Riber et al., 2009). This paper focuses on these thermochemical properties of the specific components of MSW. Analysis of variance (ANOVA) was adapted to analyze whether significant difference existed in MSW groups. Cluster analysis was carried out to classify MSW specific components. Therefore, some representatives can be selected during the experimental research of MSW. Data and Discussion Characterization and classification of specific components Food residue. Food residue prevails in MSW categories, and is normally divided to five5 subgroups, namely, vegetables, fruit peel, bones, starch food, and nutshells. The proximate and ultimate analysis and heating value results of food residue are shown in Table 1. To eliminate the impact of moisture and ash, the ultimate analysis results were unified into dry ash free basis. Proximate analysis and higher heating value (HHV) were expressed on a dry weight basis. The proximate and ultimate analysis results of food residue varieties are plotted in Figure 2. Starch food, nutshells, and fruit peel had similar proximate analysis characteristics. The proximate analysis of vegetables and bones showed significant difference from other food residue subgroups. Bones had the highest ash content, followed by vegetables. Starch food had the highest volatile matter, followed by fruit peel. Based on elemental compositions, vegetables, fruit peel, starch food, and nutshells were close to each other, while bones had high C þ H content, low O content, and high N þ S þ Cl content. The data in Table 1 are nearly normally distributed. Therefore, ANOVA was applied to investigate statistical significance of grouping the food residue, as shown in Table 2. The statistical significance level a was set as 0.05, which is commonly used in statistical analysis. Therefore, when the significance P was larger than a, there was no significant difference in food residue subgroups; when significance P was smaller than a, a significant difference existed among food residue subgroups (Agresti and Franklin, 2013). As shown in Table 2, there were significant differences in food residue subgroups for all variables. Cluster analysis was applied to classify the food residue subgroups. The variables included ash content (A), volatile content (V), fixed carbon content (FC), C, H, O, N, S, and HHV. All the variables were standardized to a value between 0 and 1. The linkage between groups method was employed as the cluster method. Calculation of distance between any two objects was Figure 2. Chemical compositions of food residue specific components: (a) proximate analysis; (b) ultimate analysis.

4 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 1. Chemical characteristics of food residue Proximate analysis (wt%) Ultimate analysis (wt%) Food residue subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used Vegetable 1. Chinese cabbage Zhang, Potato Liu et al., Pea Liu et al., Scallion Liu et al., Lettuce Zhang et al., 2008a 6. Vegetable Li et al., 1999b 7. Vegetable Dai, Vegetable Zhao et al., 2011 Mean Minimum Maximum Fruit peel 9. Orange peel Zhang et al., 2011b 10. Orange peel Li et al., 1999b 11. Orange peel Zhou et al., Banana peel Zhang et al., 2008a 13. Fruit peel Li et al., Fruit peel Nie, Fruit peel Zhao et al., Fruit peel Zou et al., 2006 Mean Minimum Maximum Bone 17. Rib Liu et al., Rib Li et al., 2001b 19. Fish bone Shen et al., Meat and bone Ren et al., 2011 Mean Minimum Maximum (Continued )

5 600 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 1. (Cont.) Proximate analysis (wt%) Ultimate analysis (wt%) Food residue subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used Starch food 21. Flour Liu et al., Flour Zhang et al., 2008b 23. Rice * 24. Rice Zhang et al., 2007a 25. Rice Liu et al., Rice Zhang, 2005 Mean Minimum Maximum Nutshell 27. Chestnut shell Wang, Peanut shell Hu, Peanut shell Fang et al., Peanut shell Zhang et al., 2011a 31. Walnut shell Wang and Li, 2008 Mean Minimum Maximum Notes: C: carbon content; H: hydrogen content; O: oxygen content; N: nitrogen content; S: sulfur content; Cl: chlorine content; A: ash content; V: volatile content; FC: fixed carbon content; daf: dry ash free basis; d: dry basis; *: the data without a reference were from our own research.

6 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 2. ANOVA of thermochemical properties in food residue subgroups Sum of squares Degree of freedom Mean square F Significance (P) A Between groups Within groups V Between groups Within groups FC Between groups Within groups C þ H Between groups Within groups O Between groups Within groups N þ S þ Cl Between groups Within groups HHV Between groups 2.147E Within groups 2.435E Figure 3. Chemical composition of wood waste specific components: (a) proximate analysis; (b) ultimate analysis. based on the Euclidean distance (Janowitz, 2010). The critical clustering coefficient was set as 1.5, which was suitable for MSW subgroups clustering after many tests. Therefore, the cluster happened with and only with a clustering coefficient less than 1.5. All statistical analyses were performed by SPSS software. The results showed that food residue could be classified into two characteristic groups: (i) vegetables, fruit peel, starch food, and nutshells, and (ii) bones. Wood waste. WoodwasteinMSWcanbedividedintofour subgroups: wood, bamboo, leaves, and weeds. The proximate and ultimate analysis and heating value results of wood waste are shown in Table 3. The proximate and ultimate analysis results of wood waste are plotted in Figure 3. Weeds and leaves had lower volatile matter and higher ash than wood and bamboo. The N þ S þ Cl content of wood waste varied from 0% to 5%. Significant difference in

7 602 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 3. Chemical characteristics of wood waste subgroups and varieties Proximate analysis (wt%) Ultimate analysis (wt%) Wood waste subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used Wood 1. China fir Song et al., Dry wood Li et al., Pine Wang et al., Pine wood Tang et al., 2003b 5. Poplar scraps Wang and Li, Sawdust Hu and Huang, Sawdust Li et al., Sawdust Zhou et al., Sawdust Zhou et al., Sawdust Jin et al., Sawdust Qing et al., Spurce chips Fang et al., Tree branches Li et al., Wood Chen et al., Wood Wu et al., Wood Jiang et al., Wood Liu, Wood Guo et al., Wood chips Zhang et al., 2011b 20. Wood chips Zheng et al., Wood chips Li et al., 1999b 22. Wooden chopsticks Xiao et al., 2009 Mean Minimum Maximum Bamboo 23. Bamboo Liu et al., Bamboo Shao et al., Bamboo Xiao et al., Bamboo Liang et al., Bamboo Yan et al., Moso bamboo Shao et al., 2006 Mean Minimum Maximum

8 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Leaves 29. Chinar leaves Zhou et al., Chinar leaves Dong et al., Fallen leaves Li et al., Leaves Luo, Leaves Zhu, Leaves Zhang, Leaves Zhang et al., 2008a 36. Pine needles Xiao et al., Poplar leaves Zhou et al., 2013 Mean Minimum Maximum Weeds 38. King grass Pu et al., King grass Teng et al., Miscanthus Fu et al., Pennisetum Fu et al., Pennisetum Liu et al., 2012 Mean Minimum Maximum

9 604 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 4. ANOVA of thermochemical properties in wood waste subgroups Sum of squares Degree of freedom Mean square F Significance (P) A Between groups Within groups V Between groups Within groups FC Between groups Within groups C þ H Between groups Within groups O Between groups Within groups N þ S þ Cl Between groups Within groups HHV Between groups Within groups 4.123E Figure 4. Chemical composition of paper specific components: (a) proximate analysis; (b) ultimate analysis. elemental composition was not observed for these subgroups. The HHV also showed little difference. The ANOVAs of proximate and ultimate analysis and HHV among wood waste subgroups are shown in Table 4. Under the 0.05 significance level, A, V, and N þ S þ Cl showed significant differences, and the most significant was for ash content. Cluster analysis indicated that each subgroup was a class and cannot cluster with others with a clustering coefficient less than 1.5. Paper. The paper in MSW can be divided into three subgroups, namely, printing paper (including newspapers, books, and magazines), cardboard, and toilet paper. The proximate and ultimate analysis and heating value results of paper are shown in Table 5. The proximate and ultimate analysis results of paper are plotted in Figure 4. Toilet paper had the highest V and the lowest A, while the proximate analysis of printing paper and cardboard showed no significant difference, as shown in Figure 4a. The values of N þ S þ Cl content of paper samples were all less than 0.25%. Compared with cardboard, toilet paper had higher O and lower C þ H, and the elemental composition of printing paper was in between. As shown in Table 5, the HHV decreasing order was toilet paper > cardboard > printing paper. The ANOVAs of proximate and ultimate analysis and HHV among paper subgroups are shown in Table 6. Under the 0.05 significance level, C þ H and O showed significant difference, and other variables showed no significance. Cluster analysis

10 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 5. Chemical characteristics of paper subgroups and varieties Paper subgroup and variety Proximate analysis (wt%) Ultimate analysis (wt%) A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used Printing paper 1. Blank A4 paper Magazine Nie, Magazine Zhang, Magazine Hu et al., Newspaper Nie, Newspaper Li et al., Newspaper Zhang, Newspaper Newspaper Shen, Newspaper Hu et al., Printing paper Shen et al., Printing paper Liu et al., Writing paper Liu et al., 1999 Mean Minimum Maximum Cardboard 14. Cardboard Dai, Cardboard Cardboard Nie, Cardboard Mi et al., Cardboard Ke et al., Carton Xiao et al., Carton Hu et al., Paper board Li et al., 1999b Mean Shen, 2005 Minimum Maximum Toilet paper 22. Tissue paper Bai et al., Toilet paper Toilet paper Na et al., 2008 Mean Minimum Maximum

11 606 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Figure 5. Chemical composition of textiles specific components: (a) proximate analysis; (b) ultimate analysis. Table 6. ANOVA of thermochemical properties in paper subgroups Sum of squares Degree of freedom Mean square F Significance (P) A Between groups Within groups V Between groups Within groups FC Between groups Within groups C þ H Between groups Within groups O Between groups Within groups N þ S þ Cl Between groups Within groups HHV Between groups 1.341E Within groups 5.560E indicated that paper could be classified to two clusters: (i) printing paper and cardboard, and (ii) toilet paper. Textiles. The textiles in MSW can be divided into three subgroups, namely, cotton, chemical fibers, and wool. The proximate and ultimate analysis and heating value results of textiles are shown in Table 7. The proximate analyses of different textiles samples were scattered, as shown in Figure 5a. Cotton had low A and various V and FC. The ash content of chemical fibers varied greatly. As shown in Figure 5b, the elemental composition of chemical fibers also showed great variance. The N content of acrylic fibers was as high as 20%, due to the monomer contained CN function group. The elemental composition of cotton was similar, and the N þ S þ Cl content was very low. The elemental composition of two wool samples varied greatly. As shown in Table 7, the HHV decreasing order was chemical fibers > wool > cotton. The ANOVA of proximate and ultimate analysis and HHV among textiles subgroups are shown in Table 8. Under the 0.05 significance level, C þ H, O and HHV showed significant

12 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 7. Chemical characteristics of textiles subgroups and varieties Proximate analysis (wt%) Ultimate analysis (wt%) Textiles subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used Cotton 1. Cotton Zhang, Cotton Miao, Cotton Yan et al., Cotton Yan et al., Cotton Liu et al., Cotton Li et al., Cotton Li and Zhang, Cotton cloth Qing et al., Cotton cloth Cotton cloth Li et al., 1999b 11. Cotton cloth Zhang et al., 2008b Mean Minimum Maximum Wool 12. Wool Liu et al., Wool Mean Minimum Maximum Chemical fibers 14. Acrylic fiber Chemical fiber Liu et al., Chemical fiber Zhang et al., 2008a 17. Chemical fiber Li et al., Polyester taffeta Miao, Terylene Mean Minimum Maximum

13 608 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 8. ANOVA of thermochemical properties in textiles subgroups Sum of squares Degree of freedom Mean square F Significance (P) A Between groups Within groups V Between groups Within groups FC Between groups Within groups C þ H Between groups Within groups O Between groups Within groups N þ S þ Cl Between groups Within groups HHV Between groups 1.310E E Within groups 8.368E Figure 6. Chemical composition of plastics specific components: (a) proximate analysis; (b) ultimate analysis. difference, and other variables showed no significance. Cluster analysis indicated that each subgroup was a class and cannot cluster with others with a clustering coefficient less than 1.5. Plastics. Unlike other MSW components, the plastic varieties tend to be pure. Five kinds of commonly used plastics include PE (including high-density polyethylene and low-density polyethylene), PP (polypropylene), PS, PVC, and PET (polyethylene terephthalate). The proximate and ultimate analysis and heating value results of plastics are shown in Table 9. The proximate and ultimate analysis results of plastics are plotted in Figure 6. The proximate analysis values of PE, PP, and PS were close to each other, with V nearly 100%. The proximate analysis of PVC varied greatly. Some PVC samples had little ash and some samples had an ash content as high as 15%. PET had more than 90% V and less than 10% FC. The C þ H content of PE, PP, and PS was nearly 100%, and O, N, S, and Cl contents were almost zero, as shown in Figure 6b. The Cl content of PVC was between 50% and 60%, and the O content of PETwas about 33%. The HHV in decreasing order was PP, PE, PS, PET, and PVC, and the HHVof PVC and PETwas about half of that of PE, PP, and PS. The ANOVA of proximate and ultimate analysis and HHVamong plastics subgroups are shown in Table 10. Under the 0.05

14 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 9. Chemical characteristics of plastics subgroups and varieties Proximate analysis (wt%) Ultimate analysis (wt%) Plastics subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used PE 1. PE Li et al., 1999b 2. PE He et al., PE Zheng et al., PE Chi et al., PE Zhang et al., 2008b 6. HDPE Zhou et al., HDPE Li et al., 2001a 8. LDPE Zhou et al., LDPE Li et al., 2001a Mean Minimum Maximum PP 10. PP Li et al., 2001a 11. PP Yan et al., PP Tang et al., 2003a 13. PP Zhao et al., PP Lan et al., PP Bai et al., PP Feng et al., PP Zhou et al., 2009 Mean Minimum Maximum PS 18. PS PS Li et al., 2001a 20. PS Zhang et al., 2011b 21. PS Zhao et al., PS Feng et al., PS Miao, 2005 Mean Minimum Maximum (Continued )

15 610 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 9. (Cont.) Proximate analysis (wt%) Ultimate analysis (wt%) Plastics subgroup and variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used PVC 24. PVC Jin et al., PVC Zhou et al., PVC Li et al., 1999b 27. PVC Zhu et al., PVC Zhao et al., PVC PVC Jin, PVC Zhang et al., 2011b 32. PVC Li et al., 2002 Mean Minimum Maximum PET 33. PET Lian et al., PET Feng et al., PET PET Zhang et al., 2012 Mean Minimum Maximum

16 Table 10. ANOVA of thermochemical properties in plastics subgroups Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Sum of squares Degree of freedom Mean square F Significance (P) A Between groups Within groups V Between groups Within groups FC Between groups Within groups C þ H Between groups Within groups O Between groups Within groups N þ S þ Cl Between groups Within groups HHV Between groups 1.794E E Within groups 8.863E significance level, all the variables except ash content showed significant difference. Cluster analysis indicated that plastics could be classified to three clusters: (i) PE, PP, PS, (ii) PVC, and (iii) PET. Rubber. Since rubber in MSW was mainly derived from waste tires, it was not subdivided for this paper. The proximate and ultimate analysis and heating value results of rubber are shown in Table 11. As shown in Table 11, the mean C content of rubber was 85.01%. The reason was that in addition to the high C content of the rubber polymer monomer, carbon black was usually added to tires to enhance wear resistance. Rubber had high H content, low O content, and high S and Cl content. Meanwhile, the HHV of rubber was as high as 31,989 kj/kg. The proximate and ultimate analysis of different rubber samples varied significantly, as shown in Figure 7. Comparison and classification of MSW specific components Comparison of MSW specific components. From to 2.1.6, the proximate and ultimate analysis and HHV of MSW specific components were compared, as shown in Table 12. Bones and vegetables had the highest A, while PE, PP, PS, PET, and toilet paper had the highest V and the lowest A and FC. The FC of nutshells and rubber was the highest. PE, PP, PS, and rubber had the highest C and H, while the C and H of PVC were relatively low, due to high Cl. PET had high C but low H. Paper and starch food had the highest O, while PE, PP, and PS had no O and N. Wool was a keratin, a fibrous insoluble animal protein (Pine et al., 1981), and bone contained protein, so they had high N and S. Chemical fiber had the highest N, because of acrylic fiber. The HHVs of PE, PP, and PS were the highest and artificial polymers such as rubber and chemical fiber also had high HHVs, while HHV values of paper, vegetables, and bones were the lowest. Classification of MSW specific components. Cluster analysis was applied to classify MSW specific components. The variables included A, V, FC, C, H, O, N, S, and HHV, as shown in Figure 8. Fruit peel, weeds, wood, bamboo, leaves, and nutshells could be clustered, as shown in Area A. The components of this cluster were composed of pectin, hemicellulose, cellulose, and lignin, with high FC and medium other indicators. Area B included starch food, cotton, toilet paper, printing paper, and cardboard. It seemed that their components were unrelated. However, the main ingredient of paper was cellulose (Wu et al., 2003), and cotton also contained 95% cellulose (Abidi et al., 2010), which had the same glucose monomer as starch food (Pine et al., 1981). Wool and chemical fiber were clustered as shown in Area C, which had low H and O, and high N, S, and HHV. PE, PP, and PS had the most similarity, as shown in Area D. The properties of this cluster were high V (nearly 100%), C, H, and HHV, and low O, N, A, and FC. Vegetables, PET, PVC, bones, and rubber had more complex composition, and thus they could not be classified into the four categories just described. Conclusion The proximate and ultimate analysis and heating value of MSW specific components were analyzed and statistical methods such as ANOVA and cluster analysis were applied. Bones and vegetables had the highest ash content, and nutshells and rubber had the highest fixed carbon content. The carbon and hydrogen content of PVC was low and the chlorine content was very high, accordingly. PET had high carbon content and low hydrogen content. Paper and starch food had the highest oxygen content, and wool and bones had the highest nitrogen and sulfur content. The sulfur and chlorine content of rubber was relatively high. PE, PP, and PS had the highest heating value, and the heating values of chemical products such as rubber and chemical fiber were also very high. Conversely, paper, vegetables, and bones had the lowest heating value. The cluster analysis of MSW components was applied based on proximate and ultimate

17 612 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Table 11. Chemical characteristics of rubber varieties Proximate analysis (wt%) Ultimate analysis (wt%) Rubber variety A d V d FC d C daf H daf O daf N daf S daf Cl daf HHV d (kj/kg) Reference used 1. Rubber Chi et al., Rubber Zhang, Rubber Zhang et al., Rubber Zhang et al., 2007b 5. Rubber Qing et al., Rubber Liu, Rubber Miao, Tire Dai et al., Tire Li et al., Tire Li et al., 1999a 11. Tire Zhou et al., Tire Wang et al., Tire Su et al., Tire Deng et al., Tire Jin et al., Tire Ma et al., Tire Zhang et al., Tire Chen et al., 2012 Mean Minimum Maximum

18 Zhou et al. / Journal of the Air & Waste Management Association 64 (2014) Figure 7. Chemical composition of rubber specific components (a) proximate analysis; (b) ultimate analysis. Table 12. The comparison of the characteristics of MSW specific components Factors Content listed in decreasing sequence A bones > vegetables > printing paper > rubber > cardboard > leaves > weeds > PVC > fruit peel > chemical fiber > nutshells > wood > bamboo > wool > cotton > starch food > PP > PE > toilet paper > PET > PS V PE> PP > PS > toilet paper > PET > cotton > starch food > chemical fiber > wool > PVC > wood > bamboo > cardboard > printing paper > fruit peel > weeds > leaves > nutshells > vegetables > rubber > bones FC nutshells > rubber > fruit peel > bamboo > leaves > weeds > wood > wool > starch food > cardboard > cotton > chemical fiber > PVC > vegetables > printing paper > PET > toilet paper > bones > PS > PE > PP C PS> PE > PP > rubber > PET >wool > chemical fiber > bones > nutshells > weeds > wood > leaves > bamboo > fruit peel > cardboard > cotton > printing paper > vegetables > toilet paper > starch food > PVC H PE> PP > PS > rubber > bones > nutshells > weeds > starch food > cotton > printing paper > cardboard > fruit peel > wood > bamboo > toilet paper > leaves > vegetables > wool > chemical fiber > PVC > PET O toilet paper > starch food > printing paper > vegetables > cardboard > cotton > bamboo > leaves > fruit peel > wood > weeds > nutshells > PET > chemical fiber > bones > wool > rubber > PP > PS > PE > PVC N wool > bones > chemical fiber > vegetables > starch food > fruit peel > nutshells > rubber > cotton > weeds > leaves > wood > bamboo > cardboard > printing paper > PET > toilet paper > PE > PVC > PP > PS S wool > rubber > bones > vegetables > chemical fiber > leaves > PVC > cardboard > nutshells > printing paper > weeds > toilet paper > fruit peel > starch food > bamboo > cotton > wood > PS > PE > PET > PP Cl Q PVC > rubber > cotton > wood > printing paper > bamboo > cardboard > fruit peel > starch food > PE ¼ PP ¼ PS PP > PE > PS > rubber > chemical fiber > PET >wool > PVC > weeds > wood > bamboo > nutshells > leaves > fruit peel > starch food > cotton > toilet paper > vegetables > cardboard > bones > printing paper analysis and heating value results. The results showed that fruit peel, weeds, wood, bamboo, leaves, and nutshells could be classified as a category; starch food, cotton, toilet paper, printing paper, and cardboard could be classified as a category; wood and chemical fiber could be classified as a category; and PE, PP, and PS could be clustered as a category. Funding Financial support from the National Basic Research Program of China (973 Program, no. 2011CB201502) and National Natural Science Foundation of China (no ) is gratefully acknowledged.

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