CHARACTERISATION OF MUNICIPAL SOLID WASTE COMPOSITION INTO MODEL INPUTS

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1 CHARACTERISATION OF MUNICIPAL SOLID WASTE COMPOSITION INTO MODEL INPUTS J. LAMBORN Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Australia SUMMARY: Landfill gas generation models require the conversion of waste composition data into model inputs. Waste composition data is usually collected in the form of waste fractions: green waste, food, plastics, metals, paper, inert etc. These waste fractions need to be characterised into cellulose, hemicellulose, lignin and inert; for inputs into models. This paper analyses the work that has been done on this characterisation and identifies where future work could be undertaken to help with the conversion of waste composition data into model inputs. 1. INTRODUCTION A landfill gas generation model is a tool that provides an estimation of generated methane or total landfill gas volume over time from a particular volume of waste. The purpose of a model is to describe (in simple terms) the complex changes during decomposition of waste in a landfill. For a model to accurately reflect the processes within a landfill, it must take into account the complex nature of the microbiological decomposition of waste within a landfill, the nature of the landfill itself, the chemical reactions and the ability of gases and liquids to move through the landfill. These types of models are known as component models and are the next generation of landfill models. The leading component models are LDAT (University of Southampton, UK), POSE (Technical University Braunshweig, Germany), HBM (Napier University, UK) and MODUELO (University of Cantabria, Spain). These models were all compared as part of the Hydro-Physico-Mechanics 2 (HPM2) Challenge to landfill modellers in 2007 (Ivanova, Richards et al. 2007b). One of the main inputs required for any landfill gas generation model is the waste composition data. For this data to be of use for a landfill model, it must be converted into input values that the particular model requires. Depending on the complexity of the model being used, the waste compositions are normally converted into the categories of fast, medium and slow degradation rates. Simple models tend to combine all these rates together to create a combined decay rate that is normally used within a first order decay model. Better developed models use a combination of degradation rates. Component models vary in their approach to dealing with the conversion of the waste composition into model inputs. Majority of models require two - three degradation rates. From the results of HPM2 (Beaven, Ivanova et al. 2007) it was noted that the conversion of the experimental data into input data was challenging for most of the models. Third International Workshop Hydro-Physico-Mechanics of Landfills Braunschweig, Germany; March 2009

2 2. WASTE COMPOSITION DATA Waste composition data is usually collected in the form of waste fractions such as: green waste, food, plastics, metals, paper, textiles, glass, inert etc. Many Environment Protection Agencies around the world undertake regular analysis of their municipal solid waste (US-EPA 2008) (Sustainability-Victoria 2007). Waste composition and quantities vary from country to country and within regions within the one country. Therefore local data is required to predict landfill degradation rates and methane quantities. Some landfills and large scale test cells have a good record of the waste composition, quantities and length of filling; however most full scale landfills are lacking this high quality input data. The waste surveys undertaken at regional, state and/or national level can help provide guidance when individual site data in missing. A good comparison of waste composition data from around the world was undertaken by in 2006, as shown in Table 1. Table 1. Selected global waste composition data ( 2006) Waste component US 2001 Singapore 2000 UK 2004 Germany Spain 2001 Australia 1999 Total paper Newsprint 3.0 Office paper 2.1 Magazines 0.9 Boxes 5.4 Other paper 16.6 Total metals Aluminium cans 0.5 Steel cans 0.6 Other metals 6.3 Total plastics PET 0.3 Milk and water 0.3 bottles (HDPE) Other plastic 14.3 Total glass Glass containers 5.3 Other glass 1.0 Food waste Yard waste Compostables Textiles, rubber and leather Wood Other Miscellaneous inorganics Once the waste composition data for a particular site is known (or estimated), this information needs to be converted into terms that predictive landfill models can use. The number and type of parameters required will depend on the model. However, the component models tend to require multiple degradation rates, rather than a single decay rate such as the more simple models use

3 i.e. the US-EPA first order decay model, LANDGEM (US-EPA 2005). 3. WASTE CHARACTERISATION STUDIES A number of studies have been undertaken over the last decade examining the characterisation of waste composition into cellulose, hemicellulose, and in some cases lignin. (Ham, Norman et al. 1993) (Stinson and Ham 1995) / et al. 1997b), et al. 1997a) (Wang, Odle et al. 1997) (Baldwin, Stinson et al. 1998) (Komilis and Ham 2000) (Environment_Agency (Rodriguez, Hiligsmann et al. 2005) ( 2006) (Ivanova, Richards et al. 2007a) ( 2008) These studies have examined different waste streams and some of these studies are significantly more comprehensive than others. 3.1 Municipal Solid Waste Studies These studies show a large variety in values of all the MSW composition studies examined. This is due to the issues, as discussed above, in the composition and quantities of municipal solid waste between regions, states and countries. Therefore, these values should only be used a guide for modelling purposes due to the range of results, as shown in Table 2. Table 2. Comparison of Municipal Solid Waste studies Secondary source Cellulose Lignin Volatile Protein (C+H)/L solids (Ham, Norman et al. 1993) et al. 1997b) et al. 1997a) Ham and ( 2006) Bookter (1982)

4 Secondary source Cellulose Lignin Volatile Protein (C+H)/L solids (Ivanova, Richards et al. 2007a) Jones and Grainger (1983a,b) Bookter and Ham (1982) et al. (1989) al. (1997) Rhew and (1995) Ress et al. (1998) (unpublished) Price et al. (2003) (unpublished) (unpublished) Average SD Various Waste Composition Studies The results from a number of studies have been grouped by waste type as shown in tables 3 6. Table 3. Comparison of Paper Product Studies Category Secondary source Newspaper (Stinson and Ham 1995) (Environment _Agency Wu et al. ( 2006) (2001) Cellulose

5 Category Secondary source Cellulose al. (1997) ( 2008) Average SD Office Paper Wu et al. ( 2006) (2001) al. (1997) ( 2008) Average SD Magazines (Environment _Agency Other paper (Stinson and Ham 1995) (Environment _Agency ( 2006) al. (1997) ( 2008) Average SD Corrugated containers (Environment _Agency ( 2006) al. (1997) ( 2008) Average SD

6 Table 4. Comparison of Green Waste Studies Category Seed Secondary Cellulose source et al. 1997b) et al. 1997a) (Wang, Odle et al. 1997) (Baldwin, Stinson et al. 1998) Average SD Grass Leaves branches et al. 1997b) et al. 1997a) ( 2008) Average SD ( 2008) ( 2006) al. (1997) ( 2008) Green waste (Environment_ Agency

7 Table 5. Comparison of Food Waste Studies Category Secondary Cellulose source Food waste (Wang, Odle et al. 1997) (Environment_ Agency ( 2006) al. (1997) (unpubl.) (unpub.) ( 2008) Average SD Table 6. Miscellaneous Waste Studies Category Cellulose Textiles (Environment_Agency Diapers (Environment_Agency Misc (Environment_Agency combustible 10mm fines (Environment_Agency % % CALCULATION OF METHANE GENERATED FROM WASTE The quantity of methane generated from waste during its degradation can be calculated from the quantities of cellulose and hemicellulose within that waste. These fractions make up over 90 % of the methane potential (Wang, Byrd et al. 1994). The methane potential of lignin is assumed to be zero due to its inability to decompose under anaerobic conditions (Ivanova, Richards et al. 2008). The maximum theoretical methane potential can be calculated using the following

8 equation (Wang, Byrd et al. 1994): C n H a O b a b + n H 4 2 n a b n a b O + C2O CH Where C 6 H 10 O 5 is cellulose C 5 H 8 O 4 is hemicellulose The maximum methane potential is useful for providing the upper limit of methane generation and is based on all the cellulose and hemicellulose converting to methane. However in reality, lignin can inhibit the degradation of cellulose and hemicellulose as it physically impedes microbial access to these components ( 2008). Also, not all cellulose and hemicellulose is in a bio-available form and therefore these components do not all convert to methane (Wang, Byrd et al. 1994). The chemical pathways for the conversion of cellulose and hemicellulose has been presented by ( 2008): Cellulose (C 6 H 10 O 5 ) n + n H 2 O = 3n CO 2 + 3n CH 4 (C 5 H 8 O 4 ) n + n H 2 O = 2.5n CO n CH 4 4. RESULTS AND DISCUSSION The above tables often show the same results reported by different papers. As many of these papers are from the same or similar groups of authors, the conclusion must be drawn that the same initial study has often provided the final figures. Therefore, for some waste categories insufficient testing has been undertaken to characterise the waste stream. There are a reasonable number of studies undertaken for the combined categories of green waste and paper/cardboard; however, further studies in the area of food waste, in particular, would be beneficial. Table 1 showed a significant variation in waste composition around the world, and Table 2 showed significant variation in the standard deviation of the results of the different MSW studies. The individual waste composition studies shown in Tables 3 6 demonstrate the variation in measured results, and therefore the likely errors which would occur if using an overall generic municipal solid waste study (i.e. from Table 2) instead of site (or region) specific composition data for modelling purposes. 5. CONCLUSIONS The characterisation of municipal solid waste composition into cellulose and hemicellulose can help simplify the conversion of waste composition into inputs that component models can handle. This comparison of studies highlights the importance of having the best site specific data for the model inputs. Using generic municipal solid waste composition data or generic characterisation of MSW data is likely to cause significant errors in the predicted methane generated from a landfill.

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