A Short Review on Feedstock Characteristics in Methane Production from Municipal Solid Waste

MSW normally consists of 46% organic fraction, 17% paper, 10% plastics, 5% glass, 4% metal and 18% others (Fig. 3) [1]. Out of total MSW generated, the organic fraction is greatly varied between 50–70% for low and middle-income communities and 20 – 40% for high-income communities in developing and developed countries respectively [38, 39].

Figure 3

When organic fraction present in MSW is more it When organic fraction present in MSW is more it results in a larger volume of CH₄ production due to bacterial decomposition[9]. The undesirable substances such as plastics, metals etc., affect the process of digestion because of its non – biodegradable nature and finally, it increases the budget of the AD process. Hence, on-site segregation of MSW or mechanically sorted MSW is required to improve the digestion process as well as CH₄ production [39, 40].

There are a variety of factors that influence OFMSW behaviour during the AD process. The general characteristics of OFMSW that significantly contribute to AD are Physical, Chemical and Elemental composition.

The OFMSW substrate includes the physical composition of particle size and density. The impact of the surface area of OFMSW on digestion rate has not been properly examined and the high density of substrate indicates the exclusion of non-biodegradable from the feedstock [1]. The elemental composition of the OFMSW substrate consists of carbon (C), hydrogen (H), nitrogen (N), Oxygen (O) and sulphur (S). These elements are considered important sources of energy and new cell formation for anaerobic bacteria [41].

The main properties of the chemical composition of the substrate are pH, WC, TS, VS, chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) [42]. These directly influence the microbial activity of the AD process [1]. Hence, the characteristic study of feedstock is needed to obtain a better quality of biogas and digestate [42].

The published studies mainly focus on the physical, chemical and elemental composition of feedstock in the generation of methane as renewable energy. Figure 4 shows the importance given by the researchers in terms of feedstock characteristics.

Figure 4

As chemical composition contributes more to methane production, the key chemical characteristics of OFMSW substrate are considered for regression and the same is explained below.

Water Content

The generation of MSW in a developing nation like India consists of high moisture content (40%–60%) and low calorific value (800–1100 kcal/kg) [14]. The high water content present in MSW is helpful for bacterial decomposition under oxygen-free conditions and supports the chemical reactions that produce methane gases[1], [9]. A higher moisture content (90%) yield leads to producing a higher amount of biogas from MSW[43]. Hence it is required to increase the water content of the substrate to produce a high amount of biogas; as this parameter is also important to determine the other chemical characteristics of MSW like TS and VS[1]. The process of AD is also classified into dry AD (60–75%) and wet AD (85–90%) based on the water content of MSW [42] making it an important parameter to be considered.

Total Solids and Volatile Solids

Total solids (TS) content of the feedstock is commonly used to classify AD into two types: i. namely low-solids or wet digestion for TS < 15% and ii. high-solids or dry digestion for TS >15 to 20% [34, 44, 45]. Many research studies reported that the performance of AD is influenced by initial substrate concentration (TS >30%) which results in reducing the methane yield. At 30% of TS content, the CH₄ production reduces by 17% compared to 20 % solid content; this is mainly due to the accumulation of volatile fatty acids (VFAs) [26, 44, 46, 47].

The degradable solid organic matter is represented as volatile solids(VS) and non-degradable besides with some non-digestible VS – called fixed solids. OLR is an operational parameter that is represented by the concentration of VS in the substrate. Higher OLR in the digestion process produces higher biogas and CH₄ yield. But the higher amount of VS fed into the digester influences the pH and alkalinity of the digester due to the formation and accumulation of volatile acids [37, 48].

pH

The process and performance of AD are affected by pH as it is a basic and vital parameter that influences the growth of microorganisms in various stages of AD [37, 49]. A lower pH value affects methanogens and higher pH leads to producing toxic ammonia. The variation of pH in AD is occurred due to three main factors such as volatile fatty acids (VFA's), bicarbonates and alkalinity of the system. The alkaline reagents such as NaOH, NaHCO₃, Na₂CO₃ and Ca(OH)₂ are used for controlling pH to improve the performance of biomethanation [50]. Based on the type of substrate and digester, the optimal pH is maintained to increase the rate of reaction in AD [42]. The pH value near neutral is suitable for methanogenesis activity and it produces higher CH₄ at high moisture content. Any material can be added to the feedstock to maintain the value of pH and to ensure the continued existence of an active biological population in AD [1].

Temperature

Temperature is the main environmental factor when considering the release of CH₄ from landfill sites as well as the digestion of OFMSW in AD. The variation in temperature influences the activity of methane-forming bacteria in the fermentation process [37, 49, 51, 52, 53]. Many researchers commonly used two kinds of AD processes; such as mesophilic AD (30–40°C) and thermophilic AD (50–60°C) for the generation of biogas and methane[14, 21, 38, 54]. Hence, the optimal temperature is considered for constant and effective fermentation. The biogas generation from OFMSW at thermophilic AD is higher than that at mesophilic AD, which is mainly due to the increased temperature [47].

Many authors carried out the experiments at mesophilic (30–40°C) and thermophilic(50–60°C) conditions in the AD process. The bio-degradable waste used as feedstock is vegetables, fruit wastes, fish wastes, food wastes, supermarket wastes and OFMSW to determine the production of CH₄ at varying operation conditions [1, 21, 38, 46, 47, 55, 56, 57, 58]. The characteristics of OFMSW are generally classified into physical, chemical and bromatological analysis[46, 59, 60, 61, 62]. Several authors studied the impact and developed the model on methane production by considering the various process parameters such as pH, Temperature, C/N ratio, TS, OLR, Hydraulic retention time (HRT)[1, 12, 24, 29, 63].

The key literature considered in understanding methane production in the last two decades is listed in Table 1.

The selection criteria for the source of feedstock are: (i) Source-separated OFMSW (ii) Mechanically separated OFMSW (without considering nonbiodegradable) (iii) Supermarket food waste – MSW (iv) Single substrate feedstock are considered for this study as they contribute a large amount to MSW.

Data regarding the input characteristics of feedstock and CH₄ production are taken from various research articles for the regression model and the units for each parameter considered are homogenised to make an easier comparison. Data used in this work include 56 samples published between the period 2000–2020.

In this study, influencing feedstock parameters such as pH, TS, VS and WC are considered for regression analysis. From the literature, most of the authors report TS and VS range between 5–50% of wet weight with high moisture content, whereas pH ranges from 3.7–8.1 and CH₄ production between 61–859 L/Kg of VS. The original data value of pH, TS and VS are transformed into natural logarithmic form to get a better fit in the model and the value of WC is not changed. The transformed value of the parameters is listed in Table 2.

MLR is a statistical tool that uses several independent variables to predict the output of a dependent variable. The objective of MLR is to develop a model between the dependent and independent variables. The key variables used for regression are mentioned in table 2 and the regression model was developed using excel software 2010. In this study, the amount of methane produced is considered a dependent variable and pH, VS, TS and WC are considered independent variables. Based on the literature, the temperature parameter taken for the regression analysis is not significantly contributing to methane production. Hence, the temperature is not considered for this study.

The co-efficient of R square between the methane production and other variables such as pH, VS, TS and WC are studied. Regression equation (1) was developed to predict the methane production for the given data and that is compared with the actual methane production mentioned in Table 2.

In the above eq (1), Y is the methane generation rate (L/kg of VS) and natural logarithmic transformation is applied to the independent variables except for water content for regression analysis. The independent variables such as VS, TS and WC are considered in percentage. The regression analysis for the given data is carried out and found regression coefficients for the model. Analysis of variance (ANOVA) is used to determine the statistical significance between dependent and independent variables with a threshold p-value is 0.05.

The regression curve was plotted using Eq. 1 shown in Figure 5 and the percentage variation accounted for by the model is low i.e R squared value = 0.636. These results revealed that 64% of the variation is explained by input feedstock characteristics. ANOVA results revealed a significant difference by the functional group in all measure feedstock characteristics (p < 0.05 in all parameters) except for TS content.

Figure 3

The pH is the most significant parameter that directly influences the digestion progress and end product. In the digestion process, a pH of 4–8.5 is required by the hydrolytic bacteria and acidogens while a narrow range of pH of 6.5–7.2 is required by methanogens. Hence, the microbial activities in AD are significantly affected under too high pH or too low pH. The optimal pH for overall AD was reported to be in the range of 6–8.

The pH is relatively correlated with methane production compared with other input feedstock factors and the tendency of correlation is negative. Figure 5 shows the minimum and maximum pH values that are taken from the data. The maximum pH value almost fits on the trendline of the generated Eq. (1). From the generated equation, methane production is suitable for a pH value of 8.1, with corresponding TS and VS ranges between 20–22% with higher water content.

Previous literature mainly focused on the AD process of landfill waste to a maximum extent and developed a model through their experimental or field data. Hence, our attempt is limited to the chemical composition of the substrate in understanding the feed-stock characteristic in CH₄ generation. Out of all chemical compositions of the substrate, few parameters were considered due to the availability of a limited data source.

The input feedstock parameters considered in the regression are pH, VS, TS and WC. However, many parameters influence the model generated to a larger extent such as COD, bromatological factors etc. in the MSW effect the methane production. The data collected from the literature is ensured that they have the considered input values to analyse the relationship the methane production and feedstock parameters.