The principal parameters influencing anaerobic digestion (AD) of sewage sludge have been extensively studied in controlled laboratory experiments, but the effects of sludge composition on full-scale systems have received relatively little attention. Sludge samples from eight major wastewater treatment plants (WWTPs) in the UK were examined to determine the effects of sludge composition on digestion performance. The biogas yield (BY) was estimated by two different methods: (1) a standard approach based on the reduction in volatile solids (VS), and (2) a more detailed mass balance of major constituent fractions of organic matter in sludge. The results showed that BY increased significantly with the overall amount of VS contained in digester feed sludge. In terms of the effects of individual fractions, BY was significantly related to and increased with the fat and cellulose contents in raw sludge, consistent with the high calorific value of fat and the digestibilities of both substrates, relative to the other major organic components. The results demonstrated the importance of sludge composition on digester performance and strategies to maximise BY were identified, for instance, by increasing codigestion of high fat containing substrates, and by utilising fat, oil and grease collected in-sewer and at WWTP.

  • Major organic fractions were determined in sewage sludge samples from eight UK wastewater treatment plants.

  • Biogas yield increased with total organic matter, fat and cellulose contents.

  • Decomposition of major organic fractions was a more reliable estimate of biogas yield than volatile solids.

  • Increasing fat, oil and grease supply to sludge digesters is recommended to improve the process energy balance.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Anaerobic digestion (AD) is a well-established process for the stabilisation and treatment of residual sewage sludge from wastewater treatment (WWT) and produces a methane (CH4)-rich biogas, which is a valuable renewable energy source. Mesophilic anaerobic digestion (MAD) of sewage sludge is receiving considerable attention to maximise the biogas yield (BY, m3/t dry solids (DS)) and renewable energy generation from sludge, to increase revenues and reduce the carbon footprint of WWT.

Biogas yield is influenced by inhibitors, operational conditions and the chemical characteristics of the raw sludge feed to the AD process. General inhibitors such as ammonia, sulphide and excessive amounts of heavy metals and organic contaminants can have potentially adverse effects on AD performance, but the concentrations present in sludge are generally unlikely to exert a negative influence (Li & Fang 2007; Chen et al. 2008). Process operating parameters including feed sludge DS content, hydraulic retention time (HRT) and digestion temperature, on the other hand, have a major influence on BY and have been extensively studied at laboratory scale (Boušková et al. 2005; Alepu et al. 2016; Kim & Lee 2016; Nielsen et al. 2017). A multi-level model of full-scale sewage sludge AD incorporating all of these routinely monitored parameters was also recently reported (Liu & Smith 2020). However, this showed that BY can vary considerably between full-scale MAD sites operating under apparently similar conditions (Liu & Smith 2020).

Volatile solids (VS) data are also routinely collected to monitor the total organic matter content in sludge and the specific biogas yield (m3/t VS) and biogas production (m3/t VS reduction (VSR)) from the AD process. Indeed, absolute biogas volume and BY from sewage sludge AD typically increase with the organic loading rate (OLR) in response to larger VS contents in sludge (Rus et al. 2013). Several studies show that BY is significantly correlated to the chemical composition of different substrates, for example, crop residues, pig slurry, agro-industrial waste, and the organic fraction of municipal solid waste (Schievano et al. 2008; Luna 2011). Thus, the properties of the organic matter in raw feed sludge could, at least in part, explain the intra- and inter-site variation observed in the BY from AD of sewage sludge. However, there has been relatively little investigation of the effects of sewage sludge composition on BY at full-scale AD sites and few studies report this information when characterising the anaerobic conversion performance of sludge to biogas.

The main organic constituents in sludge include protein, fat, carbohydrate and fibre (Table 1). Primary sludge contains more fat and fibre (6–35 and 18–25% DS, respectively), but less protein (16–30% DS), compared to surplus activated sludge (SAS) (1–12, ≤10 and 30–60% DS, respectively), and the reported carbohydrate contents in both sludge types are relatively similar (18–28% DS). The larger protein content of SAS is explained because it is derived primarily from microbial biomass with a relatively consistent carbon to nitrogen ratio of approximately 10 (Hallaji et al. 2019). Toilet paper is a major organic fraction in wastewater, consisting mainly of cellulosic fibre. Primary sludge, therefore, contains a significant amount of fibre material from toilet paper disposal that settles with the solids during primary WWT (Honda et al. 2002). However, understanding the behaviour of toilet paper in wastewater has not been examined to any extent and wastewater characterisation studies generally neglect the assessment of toilet paper in most cases (Ruiken et al. 2013). In Europe, cellulose derived from toilet paper accounts for approximately 40% of the suspended solids and represents a considerable fraction (25–30%) of the chemical oxygen demand (COD) in influent wastewater (Ruiken et al. 2013). Given the varying degradability of fibre materials during AD (Mottet et al. 2010) it would therefore be valuable to also characterise the different fibre fractions present in sludge to assess the effects of fibre content and composition on full-scale AD and BY performance.

Table 1

Major organic constituents reported in primary sludge and surplus activated sludge (SAS)

ConstituentsSludge typeAverage values (% DS) and source
Gonzalez (2006)Barber (2014)Smith (2014)Range
Protein Primary 16–23 22 20–30 16–30 
SAS 33–45 42 30–60 30–60 
Fat Primary 6–15 13 7–35 6–35 
SAS 1–2 5–12 1–12 
Carbohydrate Primary 18–27 23 Not reported 18–27 
SAS 25–28 22 Not reported 22–28 
Fibre Primary 18–25 22 19 18–25 
SAS 0–1 0–10 0–10 
ConstituentsSludge typeAverage values (% DS) and source
Gonzalez (2006)Barber (2014)Smith (2014)Range
Protein Primary 16–23 22 20–30 16–30 
SAS 33–45 42 30–60 30–60 
Fat Primary 6–15 13 7–35 6–35 
SAS 1–2 5–12 1–12 
Carbohydrate Primary 18–27 23 Not reported 18–27 
SAS 25–28 22 Not reported 22–28 
Fibre Primary 18–25 22 19 18–25 
SAS 0–1 0–10 0–10 

The aim of this research, therefore, was to determine the effects of sludge organic matter composition on the bioenergy value of sewage sludge treated by full-scale MAD. Feed and digested sludge samples were collected from eight major UK WWTPs and the contents of protein, fat, carbohydrate and fibre (lignin and cellulose, hemicellulose) were determined. A statistical analysis of the relationships between sludge composition and BY was completed to quantify the effects and significance of different sludge organic fractions on AD performance.

Sampling and data collection

Sludge samples were collected from eight major WWTPs, operated by three water utility companies in the UK, during the period May 2018–February 2019. The sites were selected to represent a wide range of AD process performance. Average site BY values and operational conditions (raw feed sludge DS content, HRT and digestion temperature) were calculated from operational database records for the period 2013–2016, and the mean BY results were in the range of 300–640 m3/t DS (Table 2). The overall mean BY for Sites 4–6 was approximately 400 m3/t DS, representing a typical BY value for MAD of sewage sludge (Bachmann et al. 2015). By contrast, Sites 1–3 and 7–8 were selected to represent higher and lower ranges of AD performance, with overall mean BY values of 595 and 338 m3/t DS, respectively. Sites 1, 3, 7 and 8 were conventional MAD processes, and the other sites operated advanced digestion pretreatments; Sites 4 and 5 were thermal hydrolysis process (THP), Site 2 was heating, pasteurisation hydrolysis (HpH), and Site 6 was enhanced enzymic hydrolysis (EEH).

Table 2

Mean biogas yield and operational conditions for the selected sampling sites based on site records for the period 2013–2016

SiteBiogas yield (m3/t DS)Raw feed sludge DS (%)Hydraulic retention time (days)Digestion temperature (°C)
639 4.1 32.8 36.3 
583 6.7 25.8 38.7 
563 4.3 13.9 36.4 
414 9.1 16.8 40.1 
401 9.1 20.7 41.5 
395 6.6 14.7 39.2 
380 4.2 22.5 37.1 
295 5.7 18.4 40.9 
SiteBiogas yield (m3/t DS)Raw feed sludge DS (%)Hydraulic retention time (days)Digestion temperature (°C)
639 4.1 32.8 36.3 
583 6.7 25.8 38.7 
563 4.3 13.9 36.4 
414 9.1 16.8 40.1 
401 9.1 20.7 41.5 
395 6.6 14.7 39.2 
380 4.2 22.5 37.1 
295 5.7 18.4 40.9 

Sludge samples were collected over a 3 day period on six occasions at intervals of 6–8 weeks, commencing on 09 May 2018, 25 June 2018, 13 August 2018, 15 October 2018, 21 November 2018 and 20 February 2019 to reflect the main seasonal temperature periods. Raw digester feed and digested sludge were sampled at each site and time. The sampling valve was opened and run to waste for at least 2 minutes until the sludge flow was consistent. A clean bucket was filled with representative material and the sludge was transferred to four 500 mL wide-necked, high-density polyethylene (HDPE) containers that were sealed by screw cap. The containers were transported to the laboratory in insulated polystyrene boxes containing frozen ice packs.

Digester feed volume (m3/d) and DS (%), and biogas volume (m3/d) data were provided by site operators and the observed BY was calculated for each sampling event.

Sludge composition analysis

Protein, fat, fibre, DS, VS and COD contents were determined in the combined primary sludge and SAS digester feed and digested sludge samples. DS was measured by oven-drying at 105°C and the loss-on-ignition was subsequently performed in a muffle furnace at 550°C to obtain the VS content (Eaton et al. 2005). Total nitrogen (TN) was examined by a standard Dumas method (Ebeling 1968) and ammonium-nitrogen (NH4-N) and nitrate-nitrogen (NO3-N) were determined by EPA-600/4-79-020 method for water and wastes (USEPA 1983). Protein was estimated by multiplying the organic nitrogen (TN minus NH4-N and NO3-N) by 6.25 (Mariotti et al. 2008). The total fat content was determined by a modified Soxhlet method (5520E) for sludge by extracting the oil and grease after drying the acidified sludge sample (APHA 2005). Neutral detergent fibre, acid detergent fibre and acid detergent lignin were determined sequentially using different detergents as described by Van Soest et al. (1991) to calculate the proportions of cellulose, hemicellulose, and lignin in the fibre fraction. The difference in VS content and the sum of the various organic fractions (fibre, protein and fat) was assumed to represent the total carbohydrate concentration (Astals et al. 2013). The COD concentration was measured using standard methods (APHA 2005) following a 200–1000× dilution with deionised water, depending on the initial DS content of the sludge.

Statistical analysis procedures and estimated biogas yield

General approach

The overall strategy followed for sludge sampling, and chemical and statistical analysis, is shown in Figure 1. The IBM SPSS Statistics 25 and Excel computer programs were used to perform the statistical calculations. Biogas yield is influenced by the total amount of organic matter contained in sludge, which is determined from the loss-on-ignition and VS content. However, the quantity and quality (i.e. CH4 content) of biogas produced during AD vary between the different major organic substrate types, as summarised in Table 3 (Weiland 2010; Li et al. 2018). Therefore, two alternative approaches were adopted to estimate BY based on sludge composition: (i) measured VS results, and (ii) organic constituent analysis.

Table 3

Theoretical CH4 yield and contents of biogas from anaerobic digestion of different organic substrates (Weiland 2010; Li et al. 2018)

SubstrateCH4 yield (Nm3/t DS)CH4 content of biogas (%)
Lipids 1,014 67 
Protein 496 70 
Carbohydrates 400 50 
Cellulose 414 50 
Hemicellulose 409 50 
Lignin 
SubstrateCH4 yield (Nm3/t DS)CH4 content of biogas (%)
Lipids 1,014 67 
Protein 496 70 
Carbohydrates 400 50 
Cellulose 414 50 
Hemicellulose 409 50 
Lignin 
Figure 1

Overall approach to sludge collection, composition analysis, model development and validation (DS, dry solids; VS, volatile solids; VSR, volatile solids reduction; BY, biogas yield; BYC, composition derived biogas yield; BYVSR, volatile solids reduction derived biogas yield).

Figure 1

Overall approach to sludge collection, composition analysis, model development and validation (DS, dry solids; VS, volatile solids; VSR, volatile solids reduction; BY, biogas yield; BYC, composition derived biogas yield; BYVSR, volatile solids reduction derived biogas yield).

Close modal

Predicted biogas yield based on volatile solids reduction

Volatile solids is a simple and practicable approach to determine the total organic matter content in sewage sludge, and the VSR represents the proportion of the VS in the feed sludge that is consumed and converted to biogas by bacteria. VSR is routinely calculated for full-scale MAD processes using the Van Kleeck equation (Van Kleeck 1945):
formula
(1)
where VSRVK is the fractional VSR based on the Van Kleeck equation, and VSf and VSb represent the VS fractions in the feed sludge and after digestion, respectively. The equation assumes no fixed solids reduction or grit accumulation during AD. Additionally, the fixed solids input and output are assumed to be equivalent.
Volatile solids reduction is often used to indicate digestion process performance, and BY can be estimated from VSR by a constant, , representing the average BY of biodegradable matter in feed sludge (Bolzonella et al. 2005; Wu et al. 2016). The BY is also related to the absolute volume of biogas produced and the total amount of sludge feed supplied to the digestion process and these relationships can be expressed as:
formula
(2)
∂ was estimated by linear regression analysis of operational VS and biogas data available for Sites 4, 7 and 8 for the period: 2013–2016. BYVSR was determined for the full-scale AD sites from measured VSR results and ∂, using Equation (2).

Theoretical biogas yield derived from sludge composition

A sludge composition derived BYC was calculated based on the destruction of major organic fractions during AD and their associated CH4 yield values (Table 3), following Equations (3) and (4):
formula
(3)
formula
(4)
where the feed and digested sludge density are assumed to be equal to water (1 t/m3), and the total volume is assumed to be unchanged before and after digestion.

Descriptive analysis

The proportions of each major organic substrate in the feed sludge and the fractions destroyed during AD were calculated on a feed DS and VS basis, respectively, and are summarised in Figure 2. The average DS, VS, COD, and organic fraction composition data for raw feed sludge on a VS basis, and of digested sludge on a DS basis, for each site, are presented in Table S1 and S2, respectively, of the Supplementary Material. The mean concentrations (on a DS basis) of the different components in raw feed sludge by site (Figure 2) were in the range: VS, 72.6–78.3%; protein, 22.4–27.3%; fat, 9.6–15.6%; carbohydrate, 8.9–19.3%; cellulose, 7.6–15.7%; hemicellulose, 1.2–7.1% and lignin, 5.3–11.0%, and were consistent with previous reports (Table 1). The mean COD was in the range 43–142 g/L and was also in line with published values (Nghiem et al. 2014).

Figure 2

Composition of organic and ash fractions in feed sludge (% DS basis; outer ring) and corresponding destroyed organic fractions (% feed VS basis; inner ring) by anaerobic digestion.

Figure 2

Composition of organic and ash fractions in feed sludge (% DS basis; outer ring) and corresponding destroyed organic fractions (% feed VS basis; inner ring) by anaerobic digestion.

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Linear regression analysis was performed and the raw data was plotted to identify and examine the relationships between feed sludge composition and corresponding destroyed organic fractions by MAD, to determine the digestibility properties of different major organic constituents present in sludge (Table 4 and Figure S1, Supplementary Material). All the individual substrates destroyed during MAD were significantly (P < 0.05) and positively related to the concentration of each corresponding fraction in the feed sludge. Interestingly, the strongest statistical association was found for cellulose (R2 = 0.88), indicating that the proportion destroyed by AD (6.0–15.9% feed VS, site mean, Figure 2) largely depended on the concentration in the feed sludge (range of site means: 7.6–15.7 and 10.3–20.1% feed DS and VS, respectively). The digestibility of the cellulose fraction in sludge was also high, equivalent to 75.1%, compared to the other substrates (Table 4), consistent with the anaerobic biodegradability of cellulosic biomass reported by Ma et al. (2019) of 76.4%.

Table 4

Linear regression analysis of organic fractions destroyed by anaerobic digestion (% feed VS basis) relative to the concentration in feed sludge (% feed VS basis) and the overall mean digestibility of individual constituents (pooled data for all sites)

ConstituentSignificance (P)R2CoefficientDigestibilitya (%)
Protein <0.001 0.46 1.18 21.4 
Fat <0.001 0.63 0.78 78.1 
Carbohydrate <0.001 0.57 0.83 41.8 
Cellulose <0.001 0.88 0.86 75.1 
Hemicellulose <0.001 0.49 0.62 50.6 
Lignin 0.001 0.23 0.50 12.8 
ConstituentSignificance (P)R2CoefficientDigestibilitya (%)
Protein <0.001 0.46 1.18 21.4 
Fat <0.001 0.63 0.78 78.1 
Carbohydrate <0.001 0.57 0.83 41.8 
Cellulose <0.001 0.88 0.86 75.1 
Hemicellulose <0.001 0.49 0.62 50.6 
Lignin 0.001 0.23 0.50 12.8 

aDigestibility represents the ratio of the fraction of each substrate destroyed (% feed VS) and of the concentration in feed sludge (% feed VS) * 100.

The highest overall digestibility was recorded for fat, equivalent to almost 80%, representing 7.6–17.8% of the overall VS destroyed (site mean, Figure 2). The R2 value obtained for the digestibility of protein (Table 4, Figure S1) was smaller (0.46) compared to most of the other substrates, except for lignin; however, protein had the largest overall regression coefficient (1.18), also indicating that protein destruction (2.1–12.0% feed VS, site mean, Figure 2) strongly depended on the concentration in the feed. This behaviour was explained because protein concentrations fell within a relatively narrow range in the feed sludge (range of site means: 22.4–27.3 and 29.1–34.5% of feed DS and VS, respectively) compared to the other substrate types. A smaller variation in protein content may be expected because it is largely derived from biosynthesis reactions during biological wastewater treatment and the relatively stable protein content present in SAS (Table 1).

The other major organic constituents in sludge, on the other hand, are mainly transferred directly from raw wastewater influent and, consequently, the concentrations in sludge are potentially considerably more variable. Weaker, albeit statistically significant, associations were observed for hemicellulose and lignin (Table 4), consistent with the poor anaerobic digestibility of these fractions (representing site average destruction values of 0.2–5.2 and 0.0–4.8% feed VS, respectively) and particularly of lignin, which is a complex, recalcitrant plant constituent with a characteristically low CH4 yield and production rate (Klimiuk et al. 2010; Weiland 2010).

Estimating biogas yield based on sludge properties

Two alternative methods were used to estimate BY based on: (i) VS and (ii) composition of major organic constituents. Firstly, a simple correlation between BY and VSR was obtained based on the analysis of operationally recorded data (n = 87) available for three of the sampling sites (Sites 4, 7 and 8) where VS was routinely measured. The results showed a statistically significant (P < 0.001) and positive relationship between VSR and BY with a moderate degree of confidence (R2 = 0.46) (Figure 3). The observed VSR for the collected sludge samples was calculated based on the corresponding feed and digested VS values using Equation (1). Finally, the BYVSR was calculated from the observed VSR using the correlation obtained from Figure 3. A sludge composition derived BYC was also calculated for each sampling event using Equations (3) and (4), based on the fraction destroyed during AD and the assumed BY for each type of organic substrate (Table 3).

Figure 3

Relationship between observed biogas yield (m3/t DS) and volatile solids reduction (%) for site recorded data obtained for Sites 4, 7 and 8.

Figure 3

Relationship between observed biogas yield (m3/t DS) and volatile solids reduction (%) for site recorded data obtained for Sites 4, 7 and 8.

Close modal

Comparison between VSR, composition derived and observed biogas yield

The BYVSR and BYC were compared to the observed BY data obtained for each sample time (for the sites that measured biogas flow). The observed BY (316.5–1,159.1 m3/t DS) for Site 1 was significantly above the range considered representative of conventional AD: 300–440 m3/t DS (CIWEM 1996; Bachmann et al. 2015). Issues associated with gas recording are a possible reason for the misalignment of gas data, and Site 1 was therefore removed from the comparison of observed and estimated BY results. Statistical analysis showed that the observed BY values were positively and highly significantly correlated to BYVSR (P = 0.011) and BYC (P < 0.001) (Figure 4). Therefore, the observed BY could be described by either BYVSR or BYC; however, the more detailed sludge composition approach provided considerably greater statistical confidence and larger R2 value (0.64) compared to VSR (0.34). Furthermore, the smaller intercept value and close to ideal slope (approximating to 1) obtained for the relationship between BYC and observed BY indicated the improved explanation of actual BY compared to BYVSR.

Figure 4

Relationship between observed biogas yield (m3/t DS) and (a) volatile solids reduction derived BYVSR and (b) sludge composition derived BYC for data collected from Sites 3, 4, 7 and 8.

Figure 4

Relationship between observed biogas yield (m3/t DS) and (a) volatile solids reduction derived BYVSR and (b) sludge composition derived BYC for data collected from Sites 3, 4, 7 and 8.

Close modal

Factors affecting the estimated biogas yield

The differences between the observed, VSR and sludge composition derived BY values may be explained because each have certain limitations in the way they represent actual digester performance, and these can be divided into three main areas.

Firstly, recording errors can influence data accuracy at full-scale sites. For example, the observed BY data provided at Site 1 is an extreme case. Furthermore, simultaneous spot measurements of feed and digested sludge properties do not directly correspond due to the effects of retention time and mixing of sludge of different age and properties in the process at full-scale sites. Finally, and most importantly, understanding the variation in the content and decomposition of biodegradable substrates in sewage sludge provides a considerably more reliable estimate of BY compared to the standard VSR approach (Figure 4).

To examine these factors further, BYVSR for the eight sampling sites was related to the BYC, for each sampling event (Figure 5). As would be expected, BYVSR and BYC were positively and highly significantly correlated (P < 0.001), and the majority of values approximated to the ideal correlation. To examine the underlying patterns between BYVSR and BYC, the data were divided into two groups: Group 1 and Group 2, representing BYC values that were either less than or above the ideal correlation, respectively. Boxplots were used to represent the compositional differences between the two groups (Figure 6). Analysis of variance (ANOVA) showed that Group 2 samples had significantly (P < 0.001) larger fat concentrations in the feed sludge compared to Group 1. The increased BYC observed for certain sites could therefore be explained by the larger fat content and because fat has a much larger CH4 yield, equivalent to 1,014 m3/t DS, compared to the other main biodegradable fractions, which have broadly similar yields of approximately 400–500 m3 CH4/t DS (Table 3) (Weiland 2010; Li et al. 2018). A linear correlation analysis of the effects of the different sludge organic components on BYC was also performed (Table 5 and Figure S2, Supplementary Material) and showed that BYC was statistically significantly and positively correlated to VS, fat, total fibre and cellulose contents in feed sludge (P values: < 0.001, 0.002, <0.001 and 0.007, respectively).

Table 5

Linear correlation results between the composition derived biogas yield (BYC) and the concentrations of volatile solids (VS) and major organic constituents (% DS) in raw feed sludge (pooled data for all sites, n = 43)

Sludge componentSignificance (P)R2Positive or negative correlation (+/−)
VS < 0.001 0.30 
Protein 0.881 < 0.01 
Fat 0.002 0.21 
Cellulose 0.007 0.16 
Hemicellulose 0.371 0.02 
Lignin 0.348 0.02 
Total fibre < 0.001 0.25 
Sludge componentSignificance (P)R2Positive or negative correlation (+/−)
VS < 0.001 0.30 
Protein 0.881 < 0.01 
Fat 0.002 0.21 
Cellulose 0.007 0.16 
Hemicellulose 0.371 0.02 
Lignin 0.348 0.02 
Total fibre < 0.001 0.25 
Figure 5

Relation between biogas yield (m3/t DS) derived from the destruction of major sludge organic fractions (BYC) and from volatile solids reduction (BYVSR) for Sites 1–8.

Figure 5

Relation between biogas yield (m3/t DS) derived from the destruction of major sludge organic fractions (BYC) and from volatile solids reduction (BYVSR) for Sites 1–8.

Close modal
Figure 6

Box-plot comparing the concentrations of major organic constituents in feed sludge (% DS) for Group 1 and 2 sludge samples with lower or increased biogas yield, respectively.

Figure 6

Box-plot comparing the concentrations of major organic constituents in feed sludge (% DS) for Group 1 and 2 sludge samples with lower or increased biogas yield, respectively.

Close modal

The particularly strong influence of fat on BY was consistent with the large calorific value and digestibility of fat (78%, Table 4) compared to other organic substrate types. The fat content of raw feed sludge to the AD process could therefore be a major factor explaining the variation in BY observed between sludge sources of similar VS content. Cellulose (which is typically the main constituent of the fibre fraction, Figure 2) was also important because it similarly demonstrated relatively high digestibility (75%, Table 4), albeit with a smaller contribution to BY compared to fat (Table 3). By contrast, no statistically significant (P > 0.05) effects of the concentrations of the other major organic constituents on BY were detected individually; however, the digestibility results showed that, collectively, they make an important contribution to overall BY (Figure 7).

Figure 7

Overall average contribution of individual organic substrates to the composition derived biogas yield (BYC m3/t DS) from mesophilic anaerobic digestion of sewage sludge.

Figure 7

Overall average contribution of individual organic substrates to the composition derived biogas yield (BYC m3/t DS) from mesophilic anaerobic digestion of sewage sludge.

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Only a limited amount of work has considered the effects of organic matter composition on anaerobic biodegradation and CH4 potential of sewage sludge. For example, Astals et al. (2013) conducted laboratory-scale, batch biomethane potential (BMP) experiments with seven mixed sewage sludge samples collected from different WWTPs and found no statistically significant relationships between the concentrations of protein, fat or carbohydrate in sludge and the cumulative specific CH4 yield. By contrast, we found BY was significantly influenced by the fat content in feed sludge. This may be explained because the sludge samples collected here, which were representative of typical UK conditions, contained considerably more fat, in the range 9.6–15.6% DS, compared to material examined by Astals et al. (2013), with only 2.0–8.0% of fat in the DS.

The individual contribution of each substrate type to the overall BY for each site and sampling time is shown in Figure 8. Thus, whilst fat represented one of the smallest mass fractions, only accounting for up to 15.6% of the DS in feed sludge (Figure 2), fat contributed up to almost 80% of the overall BYC, with an average value equivalent to 41%. In comparison, the mean contributions of protein, carbohydrate, cellulose and hemicellulose to the total BYC were approximately 12, 20, 21, and 6.6%, respectively. Therefore, the results demonstrate the importance of fat as a critical substrate and contributor to the BY from AD of sewage sludge.

Figure 8

Contribution of individual organic substrates to the composition derived biogas yield (BYC m3/t DS) from mesophilic anaerobic digestion of sewage sludge.

Figure 8

Contribution of individual organic substrates to the composition derived biogas yield (BYC m3/t DS) from mesophilic anaerobic digestion of sewage sludge.

Close modal

Several studies confirm that the BY of agro-industrial substrates is significantly correlated with the fibre composition of the feedstock. For example, Luna (2011) measured the BMP of 61 different agricultural/industrial substrates and found the BY and kinetic rate constant decreased significantly with increasing lignin content. Our results were similarly consistent with previous reports (Klimiuk et al. 2010; Weiland 2010) and showed lignin was more resistant and recalcitrant to AD compared to the other organic fractions present in sludge (Figure 2 and Table 4). Raw feed sludge contained up to 30% DS of fibre, with an overall site mean of 23% DS and mean lignin and cellulose contents in the fibre of approximately 30 and 51% DS, respectively, probably originating mainly from toilet paper, which is a major source of fibre entering WWT (Honda et al. 2002). Cellulose and total fibre contents in the feed sludge were significantly and positively correlated to the BY. This may be explained because cellulose had the second highest digestibility (75.1%; Table 4) and contribution (21%) to the total BYC (Figure 7). Therefore, an increasing proportion of cellulose in the total fibre content in raw feed sludge may also have a positive impact on the BY.

Temporal patterns in digester performance

Seasonal differences were apparent in the VS and fat concentrations in raw feed sludge and BYC, over the duration of the sampling period, shown in Figure 9. In general, VS in the feed sludge increased during the cooler autumn/winter and spring period (October, November and February) compared to the summer season (May, June and August). This behaviour could be explained by the conservation of easily digestible organic matter in sewer during the cooler winter and spring period. On the contrary, the fat content in feed sludge decreased significantly during the autumn/winter period (October and November) compared to the summer season (May and June), possibly due to the reduced mobility of fat in sewers in cooler conditions. The seasonal patterns observed in VS and fat contents appeared to have an important influence on the overall BY performance. For example, increased VS concentrations measured in feed sludge in October and November would be expected to increase BY, but the fat concentration, which is the most significant substrate influencing BY, declined during this period; thus, the overall calorific balance and BYC remained relatively stable and consistent during May to November. However, high VS and fat concentrations in the feed sludge both occurred in February and, as may be expected, this was associated with the highest overall BYC values recorded compared to other times.

Figure 9

Relationship between composition derived biogas yield (BYC, m3/t DS), and volatile solids and fat concentrations in feed sludge (fat concentrations are represented by the bubble size).

Figure 9

Relationship between composition derived biogas yield (BYC, m3/t DS), and volatile solids and fat concentrations in feed sludge (fat concentrations are represented by the bubble size).

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Optimisation strategies

The results showed that BY was positively affected by the VS, fat and cellulose content in the raw feed sludge to the AD process. Therefore, one approach to increasing biogas and renewable energy outputs with the minimum digestion capacity is to co-digest sewage sludge with high fat containing materials. At WWTPs, fat, oil and grease (FOG) is the primary source of fat in sludge, which is a lipid-rich material from edible oil disposal, food processing industries, slaughterhouses, and food wastes (Salama et al. 2019). However, FOG is regarded as a major problem and challenge to WWTP operation. For example, blockages caused by FOG accumulation in the sewer system are responsible for over 12,000 annual flooding incidents in the UK (Williams et al. 2012).

Once FOG reaches the WWTP, it can cause additional operational problems. FOG is more difficult to biologically degrade in WWT than other common components of municipal sewage as it can congeal and form solid deposits on the surface of settling tanks, pipes, pumps, sensors and other surfaces within the WWTP. Therefore, it is important to have effective FOG collection systems in sewer and at WWTPs. FOG can be removed at source, within the sewer network and at the WWTP, and FOG waste collected from grease traps can be supplied as a supplementary feedstock to AD. Many studies report that the co-digestion of WWTP sludge with high fat containing co-substrates, such as grease trap waste, can provide a stable digestion process and increase the BY (Davidsson et al. 2008; Long et al. 2012; Cook et al. 2017). For example, Long et al. (2012) reported a 30–80% increase in digester gas volume at two full scale municipal sludge AD co-digestion plants supplied with FOG at a rate of 10–30% by volume of total digester feed.

Glycerol is an organic, readily digestible substrate and is a product from the hydrolysis of FOG (Salama et al. 2019), which can be easily stored over a long period (Fountoulakis et al. 2010) and is another popular and effective substrate for sewage sludge co-digestion. For example, Chow et al. (2020) showed the co-digestion of sewage sludge with crude glycerol, supplied at a rate of 0.5 and 1.0% by volume of feed sludge, increased the CH4 yield by 73 and 115%, respectively.

However, excess fat can disrupt the AD process by inhibiting acetoclastic and methanogenic bacteria, and causing sludge flotation, digester foaming, blockage of pipes and pumps, and clogging of gas collection and handling systems (Long et al. 2012). Chow et al. (2020) suggested that the FOG to sewage sludge ratio should be maintained below 60% on a VS basis to maintain stable AD operation. Higher FOG ratios may cause biomass aggregation and mass transfer limitations due to long-chain fatty acid (LCFA) accumulation on and in the biomass aggregates. High LCFA contents from the degradation of lipid-rich materials may inhibit methanogenic bacteria and have operational consequences such as clogging and scum formation (Chow et al. 2020). Nevertheless, significant improvements in BY and preferential energy balances could be readily achieved by supplying lipid-rich waste materials and problematic FOG waste, collected in-sewer and at WWTP, to the AD process. However, the codigestion of high fat containing substrates with sewage sludge should be carefully optimised to maximise the renewable energy and waste treatment benefits and reduce potential risks to the AD process.

The concentrations of major organic constituents and VS were measured in raw feed and digested sludge samples collected between May 2018 and February 2019 from full-scale, mesophilic anaerobic digesters at eight WWTPs in the UK to determine the relationships between sludge composition, biodegradation and BY. A series of statistical tests and two BY estimation methods were applied to examine the effects of sludge composition on BY. Biogas yield significantly increased with the total organic, fat and cellulose contents in feed sludge. The content and decomposition of organic fractions in sewage sludge was considerably more reliable compared to the standard VSR approach to predict BY (Figure 4). Fat was identified as being particularly critical to the AD process and BY due to the larger CH4 yield and digestibility (78%) of this substrate compared to several other major organic constituents (protein, carbohydrate, hemicellulose) present in sludge, which had no significant effect on BY individually, but made an important contribution collectively to BY. This may be explained because, with the exception of lignin which, as was expected, showed relatively poor digestibility (13%), the CH4 and biogas potentials of other major substrates were generally similar; consequently, variation in sludge content had little influence on the overall BY. In contrast, cellulose, probably largely originating from toilet paper, was also a significant contributor to the overall BY due to the relatively high digestibility (75%) of this substrate in sewage sludge AD, which was the second largest after fat. The results demonstrated seasonal variations in sludge composition and digester performance; for instance, concentrations of VS and fat in feed sludge, and BY, increased in the spring compared to sludge sampled at other times. The results indicate that increasing the amount of fat in sewage sludge AD offers an effective strategy to improve the renewable energy balance and BY of the process. One way to achieve this would be to optimise the codigestion of sewage sludge with FOG waste, which accumulates and is readily available, but often presents a major operational and disposal problem, in sewer networks and at WWTPs.

This research was sponsored by Anglian Water Services Ltd, Severn Trent Plc, Thames Water Utilities Limited, United Utilities Group Plc, and Yorkshire Water Services Ltd. The authors would like to thank the steering group for their expert advice and feedback on the project. We also thank Matthew Smith for technical support and providing transportation to the sampling sites. The views expressed in the paper are those of the authors and do not necessarily represent the companies supporting the research.

All relevant data are included in the paper or its Supplementary Information.

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Supplementary data