Mini-review of sewage sludge parameters related to system modelling

Abstract

System modelling of sewage sludge (SS) treatment attracts a growing interest for better comparison and optimisation of technologies. However, SS parameters need to be generalised to be used in holistic assessments, since scattered data may inhibit the development and interpretation of system models. A review of the literature on SS parameters relevant to modelling SS treatment systems revealed 208 datasets published in 162 publicly available scientific papers. We treated thickened and dewatered sludge in the same data analysis, but in some cases, this was an incorrect assumption. The compositional data showed significant variations, but most of the data subscribed to a lognormal distribution, albeit with varying levels of significance. On average, the thickened sludge contained 3.3 ± 1.7% total solid (TS), and the dewatered sludge contained 21.0 ± 6.7% TS. For the combined data, the average Ash content was 32.4 ± 11.8% of TS. Other characteristic parameters were the lower heating value (LHV) of 22.1 ± 2.1 MJ kg⁻¹ volatile solid (VS) and the biochemical methane potential (BMP) of 0.25 ± 0.11 m³ CH₄ kg⁻¹ VS. Fertiliser-related elements were on average 53.3 ± 9.3% C in VS, 6.8 ± 2.2% N in VS, 6.7 ± 2.4% P in Ash and 1.7 ± 1.3% K in Ash. The data reviewed herein provide a good basis for assessing the generality of individual SS data and for selecting key parameters for modelling SS treatment systems. However, the review reveals a need for the better characterisation of SS in the future.

Introduction

Sewage sludge (SS) is a major challenge worldwide due to the large amounts constantly being produced by wastewater treatment plants. Much research (e.g., Ramachandran et al., 2019; Wei et al., 2018; Wu et al., 2020) has focused on the development and optimisation of technologies to handle this sludge and make it environmentally harmless. However, the concepts of sustainability, resource recovery and climate change mitigation have recently developed a growing interest in the modelling of SS treatment systems in terms of material flow analysis (MFA) and life cycle assessment (LCA) (e.g., Dhar et al., 2012; Dussan and Monaghan, 2018; Fang et al., 2019; Li and Feng, 2018; Li et al., 2017; Piippo et al., 2018; Teoh and Li, 2020; Yoshida et al., 2013, 2015). The aim of such modelling is to identify the best technologies and systems for treating SS by quantifying every flow into the system and identifying key environmental data, thereby contributing to better decision-making. However, these studies applied different sludge parameters according to their specific cases, which resulted in difficulties in comparing and generalising the results and conclusions.

Reliable modelling of a SS treatment system requires a thorough characterisation of the sludge entering the treatment system, because the model must keep track of key parameters and their changes as the sludge is routed through the individual technologies constituting the treatment system. Sludge treatment technologies include dewatering, anaerobic digestion, composting, incineration, drying and many others. From a system modelling point of view, the key characterisation parameters for SS are water content (WC) and total solid (TS) content, the content of volatile solids (VS) and Ash in solids, the degradability of VS and the Heating value (HV) of solids. In addition, the contents of carbon (C), nitrogen (N), phosphorous (P) and potassium (K) are also important, because they appear in side streams and rejects that need treatment or can be utilised.

Many papers contain case-specific data on SS composition. González et al. (2017) used multivariate analysis to characterise SS in Andalusia, Spain, while Mailler et al. (2017) provided data on emerging and priority micropollutants in SS in Paris, France, and the heavy metal content of SS was described by Mulchandani and Westerhoff (2016) and Fijalkowski et al. (2017). Other papers have focused on composition change during sludge treatment: Thomsen et al. (2017) reported its composition after pyrolysis, gasification and incineration; Lemée et al. (2017) addressed organic matter changes during drying, irradiation and anaerobic digestion, and Pastor et al. (2008), Egle et al. (2016), Cieslik and Konieczka (2017) and Amann et al. (2021) focused on the content and recovery of phosphorus. These case-specific data showed high variation in sludge parameters, with various sources, preprocesses, analyses and inherences. However, we have found no reviews or data compiling SS parameters from a statistical perspective. This type of data would be useful for the system modelling of sludge treatment in general.

This lack of any overviews of SS parameters relevant to system modelling may inhibit the development of models with respect to process assumptions and structure, input flexibility and the choice of parameters to track throughout the model. Furthermore, this lack of an overview means that it is problematic to evaluate whether sludge in a specific case can be compared to average sludge or is actually an outlier.

This paper reviews and assesses the existing literature on SS parameters relevant to modelling its treatment at the system level and provides a statistical representation of the available data. Only raw sludge from sedimentation and thickening tanks (including primary, secondary or mixed sludge) and sludge after mechanical dewatering were included. This selection was made since this represents the sludge at the interface between the sewage treatment system and the sludge treatment system. The review does not involve treated sludge such as anaerobically digested, aerobically composted or thermally treated sludge, and thus does not address advanced design parameters nor include data on contaminants that may be relevant in national regulation for certain uses of the sludge. By providing this review, we hope to support system modelling in SS management and inspire modellers to use the same set of parameters, thereby enhancing the possibility of exchanging inventory data and sub-models.

Materials and methods

Web of Science^TM was searched for papers directly or indirectly related to SS treatment and disposal in the period 2003–2021. We identified approximately 570 papers, 162 of which contained 208 datasets on sludge composition which clearly stated that they originated from thickened or dewatered sludge without biological treatment processes. Data on SS composition were also found in the other papers, but they were excluded by manual selection because the sludge was treated sludge, the sludge source was not clearly stated or the specification of parameters or units was inadequate. Furthermore, we only selected datasets that were internally consistent by excluding those unbalanced or unexplainable, albeit not all datasets contained information about all the relevant parameters. 85% of the papers were published within the last decade. All original papers (162 papers) and original data are listed in Supplemental A3.

Raw SS is often referred to as ‘primary’ (from mechanical sewage treatment), ‘secondary’ (from biological sewage treatment) or ‘tertiary’ (from advanced sewage treatment), depending on where in the sewage treatment plant it is removed. However, we were not able to use this classification in our data analysis, because the scientific papers often did not inform which type of sludge was being examined and whether the sludge characterised was a mix of different sludges. In the 100 datasets of thickened sludge that reported this information, secondary sludge constituted the main part of the data identified (45%), but undefined mixes were also frequent, accounting for 23% of the data. In the 108 datasets of dewatered sludge that reported this information, undefined mixes constituted the main part of the data identified (66%), while secondary sludge accounted for 26% of the data.

Depending on the interface between the sewage treatment system and the sludge treatment system, and perhaps also depending on the physical distance between the two systems, the sludge may have been reduced in volume via the removal of water prior to its characterisation. In addition, the sludge may also have been aerobically or anaerobically digested prior to water removal or characterisation. In most cases the papers described whether or not some preprocessing had taken place prior to sampling and characterisation of the SS, but not in all cases. This aspect is particularly crucial when reporting TS content or WC, so we had to exclude digested sludge and other treated sludge not given transparent information about preprocessing, and divided SS into ‘Thickened SS’ (after sedimentation and gravity thickening) and ‘Dewatered SS’ (after mechanically dewatering without digestion). For all other key parameters, we referred to TS content. In addition, we assumed that only WC and TS were different between the two sludge types. This is a convenient approximation, but, where possible, we marked the data to check against any violation of this assumption.

System modelling parameters

In the MFA and LCA modelling of SS treatment systems, keeping track of the water, solids and conversions the solids undergo in the system is crucial. SS characterisation must thus provide correct quantitative information on key parameters that undergo transformations in the system. We are fully aware of the fact that many specific parameters are needed in modelling and designing specific processes, such as dewatering and anaerobic digestion. We are also aware of that contaminant levels may restrict certain uses of the SS, for example, the use on land. Nonetheless, herein, we focused on the basic parameters required for more general system-based modelling. We determined the parameters as follows:

• a) WC and TSs content: WC and TS are complementary parameters because they are determined by the same analysis.

WC is a technically important parameter in system design in terms of both volume and mass transported and retention time in the system, thereby allowing processes to perform. This parameter also relates to energy consumption. A common assumption is that water as such is not transformed, although some is actually used in the anaerobic conversion of organic matter. In some cases, water also contributes to side streams and rejected materials that call for further treatment.

TS is involved in many conversions, dependent on the technologies involved. This parameter may also include transfer to the gas and liquid phases. As will be shown below, TS is a complex parameter and must be further specified.

• b) VS and Ash: VS and Ash content are complementary parameters because they are determined by the same analysis. VS and Ash equal TS.

VS is normally used as an estimation of the maximum content of organics present in sludge, though a small quantity of oxidable or decomposable inorganics may also be included. It also contains organic fractions that are difficult to degrade by biological, aerobic and anaerobic processes, and it usually comprises a small fraction of fossil carbon and recalcitrant carbon.

During treatment processes, VS undergoes changes and part of it dissolves, which in turn affects its distribution throughout the system. Thus, distinguishing between volatile suspended solid and volatile dissolved solid (VDS) is done.

Ash characterises the inorganic content in the TS left after all organics have been combusted.

• c) HV: HV is determined by a standardised method and measures how much heat is generated when incinerating sludge. Different HVs and units were found in the literature, but we recalculated these data to the lower heating value (LHV), which is energy content after complete combustion and the evaporation of all water (water in the SS and water generated from oxidising hydrogen in organic matter). If the LHV value is based on TS content, the value includes only the evaporation of water generated from hydrogen converted to water during the combustion of organic matter.

• d) Biochemical methane potential (BMP) is measured by a standardised method in order to determine how much methane can be generated by the sludge under optimal conditions and long retention times (usually 30–50 days). BMP is often used in estimating how much biogas can be generated in an anaerobic digester.

• e) Carbon, nitrogen, phosphorous and potassium: These elements are often used in MFAs and in some LCAs to establish consistent flows and mass balances in individual technologies and at the system level. C and N are used because, to a large extent, they follow the flows and conversions of organic matter. P is primarily monitored in the Ash and is thus related to the solid content of SS. K is rather soluble and is not quantified very often. In addition, all of the elements are important in side streams, fugitive flows and final products leaving the sewage treatment system.

It should be mentioned at this point that the reported studies used a range of units. In some cases, we recalculated these values on the basis of available information, in order to increase the amount of comparable data.

Statistics

Statistical analysis was performed to describe the reviewed data. The parameter distributions were analysed using the software Origin^®. From the preliminary test, we chose lognormal and normal distributions to describe the data. The statistical approach followed common practice. The probability density function and the test statistics are described in Supplemental A1.

Where statistical distributions were determined, we presented the number of data (n), distribution parameters, the mean ± standard deviation (SD) and the p value, representing how well the data fitted the distribution. See Supplemental A1 for details.

Results and discussion

The results of the literature review and the performed statistical analysis are provided in detail in Supplemental Information and summarised and discussed in the following sections.

Total solids and water content

Figure 1 presents data on the TS of thickened SS (n = 97) and dewatered SS (n = 107). As stated earlier, TS and WC are complementary data and originate from the same reported data: TS (%) + WC (%) = 100%. Only a few datasets contain corresponding data on thickened SS and dewatered SS, so we consider the two datasets independent and general. Datasets on TS were fitted by a lognormal distribution; the p value is low for dewatered sludge and not significant for the thickened sludge. The mean ± SD is 3.3 ± 1.7% TS and 21.0 ± 6.7% TS for thickened SS and dewatered SS, respectively.

Figure 1.

Distributions of TS content (%TS) in thickened SS (n = 97) and dewatered SS (n = 107).

The spread in the data is large, thereby reflecting differences in the sludge, the use of supporting chemicals and the specific technologies involved, which are crucial in system modelling and assessment of sludge management technologies. This is particularly the case for the dewatered SS. The data collected do not allow for determining the importance of any of these contributing factors.

Assuming that the dewatered SS was initially thickened, the effect of dewatering means an average increase in TS from 3.1% to 21.2%. To et al. (2016) and Visigalli et al. (2017) described that 20–25% TS is obtained after dewatering on an industrial scale. The extremely high TS contents of dewatered SS (three cases in Figure 1) originate from the experimental use of electrical fields in filter pressing and thus do not represent common technologies. Filter pressing generally yields TS contents between 15 and 25%; approximately 73% of the reported data (n = 78) fall within this range. The observed data are similar but not identical to the data collected in Austria by Amann et al. (2021), who observed from 238 datasets (no information on sludge type was available), median TS contents of 28% for filter presses, 25% for centrifuges and 24% for screw presses.

Ash and VSs

Figure 2 presents data on the Ash content of TS, as reported for thickened SS and dewatered SS; the total number of data is 185 (n = 185). As stated earlier, VS and Ash are complementary data and originate from the same reported data: on a dried sample, on a TS-basis, VS (%) + Ash (%) = 100%. Datasets on Ash can be described by a lognormal distribution with a p value = 0.3, while the mean ± SD is 32.4 ± 11.8%.

Figure 2.

Distribution of ash in the TS (% of TS) of thickened SS and dewatered SS (n = 185).

The spread in the data is large, reflecting differences in the sludge, the use of supporting chemicals and the specific technologies involved. However, the collected data do not allow for determining any of these contributing factors. Figure 2 shows that the general lognormal distribution of Ash is biased, because data for the thickened SS tend to show lower Ash content than dewatered sludge, albeit they both show large variations. This finding may reflect the fact that dewatering covers many different technologies, as well as the use of highly varying types and dosages of polymers added to improve dewatering. This variation is crucial to be considered when assessing efficiencies of technologies and associated use of consumables, to improve the overall sludge management.

Figure 3 presents a plot of the few data we found on VDS (n = 12) and illustrates the percentage of VS that is dissolved as a function of VS in sludge. The dissolved fraction range is between 3 and 15% of VS and constitutes 6.9 ± 3.2% of VS (n = 12) on average. From Figure 3, the percentage of dissolved VS does not correlate with VS content.

Figure 3.

VDS expressed as percentage of VS as a function of the VS content of TS (% of TS).

Lower heating value

HVs were reported in 30 datasets but with several different units. We normalised the data by converting them into the LHV of the water-Ash-free sample (as often used in combustion engineering), which is identical to VS in SS. Figure 4 shows the LHV of dry VS expressed in MJ kg⁻¹ VS as a function of VS content; the average value is 22.1 ± 2.1 MJ kg⁻¹ VS, which is close to the LHV reported for other organic materials, such as leaf litter (21.6 MJ kg⁻¹ VS), food waste (18.1 MJ kg⁻¹ VS), vegetable waste (20.1 MJ kg⁻¹ VS), fruit waste (20.9 MJ kg⁻¹ VS) and paper (20.8 MJ kg⁻¹ VS) (Green and Perry, 2008; Triyono et al., 2018).

Figure 4.

LHV of VS in SS (n = 30) (water-Ash-free sample).

Biochemical methane potential

BMP of the sludge is a key parameter estimating the mass flow and energy recovery of potential subsequent anaerobic digestion of the sludge. Figure 5 presents the BMP in relation to VS content (n = 58). The data are lognormally distributed with an average of 0.25 ± 0.11 m³ CH₄ kg⁻¹ VS. The reported data vary considerably, thereby reflecting the different levels of organic matter degradability in the sludge. The data did not allow for determining the BMP based on the amount of VS degraded. In consideration of an average composition of VS, Buswell’s equation (Buswell and Hatfield, 1936) estimates a theoretical CH₄ production of 0.372 ± 0.096 m³ CH₄ kg⁻¹ VS (n = 28) with a range of 0.21–0.58 m³ CH₄ kg⁻¹ VS, assuming that all VS is converted (Supplemental A2). The average of the BMP values is thus nearly half of the theoretical maximum suggested by Buswell’s equation, which suggests that not all VS in SS is anaerobically digestible. Figure 5 contains only a few BMP data on dewatered SS, but the graph may indicate some differences between thickened SS and dewatered SS, which should be addressed in future studies.

Figure 5.

Distribution and scatter plot of the BMP of VS in SS (n = 58).

Carbon, nitrogen, phosphorous and potassium

Figure 6 shows the distribution of C, N and P; C and N refer to VS content and P refers to Ash content. Most of the data are from dewatered SS: 49 out of 60 samples for C, 53 out of 72 samples for N and 16 out of 26 samples for P. The relative composition of VS in terms of the percentages of C and N is independent of the VS content of sludge. Similarly, the relative content of P in the Ash is independent of Ash content in sludge. The distributions are lognormal, and all p values are high. The average values are 53.2 ± 9.3% C in VS, 6.8 ± 2.2% N in VS and 6.7 ± 2.4% P in Ash.

Figure 6.

(a) Distribution and (b) scatter plot of the C of VS (n = 60); (c) distribution and (d) scatter plot of the N of VS (n = 72); (e) distribution and (f) scatter plot of P expressed as a percentage of Ash content (% of Ash) as a function of the Ash content of TS (% of TS) (n = 26).

We found only 11 datasets for K (five from thickened SS and six from dewatered SS). The average value is 0.38 ± 0.28% K in TS (1.7 ± 1.3% K in Ash) with a huge range of 0.01–1.14% K in TS (0.09–4.98% K in Ash).

Conclusions

Reviewing the literature on SS parameters relevant to modelling treatment systems is important, because the application of material flow modelling (MFA) and LCA helps establish a technically and environmentally consistent platform for identifying good SS treatment systems.

We found 208 datasets with consistent information on SS parameters published in 162 papers in the scientific literature. The data showed significant variations, but most of them subscribed to a lognormal distribution, albeit with varying degrees of fitness. On average, the thickened SS contained 3.3 ± 1.7% TS, and the dewatered SS contained 21.0 ± 6.7% TS. All other parameters were represented on dry samples (TS), sometimes referring to VS content and other times to Ash content. We treated the thickened SS and the dewatered SS in the same data analysis, but for some parameters they were differentiated due to the greater variations in dewatering technologies and the use of additives. For the combined data, average Ash content was 32.4 ± 11.8% of TS. Only nine datasets reported the HV of the sludge with an average LHV of 22.1 ± 2.1 MJ kg⁻¹ VS, which fits with what has been reported for other organic materials. The BMP was reported in 40 cases and showed a large variation with an average of 0.25 ± 0.11 m³ CH₄ kg⁻¹ VS. Four elements reflecting the fertiliser content of the SS were considered, showing 53.2 ± 9.3% C in VS (n = 60), 6.8 ± 2.2% N in VS (n = 72), 6.7 ± 2.4% P in Ash (n = 26) and 1.7 ± 1.3 % K in Ash (n = 11) on average.

The data reviewed in this paper provide a good basis on SS composition for assessing the generality of individual data collected in specific projects, and for the general modelling of treatment systems by applying data distributions with statistical and generalisable significance. However, the review reveals the need for higher precision in the description of different types of sludge and the originating sewage, particularly in relation to technical history prior to characterisation. We deem that consistent information on raw sludge types (primary, secondary or mixed), preprocessing in terms of water removal (thickening or dewatering) and the characterisation data including WC, TS, VS, Ash, degradability of VS, BMP, HV and the contents of C, N, P and K are essential, which could provide a better statistical description of the sludge parameters used in system modelling.