Elsevier

Water Research

Volume 50, 1 March 2014, Pages 359-372
Water Research

Prediction of anaerobic biodegradability and bioaccessibility of municipal sludge by coupling sequential extractions with fluorescence spectroscopy: Towards ADM1 variables characterization

https://doi.org/10.1016/j.watres.2013.10.048Get rights and content

Highlights

  • Anaerobic biodegradability and bioaccessibility prediction of municipal sludge.

  • New organic matter characterization methodology based on 3 dimension fluorescence spectroscopy.

  • Interpretation of the model set up gave information about recalcitrant organic matter.

  • Validation of anaerobic digestion modelling using this new variables characterization.

Abstract

Advanced dynamic anaerobic digestion models, such as ADM1, require both detailed organic matter characterisation and intimate knowledge of the involved metabolic pathways. In the current study, a methodology for municipal sludge characterization is investigated to describe two key parameters: biodegradability and bioaccessibility of organic matter. The methodology is based on coupling sequential chemical extractions with 3D fluorescence spectroscopy. The use of increasingly strong solvents reveals different levels of organic matter accessibility and the spectroscopy measurement leads to a detailed characterisation of the organic matter. The results obtained from testing 52 municipal sludge samples (primary, secondary, digested and thermally treated) showed a successful correlation with sludge biodegradability and bioaccessibility. The two parameters, traditionally obtained through the biochemical methane potential (BMP) lab tests, are now obtain in only 5 days compared to the 30–60 days usually required. Experimental data, obtained from two different laboratory scale reactors, were used to validate the ADM1 model. The proposed approach showed a strong application potential for reactor design and advanced control of anaerobic digestion processes.

Introduction

In the current context, where anaerobic digestion has become a key process for organic matter treatment and energetic valorization, precise control and prediction of process performance is a must-be. For that purpose, advanced mathematical models have to be implemented. Ten years ago, the International Water Association (IWA) specialist group on anaerobic digestion developed the Anaerobic Digestion Model N° 1 (ADM1) (Batstone et al., 2002). The major elements of the model were biological pathways through the identification of limiting step and detailed organic matter characterization. Indeed, according to Angelidaki and Sanders (2004) and Buffiere et al. (2006), the methane yields depend on the nature of substrate characterization. Besides, a key-point for a successful description of a bioprocess using a mathematical model is a good influent characterisation (Huete et al., 2006). From 1969 to 2002, researchers made important efforts to better understand and represent anaerobic digestion as mentioned by a review from Jimenez et al. (2013b). The historical evolution showed a “complexification” of the models, searching to detail more the metabolic pathways, and closer to the reality. The increasing substrate complexity led to a more detailed model input, taking into account the main biochemical families: lipids, proteins and carbohydrates. However, Kleerebezem and van Loosdrecht (2006) admitted that identification of individual substrate concentrations from ADM1 requires specific and not easily available analytical techniques. Many authors have proposed experimental protocol or methodologies in order to characterize ADM1 input variables.

Kleerebezem and van Loosdrecht, 2006, Zaher et al., 2009 and Huete et al. (2006) proposed the techniques which consist on lumping variables with practical analysis. However, there are many assumptions on the carbon and nitrogen inert content, on biochemical fractionation and on the non-biodegradable variables. Another technique consists on mapping activated sludge models variables with the ADM1 variables (Copp et al., 2003, Jones et al., 2008, Nopens et al., 2009, Ekama et al., 2007). The main drawbacks of this method are the knowledge of the whole wastewater treatment plant, the assumptions made about non-biodegradable fraction (i.e. non-biodegradable fraction in activated sludge is the same than in anaerobic digestion) and about biochemical fractions mapping.

The last technique found in the literature is the methane production curve identification (Yasui et al., 2006, Yasui et al., 2008, Girault et al., 2012, Mottet et al., 2013). However, using low ratios of substrates on biomass allowed decreasing batch test duration (i.e. 4–10 days) but it could bring underestimations possible of entire biodegradable fractions and an overestimation of non-biodegradable fraction. In the case of the model developed by Yasui et al. (2008), fractionation has to be adapted for each type of substrate. Besides of a fine characterization of substrates, the limiting step of anaerobic digestion has to be well represented. For municipal sludge, a particulate and complex substrate, hydrolysis of macromolecules has been identified as the limiting step (Vavilin et al., 2008, Yasui et al., 2008, Ramirez et al., 2009). However, the classical ADM1 does not take into account the surface limitation and enzymatic colonization occurring during the hydrolysis of sludge (Vavilin et al., 2008, Mottet et al., 2013). In terms of organic matter characterisation of complex substrates, three concepts are of the most relevance: the bioavailability, the bioaccessibility and the biodegradability. Various definitions of bioavailability are used across many disciplines. Aquino et al. (2008) defined bioavailability as the direct access to the molecule to be degraded by a microorganism. Molecules with a weight below 1000 Da can pass through the cell wall. Due to the complex organisation of sludge, bioaccessibility is mainly defined for the particles by their possible access to the molecule depending on the digestion time, hydrolytic activity and the pre-treatment applied to the sludge (a molecule bioaccessible could become bioavailable with a sufficient HRT). And finally, the biodegradable fraction is the organic matter bioavailable consumed by the biomass.

Both were traditionally obtained from the laboratory batch test used to determine the biochemical methane potential (BMP). Although reliable, it is a time consuming and tedious methodology. Many authors wanted to predict BMP value using characterization information such as biochemical fractionation (Chandler et al., 1980, Gunaseelan, 2009, Mottet et al., 2010), aerobic tests (Cossu and Raga, 2008, Scaglia et al., 2010), elemental analysis (Davidsson et al., 2007, Shanmugam and Horan, 2009) or near infrared spectroscopy (Lesteur et al., 2011). But these techniques are sometimes not applied for sludge or not validated enough by data and they do not take into account the bioaccessibility aspect. The main conclusion, withdrawn from the mentioned above, is the lack of a rapid and pertinent tool to determine in municipal sludge both anaerobic biodegradability and bioaccessibility for hydrolysis prediction and dynamic models implementation.

In the recent years, a new promising technology – the three dimensional excitation emission matrix (3D-EEM) fluorescence spectroscopy – has been widely used for qualitative characterization of Extracellular Polymeric Substances (EPS) extracted from sludge (Esparza-Soto and Westerhoff, 2001, Sheng and Yu, 2006, Li et al., 2008, Henderson et al., 2009, He et al., 2013, Muller et al., 2013). Coupled with sequential extractions of municipal sludge, the technique also revealed information on bioaccessibility (Wang et al., 2010). Results were encouraging to establish the link between complexity, sludge stabilisation degree and accessibility (i.e.: the complexity of the EPS found in dissolved matter is different compared to the bound EPS). Concerning anaerobic digestion, Wan et al. (2012) showed the potential of the fluorescence spectroscopy to be linked with anaerobic biodegradation of cattle and duck manure. They compared digestion and co-digestion of both substrates. Through the 3D-EEM analysis of dissolved organic matter sampled through the digestion time, authors identified molecules remaining after digestion. Thanks to different fluorescence intensity peak ratios (protein-like on fulvic acid-like, and protein-like on humic acid-like), they showed that fulvic acid-like and humic acid-like remained stable during both separated digestion and co-digestion whereas the aromatic proteins tyrosine-like decreased, suggesting hydrolysis of these molecules into non-fluorescent structures.

Based on these observations, this study focuses on developing a performing and practical methodology to characterize anaerobic digestion model input variables linked with the biodegradable and bioaccessible fractions of municipal sludge.

Section snippets

Sludge samples

Fifty two municipal sludge samples were recovered through a large measurement campaign in wastewater treatment plants in Europe. They were of different nature: 6 samples are primary sludge, 25 are secondary sludge, 15 are anaerobic sludge and 8 are thermally treated secondary sludge. Nomenclature were created as following:

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    “S” for sludge and “R” for refusal (from screeners for example)

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    “I”, “II”, “D” or TT temperature for respectively primary, secondary, digested and thermal treatment

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    Alphabetical

Extracted fractions and fluorescence footprint

Fractionation obtained by chemical extractions was performed for all the sludge samples. As primary, secondary, digested and thermally treated sludge were used, analysis of the repartition of the fractions was performed in order to check the variability and profile differences of each sludge. Median of all the sludge samples was about 41% of total COD and half of the sludge samples were extracted with percentages between 38 and 52% of total COD. This extraction yield is not very high (i.e.

Conclusions

The 3D-SE-LPF methodology allowed to measure both the biodegradability and the readily/slowly bioaccessible sludge fractions XRC/XSC. Validation of the methodology was successfully performed through the modelling of two lab scale reactors. To determine optimal design of anaerobic digesters, the use of the PLS results and the ADM1 modified model led to an efficient methane production prediction and reactor performances. Moreover, PLS models could also be used for sensitive analysis to identify

Acknowledgements

The authors wish to thank Arnaud Ponthieux from VERI Limay for his support and advice on this work and Alexis Mottet from INRA Narbonne for the modified model implementation

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