Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions

Improved organic matter biodegradation through pulsed H2 injections during in situ biomethanation

During in situ biomethanation, microbial communities can convert complex Organic Matter (OM) and H2 into CH4. OM biodegradation was compared between Anaerobic Digestion (AD) and in situ biomethanation, in semi-continuous processes, using two inocula from the digester (D) and the post-digester (PoD) of an AD plant. The impact of H2 on OM degradation was assessed using a fractionation method. Operational parameters included 20 days of hydraulic retention time and 1.5 gVS.L-1.d-1 of organic loading rate. During in situ biomethanation, 485 NmL of H2 were injected for each feeding (3 times a week). Maximum organic COD removal was 0.6 gCOD in AD control and at least 1.6 gCOD for in situ biomethanation. Therefore, COD removal was 2.5 times higher with H2 injections. These results bring out the potential of H2 injections during AD, not only for CO2 consumption but also for better OM degradation.

Modelling of anaerobic digestion of microalgae biomass: Effect of overloading perturbation

Anaerobic digestion (AD) of microalgae is an intriguing approach for bioenergy production. The scaling-up of AD presents a significant challenge due to the systematic efficiency losses related to process instabilities. To gain a comprehensive understanding of AD behavior, this study assessed a modified version of the anaerobic digestion model No1 (ADM1) + Contois kinetics to represent microalgae AD impacted by overloading. To this end, two new inhibition functions were implemented: inhibition by acetate for acidogenesis/acetogenesis and total volatile fatty acids for hydrolysis. This proposed ADM1 modification (including Contois kinetics) simulated AD behavior during the stable, disturbed and recovery periods, showing that the inhibition functions described in the original ADM1 cannot explain the AD performance under one of the most common perturbations at industrial scale (overloading). The findings underscore the importance of refining the inhibitions present in original ADM1 to better capture and predict the complexities of microalgae AD against overloading.

Analysis of biogas production from sewage sludge combining BMP experimental assays and the ADM1 model

The Anaerobic Digestion Model No. 1 (ADM1) was employed to simulate methane (CH4) production in an anaerobic reactor (AR), and the associated bench-scale biochemical methane potential (BMP) assay, having sewage sludge (SWS) from a municipal wastewater treatment plant (WWTP) as feedstock. The SWS presented the following physical-chemical characteristics: pH (7.4-7.6), alkalinity (2,382 ± 100 mg CaCO3 L-1), tCOD (21,903 ± 1,000 mg L-1), TOC (895 ± 100 mg L-1), TS, TVS, and VSS (2.0%, 1.1%, and 0.8%, respectively). The BMP assay was conducted in six replicates under anaerobic mesophilic conditions (37 ± 0.1°C) for 11 days with a CH4 yield registered of 137.6 ± 6.39 NmL CH4 or 124 ± 6.72 CH4 g-1 VS-1. When the results obtained with the BMP bench-scale reactors were compared to the output generated with computational data by the ADM1 model having as input data the same initial sewage tCOD, similar cumulative CH4 production curves were obtained, indicating the accuracy of the ADM1 model. This approach allowed the characterization of the sludge and estimation of its biogas production potential. The combination of BMP assays, experimental data, and ADM1 model simulations provided a framework for studying anaerobic digestion (AD) processes.

Production of short-chain fatty acids (SCFAs) as chemicals or substrates for microbes to obtain biochemicals

Carboxylic acids have become interesting platform molecules in the last years due to their versatility to act as carbon sources for different microorganisms or as precursors for the chemical industry. Among carboxylic acids, short-chain fatty acids (SCFAs) such as acetic, propionic, butyric, valeric, and caproic acids can be biotechnologically produced in an anaerobic fermentation process from lignocellulose or other organic wastes of agricultural, industrial, or municipal origin. The biosynthesis of SCFAs is advantageous compared to chemical synthesis, since the latter relies on fossil-derived raw materials, expensive and toxic catalysts and harsh process conditions. This review article gives an overview on biosynthesis of SCFAs from complex waste products. Different applications of SCFAs are explored and how these acids can be considered as a source of bioproducts, aiming at the development of a circular economy. The use of SCFAs as platform molecules requires adequate concentration and separation processes that are also addressed in this review. Various microorganisms such as bacteria or oleaginous yeasts can efficiently use SCFA mixtures derived from anaerobic fermentation, an attribute that can be exploited in microbial electrolytic cells or to produce biopolymers such as microbial oils or polyhydroxyalkanoates. Promising technologies for the microbial conversion of SCFAs into bioproducts are outlined with recent examples, highlighting SCFAs as interesting platform molecules for the development of future bioeconomy.

A novel approach for the fractionation of organic components and microbial degraders in ADM1 and model validation based on the methanogenic potential

The anaerobic digestion model No 1 (ADM1), with fixed fractions of the substrate components, is currently used to simulate methane production during the anaerobic digestion (AD) of waste activated sludge (WAS). However, the goodness-of-fit for the simulation is not ideal due to the different characteristics of WAS from different regions. In this study, a novel methodology based on a modern instrumental analysis and 16S rRNA gene sequence analysis for the fractionation of organic components and microbial degraders in the WAS is investigated to modify the fractions of the components in the ADM1. The combination of Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance (NMR) analyses were used to achieve a rapid and accurate fractionation of the primary organic matters in the WAS that was verified using both the sequential extraction method and the excitation-emission matrix (EEM). The protein, carbohydrate, and lipid contents in the four different sludge samples measured using the above combined instrumental analyses were 25.0 - 50.0%, 2.0 - 10.0%, and 0.9 - 2.3%. The microbial diversity based on 16S rRNA gene sequence analysis was utilized to re-set the initial fractions of the microbial degraders in the ADM1. A batch experiment was utilized to further calibrate the kinetic parameters in the ADM1. Based on the above optimization of the stoichiometric and kinetic parameters, the ADM1 with full parameter modification for WAS (ADM1-FPM) simulated the methane production of the WAS very well with a Theil's inequality coefficient (TIC) of 0.049, which was increased by 89.8% than that of the default ADM1 fit. The proposed approach, with its rapid and reliable performance, demonstrated a strong application potential for the fractionation of organic solid waste and the modification of ADM1, which contributed to a better simulation of methane production during the AD of organic solid wastes.

SoilFract: A mechanistic model accounting for the fate of exogenous organic matter in soil carbon and nitrogen cycles

As its use in agriculture grows, the fate of digestate in soil raises concerns on many different levels. In particular, the degradability of its organic matter when spread on soil is still an ongoing topic. In an effort to better understand the processes and dynamics of digestate soil incubation, C and N mineralization kinetics obtained in 358 days long laboratory incubations during decomposition of digestates were simulated using a dynamic model. The model includes twelve compartments related through processes including 18 parameters. The main novelty of this model is the use of accessibility-related variables to describe the fate of exogenous organic matter in soil, thus enabling a detailed understanding of its outcome in soil. Model calibration on cattle manure digestate incubation resulted in the estimation of parameter values. The newly calibrated model was then tested on an energy crop digestate incubation experiment. The model was able to reproduce accurately the experimental behavior of most variables.

Predicting the Stability of Organic Matter Originating from Different Waste Treatment Procedures

Recycling organic wastes into farmland faces a double challenge: increasing the carbon storage of soil while mitigating CO₂ emission from soil. Predicting the stability of organic matter (OM) in wastes and treatment products can be helpful in dealing with this contradiction. This work proposed a modeling approach integrating an OM characterization protocol into partial least squares (PLS) regression. A total of 31 organic wastes, and their products issued from anaerobic digestion, composting, and digestion-composting treatment were characterized using sequential extraction and three-dimension (3D) fluorescence spectroscopy. The apportionment of carbon in different fractions and fluorescence spectra revealed that the OM became less accessible and biodegradable after treatments, especially the composting. This was proven by the decrease in CO₂ emission from soil incubation. The PLS model successfully predicted the stability of solid digestate, compost, and compost of solid digestate in the soil by using only the characterized variables of non-treated wastes. The results suggested that it would be possible to predict the stability of OM from organic wastes after different treatment procedures. It is helpful to choose the most suitable and economic treatment procedure to stabilize labile organic carbon in wastes and hence minimize CO₂ emission after the application of treatment products to the soil.

Evaluating the impact of substrate addition for anaerobic co-digestion on biogas production and digestate quality: The case of deinking sludge

To reduce greenhouse gas emissions from organic waste, anaerobic digestion has created new opportunities for energy and nutrient recovery from these wastes. However, the use of certain organic wastes in anaerobic digestion is limited due to their atypical physicochemical characteristics (e.g. unbalanced carbon to nitrogen ratio, high ash concentration). Deinking sludge is a residue from the paper recycling industry and is one of such substrates. This study aims at evaluating the impact of deinking sludge (DS) addition into a conventional co-digestion mixture on methane production and digestate quality. To this end, an integrated method was proposed, combining the analysis of physicochemical and biodegradability characteristics with parsimonious modeling using the SYS-Metha tool. The measured characteristics of the deinking sludge showed that its potential use in mono-digestion conditions is very limited. When co-digested with food waste and municipal sludge, no significant synergies or antagonisms were found. Based on these experiments, model simulations were executed to determine the optimal conditions for co-digestion with food waste and municipal sludge. A maximum of 22% of deinking sludge on a fresh mass basis can be added into a co-digestion mixture to achieve proper wet anaerobic digestion conditions. Regarding digestate quality, the addition of DS reduced nutrient and contaminants concentrations, which have an impact on digestate management, particularly for land application. Overall, the proposed methodology in this study allows determining optimal co-digestion mixtures and highlighted the limits needing further investigation under pilot/real conditions.

Prediction of organic matter accessibility and complexity in anaerobic digestates

Further characterization to properly assess the fate of organic matter quality during anaerobic digestion and organic carbon mineralization in soils is required. Organic matter quality based on its accessibility and complexity was employed to successfully classify 28 substrate/digestate pairs through principal components and hierarchical clustering analysis. The two first components explained 58.02% of the variability and four main groups were separated according to the feedstock type. A decrease in the accessibility (16-66%) and an increase in the complexity (34-98%) of the most accessible fractions was noticed. Besides, an increase of non-biodegradable compounds (17-66%) was globally observed after anaerobic digestion. The observed trends in the conversion of organic matter during anaerobic digestion have allowed to fill the gap in the modeling of the anaerobic digestion process chain. Indeed, partial least squares regressions have accurately predicted the organic matter quality of digestates from their inputs (R² = 0.831, Q² = 0.593) although the digester operational conditions (temperature and hydraulic retention time) were non-explicative enough. As a novel approach, the predicted digestate quality was used to feed a partial least squares regression model previously developed to predict organic carbon mineralization in soil. The combined models have predicted experimental organic carbon mineralization in soil (R² = 0.697) with a model quality similar to the model for organic carbon mineralization in soil (R² = 0.894). This is the first study that has successfully conceived an additional step in the prediction of organic matter fate from raw substrate before anaerobic digestion to soil carbon mineralization.

Optimizing ADM1 Calibration and Input Characterization for Effective Co-Digestion Modelling

Anaerobic co-digestion in wastewater treatment plants is looking increasingly like a straightforward solution to many issues arising from the operation of mono-digestion. Process modelling is relevant to predict plant behavior and its sensitivity to operational parameters, and to assess the feasibility of simultaneously feeding a digester with different organic wastes. Still, much work has to be completed to turn anaerobic digestion modelling into a reliable and practical tool. Indeed, the complex biochemical processes described in the ADM1 model require the identification of several parameters and many analytical determinations for substrate characterization. A combined protocol including batch Biochemical Methane Potential tests and analytical determinations is proposed and applied for substrate influent characterization to simulate a pilot-scale anaerobic digester where co-digestion of waste sludge and expired yogurt was operated. An iterative procedure was also developed to improve the fit of batch tests for kinetic parameter identification. The results are encouraging: the iterative procedure significantly reduced the Theil’s Inequality Coefficient (TIC), used to evaluate the goodness of fit of the model for alkalinity, total volatile fatty acids, pH, COD, volatile solids, and ammoniacal nitrogen. Improvements in the TIC values, compared to the first iteration, ranged between 30 and 58%.

Assessment of alkaline stabilization processes in industrial waste streams using a model-based approach

Chemical conditioning prior to disposal is a common practice in biotech companies to stabilize the biological waste generated during production. Nevertheless, the state of the art models used to analyze management strategies in water treatment systems (WTS) do not include the effect of high alkaline conditions during bio-solids processing. In this paper, the prediction capabilities of a novel model-based approach describing the effect of quicklime addition (CaO) on the waste streams of an industrial WTS is assessed. Two measuring campaigns were carried out taking samples of TSS, VSS and total/soluble COD, N, P, S and multiple metals before and after chemical stabilization, and dewatering under and overflow. Mass balances were set up and Sankey diagrams were generated to represent the occurrence, transformation and fate of the major compounds within the studied facility. A simulation model was used to predict plant at different locations. Next, a scenario analysis was carried out in order to assess potential alternatives to the current operational practice. The resulting mass balances show a mismatch between the system's input and output up to 17%. It was also possible to identify different types of compound-behavior depending on the effect that high pH induced on the soluble and particulate fractions: hydrolysis, precipitation and unaltered. Model predictions and measurements differed 9.6% (steady state) and 12.4% (dynamic state) respectively. Finally, in the scenario analysis, the model suggested that the change from quicklime to sodium hydroxide (NaOH) would increase the quantity of organics in the dewatered cake (+23%), but with a considerable increase in chemical consumption (+50%). The selective stabilization of the incoming streams has the lowest use of chemicals (-30%) and reduces the load of CODsol (-13%) and TNsol (-14%) recirculated to the water line of the WWTP.

Molecular Microbial Community Analysis as an Analysis Tool for Optimal Biogas Production

The microbial diversity in anaerobic digestion (AD) is important because it affects process robustness. High-throughput sequencing offers high-resolution data regarding the microbial diversity and robustness of biological systems including AD; however, to understand the dynamics of microbial processes, knowing the microbial diversity is not adequate alone. Advanced meta-omic techniques have been established to determine the activity and interactions among organisms in biological processes like AD. Results of these methods can be used to identify biomarkers for AD states. This can aid a better understanding of system dynamics and be applied to producing comprehensive models for AD. The paper provides valuable knowledge regarding the possibility of integration of molecular methods in AD. Although meta-genomic methods are not suitable for on-line use due to long operating time and high costs, they provide extensive insight into the microbial phylogeny in AD. Meta-proteomics can also be explored in the demonstration projects for failure prediction. However, for these methods to be fully realised in AD, a biomarker database needs to be developed.