ADM1 based mathematical model of trace element complexation in anaerobic digestion processes

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

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.

Modifications to the anaerobic digestion model no. 1 (ADM1) for enhanced understanding and application of the anaerobic treatment processes - A comprehensive review

Anaerobic digestion (AD) is a promising method for the recovery of resources and energy from organic wastes. Correspondingly, AD modelling has also been developed in recent years. The International Water Association (IWA) Anaerobic Digestion Model No. 1 (ADM1) is currently the most commonly used structured AD model. However, as substrates become more complex and our understanding of the AD mechanism grows, both systematic and specific modifications have been applied to the ADM1. Modified models have provided a diverse range of application besides AD processes, such as fermentation and biogas upgrading processes. This paper reviews research on the modification of the ADM1, with a particular focus on processes, kinetics, stoichiometry and parameters, which are the major elements of the model. The paper begins with a brief introduction to the ADM1, followed by a summary of modifications, including extensions to the model structure, modifications to kinetics (including inhibition functions) and stoichiometry, as well as simplifications to the model. The paper also covers kinetic parameter estimation and validation of the model, as well as practical applications of the model to a variety of scenarios. The review highlights the need for improvements in simulating AD and biogas upgrading processes, as well as the lack of full-scale applications to other substrates besides sludge (such as food waste and agricultural waste). Future research directions are suggested for model development based on detailed understanding of the anaerobic treatment mechanisms, and the need to recover of valuable products.

CFD simulation of anaerobic granular sludge reactors: A review

Anaerobic digestion processes can generate renewable energy in the form of biogas while treating organic wastewater. The generation of biogas within anaerobic digestion systems is directly linked to the mixing conditions inside the reactors. In high-rate reactors such as the upflow anaerobic sludge blanket (UASB) reactor, the expanded granular sludge bed (EGSB) reactor and the internal circulation (IC) reactor, the hydrodynamic behaviour will depend on the interactions between the wastewater, the biogas, and the biomass granules. Over the past few years, various researchers have used computational fluid dynamics (CFD) to study the hydrodynamic behaviour in these types of reactors. This review aims to present and critically discuss the state of the art in the use of CFD applied to anaerobic granular sludge reactors (AGSRs). It briefly introduces and discusses the various aspects of modelling. It also reviews the various papers which used CFD to model these reactors and critically analyses the models used for the simulations in terms of general approaches and single-phase vs multiphase studies. The methods used in the validation of the CFD models are also described and discussed. Based on the findings, the challenges and future perspectives for the CFD modelling of AGSRs are discussed and gaps in the knowledge are identified.

Dynamic modelling the effects of ionic strength and ion complexation on trace metal speciation during anaerobic digestion

Dosing trace metals into anaerobic digestors is proven to improve biogas production rate and yield by stimulating microorganisms involved in the metabolic pathways. Trace metal effects are governed by metal speciation and bioavailability. Though chemical equilibrium speciation models are well-established and widely used to understand metal speciation, the development of kinetic models considering biological and physicochemical processes has recently gained attention. This work proposes a dynamic model for metal speciation during anaerobic digestion which is based on a system of ordinary differential equations aimed to describe the kinetics of biological, precipitation/dissolution, gas transfer processes and, a system of algebraic equations to define fast ion complexation processes. The model also considers ion activity corrections to define effects of ionic strength. Results from this study shows the inaccuracy in predicting trace metal effects on anaerobic digestion by typical metal speciation models and the significance of considering non-ideal aqueous phase chemistry (ionic strength and ion pairing/complexation) to define speciation and metal labile fractions. Model results show a decrease in metal precipitation and increase in metal dissolved fraction and methane production yield with increase in ionic strength. Capability of the model to dynamically predict trace metal effects on anaerobic digestion under different conditions, like changing dosing conditions and initial iron to sulphide ratio, was also tested and verified. Dosing iron increases methane production and decreases hydrogen sulphide production. However, when iron to sulphide ratio is greater than 1, methane production decreases due to increase in dissolved iron which reaches inhibitory concentration levels.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Considering syntrophic acetate oxidation and ionic strength improves the performance of models for food waste anaerobic digestion

Current mechanistic anaerobic digestion (AD) models cannot accurately represent the underlying processes occurring during food waste (FW) AD. This work presents an update of the Anaerobic Digestion Model no. 1 (ADM1) to provide accurate estimations of free ammonia concentrations and related inhibition thresholds, and model syntrophic acetate oxidation as acetate-consuming pathway. A modified Davies equation predicted NH₃ concentrations and pH more accurately, and better estimated associated inhibitory limits. Sensitivity analysis results showed the importance of accurate disintegration kinetics and volumetric mass transfer coefficients, as well as volatile fatty acids (VFAs) and hydrogen uptake rates. In contrast to the default ADM1, the modified ADM1 could represent methane production and VFA profiles simultaneously (particularly relevant for propionate uptake). The modified ADM1 was also able to predict the predominant acetate-consuming and methane-producing microbial clades. Modelling results using data from reactors dosed with granular activated carbon showed that this additive improves hydrogen uptake.

Enhancing anaerobic digestion process with addition of conductive materials

Anaerobic digestion is widely used for the treatment of wastewater for its low costs and bioenergy production, but the performances of anaerobic digestion often need improving in practical applications. The addition of conductive materials could lead to direct interspecies electron transfer (DIET) among the anaerobic microorganisms, and consequently enhance the efficiencies of anaerobic digestion. In this paper, the effects of DIET via conductive materials on chemical organic demand (COD) removal, volatile fatty acid (VFA) consumption and methane production were reviewed. The reports on the increase of conductive microorganisms due to the addition of conductive materials were discussed. Results regarding activities of microorganisms and morphology and properties of sludge were described and commented, and future research needs were also proposed which included better understanding of the roles of DIET in each step of anaerobic digestion, mechanisms of metabolism of pollutants in DIET-established systems and inhibition of excessive dosage of conductive materials.

Modelling of dark fermentation of glucose and sour cabbage

In the article, modified Anaerobic Digestion Models 1 (ADM-1) was tested for modelling dark fermentation for hydrogen production. The model refitting was done with the Euler method. The new model was based on sets of differential equations. The model was checked for hydrogen production from sour cabbage in batch and semi-batch in 5 g VSS (volatile solid suspension)/L and at the semi-batch process from glucose at 5 and 10 g VSS/L. Added parameters determined the conversion of a substrate, hydrogen production, and stress parameters. In the case of a semi-batch process, for one month, cumulative hydrogen production from sour cabbage of 5 g VSS/L was 0.9 L of cumulative hydrogen volume and from glucose 5 g VSS/L (in case of feeding 2 g VSS/L every two days) 2.5 L of cumulative hydrogen volume. At the bacterial population level, hydrogen production was a continuous process at an adequate range of population size and environmental parameters.

Anaerobic co-digestion of agricultural wastes toward circular bioeconomy

A huge amount of agricultural wastes and waste activated-sludge are being generated every year around the world. Anaerobic co-digestion (AcD) has been considered as an alternative for the utilization of organic matters from such organic wastes by producing bioenergy and biochemicals to realize a circular bioeconomy. Despite recent advancement in AcD processes, the effect of feedstock compositions and operating conditions on the biomethane production processe has not been critically explored. In this paper, we have reviewed the effects of feedstock (organic wastes) characteristics, including particle size, carbon-to-nitrogen ratio, and pretreatment options, on the performance of an anaerobic digestion process. In addition, we provided an overview of the effect of key control parameters, including retention time, temperature, pH of digestate, volatile fatty acids content, total solids content, and organic loading rate. Lastly, based on the findings from the literature, we have presented several perspectives and prospects on priority research to promote AcD to a steppingstone for a circular bioeconomy.

A general framework to model the fate of trace elements in anaerobic digestion environments

Due to the multiplicity of biogeochemical processes taking place in anaerobic digestion (AD) systems and limitations of the available analytical techniques, assessing the bioavailability of trace elements (TEs) is challenging. Determination of TE speciation can be facilitated by developing a mathematical model able to consider the physicochemical processes affecting TEs dynamics. A modeling framework based on anaerobic digestion model no 1 (ADM1) has been proposed to predict the biogeochemical fate TEs in AD environments. In particular, the model considers the TE adsorption–desorption reactions with biomass, inerts and mineral precipitates, as well as TE precipitation/dissolution, complexation reactions and biodegradation processes. The developed model was integrated numerically, and numerical simulations have been run to investigate the model behavior. The simulation scenarios predicted the effect of (i) organic matter concentration, (ii) initial TEs concentrations, (iii) initial Ca–Mg concentrations, (iv) initial EDTA concentration, and (v) change in TE binding site density, on cumulative methane production and TE speciation. Finally, experimental data from a real case continuous AD system have been compared to the model predictions. The results prove that this modelling framework can be applied to various AD operations and may also serve as a basis to develop a model-predictive TE dosing strategy.

Surrogate based Global Sensitivity Analysis of ADM1-based Anaerobic Digestion Model

In order to calibrate the model parameters, Sensitivity Analysis routines are mandatory to rank the parameters by their relevance and fix to nominal values the least influential factors. Despite the high number of works based on ADM1, very few are related to sensitivity analysis. In this study Global Sensitivity Analysis (GSA) and Uncertainty Quantification (UQ) for an ADM1-based Anaerobic Digestion Model have been performed. The modified version of ADM-based model selected in this study was presented by Esposito and co-authors in 2013. Unlike the first version of ADM1, focused on sewage sludge degradation, the model of Esposito is focused on organic fraction of municipal solid waste digestion. It his recalled that in many applications the hydrolysis is considered the bottleneck of the overall anaerobic digestion process when the input substrate is constituted of complex organic matter. In Esposito's model a surfaced based kinetic approach for the disintegration of complex organic matter is introduced. This approach allows to better model the disintegration step taking into account the effect of particle size distribution on the digestion process. This model needs thus GSA and UQ to pave the way for further improvements and reach a deep understanding of the main processes and leading input factors. Due to the large number of parameters to be analyzed a first preliminary screening analysis, with the Morris' Method, has been conducted. Since two quantities of interest (QoI) have been considered, the initial screening has been performed twice, obtaining two set of parameters containing the most influential factors in determining the value of each QoI. A surrogate of ADM1 model has been defined making use of the two defined quantities of interest. The output results from the surrogate model have been analyzed with Sobol' indices for the quantitative GSA. Finally, uncertainty quantification has been performed. By adopting kernel smoothing techniques, the Probability Density Functions of each quantity of interest have been defined.

Influence of particle scattering on photo biochemical transformation process of direct absorption methane digester

An outdoor anaerobic fermentation reactor loses a significant amount of energy due to heat dissipation to the surrounding environment. The digester of direct absorption biogas can effectively utilize solar energy and scattering of the medium to enhance reaction temperature, which can promote anaerobic fermentation of microorganisms. A numerical model for the direct absorption methane digester was established to investigate the mechanism of photo biochemical transformation. The average relative values of simulated results were 4.1% and 9.6%, indicating that the model can effectively simulate the heat transfer process of biogas slurry under solar irradiation. Decreasing the albedo and increasing the effect of forward scattering of small particles can improve the regenerative performance and biogas production of digester. Increasing the backward scattering effect of small particles limited biogas fermentation. Scattering distribution had bigger effects on the rates of biogas and propionic acid production than those of albedo.

Dynamics of bacterial communities and substrate conversion during olive-mill waste dark fermentation: Prediction of the metabolic routes for hydrogen production

The aim of this work was to study the biological catalysts and possible substrate conversion routes in mesophilic dark fermentation reactors aimed at producing H₂ from olive mill wastewater. Bacillus and Clostridium were the most abundant phylotypes during the rapid stage of H₂ production. Chemical analyses combined with predictive functional profiling of the bacterial communities indicated that the lactate fermentation was the main H₂-producing route. In fact, during the fermentation process, lactate and acetate were consumed, while H₂ and butyrate were being produced. The fermentation process was rich in genes that encode enzymes for lactate generation from pyruvate. Lactate conversion to butyrate through the generation of pyruvate produced H₂ through the recycling of electron carriers via the pyruvate ferredoxin oxydoreductase pathway. Overall, these findings showed the synergy among lactate-, acetate- and H₂-producing bacteria, which complex interactions determine the H₂ production routes in the bioreactors.

Biohydrogen and poly-β-hydroxybutyrate production by winery wastewater photofermentation: Effect of substrate concentration and nitrogen source

The applicability and convenience of biohydrogen and poly-β-hydroxybutyrate production through single-stage photofermentation of winery wastewater is demonstrated in the present study. Experiments are conducted using a purple non-sulfur bacteria mixed consortium, subject to variable nutrient conditions, to analyze the effect of initial chemical oxygen demand and the available nitrogen source on the metabolic response. Results show that winery wastewater is a promising substrate for photofermentation processes, despite the presence of inhibiting compounds such as phenolics. Nonetheless, the initial chemical oxygen demand must be carefully controlled to maximize hydrogen production. Up to 468 mL L^-1 of hydrogen and 203 mg L^-1 of poly-β-hydroxybutyrate can be produced starting from an initial chemical oxygen demand of 1500 mg L^-1. The used nitrogen source may direct substrate transformation through different metabolic pathways. Interestingly, the maximum production of both hydrogen and poly-β-hydroxybutyrate occurred when glutamate was used as the nitrogen source.

A simultaneous assessment of organic matter and trace elements bio-accessibility in substrate and digestate from an anaerobic digestion plant

This study evaluates a simultaneous assessment of organic matter (OM) and trace elements (TE) bio-accessibility in substrate and digestate from a full-scale anaerobic digester by a sequential OM extraction method. Simultaneous release of TE was determined along with the extraction of different OM fractions and the effects of extracting reagents on characteristics of OM were evaluated by nuclear magnetic resonance (NMR) spectroscopy. The reagents used for sequential extraction of OM were not enough selective. However, proteins were particularly removed by 0.1 M NaOH, while 72% H₂SO₄ mainly extracted hemicellulose and cellulose. The OM fractionation allowed for simultaneous extraction of >60% of total As, Cd, Co, Fe, Mn, Ni and Zn, while the extraction was limited for Al, Cr, Cu, Mo, and Pb. In substrate, >50% of total As, Co, Mn and Ni and <40% of total Fe, Zn and Mo were identified in bio-accessible fractions. In digestate, all elements demonstrated poor bio-accessibility except for As.

Repeated-Batch Fermentation of Cheese Whey for Semi-Continuous Lactic Acid Production Using Mixed Cultures at Uncontrolled pH

The paper investigates mixed-culture lactate (LA) fermentation of cheese whey (CW) in order to verify the possibility of using waste materials as feedstock to produce a product with high economic potential. The fermentation performance of two reactors operating in repeated-batch mode under uncontrolled pH conditions and various hydraulic retention time and feeding conditions was evaluated in terms of LA production. Five experimental phases were conducted. The hydraulic retention time (HRT) was varied from 1 to 4 days to verify its effect on the process performance. The best results, corresponding to the maximum LA concentration (20.1 g LA/L) and the maximum LA yield (0.37 g chemical oxygen demand (COD)(LA)/g COD(CW)), were reached by feeding the reactors with cheese whey alone and setting the HRT to 2 days. The maximum productivity of lactic acid (10.6 g LA/L/day) was observed when the HRT was decreased to 1 day.

A Preliminary Study of the Effect of Bioavailable Fe and Co on the Anaerobic Digestion of Rice Straw

Rice straw is an abundant and sustainable substrate for anaerobic digestion (AD), but it is often deficient in essential trace elements (TEs) for proper microbial growth and metabolism. A lack of TEs leads to AD imbalances and suboptimal biogas yields. However, the total TE concentration is not a sufficient indicator of the amount of TEs available to the microorganisms. Therefore, this study investigated the degree of bioavailability of iron (Fe) and cobalt (Co) during the AD of rice straw, and correlated it to the biomethane yields and volatile fatty acids (VFAs) produced. When the two TEs were dosed at 205 µg Fe/g TS and 18 µg Co/g TS of rice straw, the biomethane production was approximately 260 mL CH4/g VS, i.e., similar to that obtained when Fe and Co were not added. Despite an increased bioavailable fraction of 23 and 48% for Fe and Co, respectively, after TEs addition, the AD performance was not enhanced. Moreover, VFAs did not exceed 250 mg HAc/L both in the presence and absence of added TEs, confirming no enhancement of the methanogenesis step. Therefore, the bioavailability of Fe and Co was not a limiting factor for the biomethane production at low total VFAs concentration.