Modified ADM1 for modeling free ammonia inhibition in anaerobic acidogenic fermentation with high-solid sludge

Improved biohydrogen production by co-fermentation of corn straw and excess sludge: Insights into biochemical process, microbial community and metabolic genes

The global climate change mainly caused by fossil fuels combustion promotes that zero-carbon hydrogen production through eco-friendly methods has attracted attention in recent years. This investigation explored the biohydrogen production by co-fermentation of corn straw (CS) and excess sludge (ES), as well as comprehensively analyzed the internal mechanism. The results showed that the optimal ratio of CS to ES was 9:1 (TS) with the biohydrogen yield of 101.8 mL/g VS, which was higher than that from the mono-fermentation of CS by 1.0-fold. The pattern of volatile fatty acids (VFAs) indicated that the acetate was the most preponderant by-product in all fermentation systems during the biohydrogen production process, and its yield was improved by adding appropriate dosage of ES. In addition, the content of soluble COD (SCOD) was reduced as increasing ES, while concentration of NH4+-N showed an opposite tendency. Microbial community analysis revealed that the microbial composition in different samples showed a significant divergence. Trichococcus was the most dominant bacterial genus in the optimal ratio of 9:1 (CS/ES) fermentation system and its abundance was as high as 41.8%. The functional genes prediction found that the dominant metabolic genes and hydrogen-producing related genes had not been significantly increased in co-fermentation system (CS/ES = 9:1) compared to that in the mono-fermentation of CS, implying that enhancement of biohydrogen production by adding ES mainly relied on balancing nutrients and adjusting microbial community in this study. Further redundancy analysis (RDA) confirmed that biohydrogen yield was closely correlated with the enrichment of Trichococcus.

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

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.

Fostering the valorization of organic wastes into carboxylates by a computer-aided design tool

The carboxylate platform has the potential to constitute an outstanding opportunity for converting organic wastes into chemicals and other value-added products within a circular economy framework. However, its development is still hampered by technological and financial constraints due to difficulties at forecasting the carboxylates yields by different wastes. This work provides a framework that can be the key to foster circular economy and bridge the development risks, allowing early-stage evaluation of process performance. This framework, which is implemented as a computer-aided design tool, is comprised by: (i) a library of substrates including their characterization and appropriate kinetic parameter selection, (ii) an integral kinetic and stoichiometric model which solves both identified gaps regarding the disintegration mechanisms and the acidogenic stoichiometry variability in the anaerobic mono and cofermentation of complex organic wastes, and (iii) a set of indicators to interpret simulation results and assist the decision making; and presents a showcase of applications supported by two case studies. These case studies show that the optimal conditions to steer VFA spectrum towards odd-chain VFA in MCF of regrind pasta are neutral pH (6.5-7) and a relatively low HRT (3-4 days), while cofermentation of tuna canning wastewater and regrind pasta follows interactive mechanisms that cannot be captured by a "naïve approach", i.e. by adding up the individual contributions. Finally, it is discussed how value chain actors with different interests can benefit from the proposed tool: identifying technical, economic, and environmental bottlenecks, and proposing innovative solutions prior to costly lab research and piloting.

A comprehensive review on current status and future perspectives of microbial volatile fatty acids production as platform chemicals

Volatile fatty acids (VFA), the secondary metabolite of microbial fermentation, are used in a wide range of industries for production of commercially valuable chemicals. In this review, the fermentative production of VFAs by both pure as well mixed microbial cultures is highlighted along with the strategies for enhancing the VFA production through innovations in existing approaches. Role of conventionally applied tools for the optimization of operational parameters such as pH, temperature, retention time, organic loading rate, and headspace pressure has been discussed. Furthermore, a comparative assessment of above strategies on VFA production has been done with alternate developments such as co-fermentation, substrate pre-treatment, and in situ removal from fermented broth. The review also highlights the applications of different bioreactor geometries in the optimum production of VFAs and how metagenomic tools could provide a detailed insight into the microbial communities and their functional attributes that could be subjected to metabolic engineering for the efficient production of VFAs.

Application of machine learning in anaerobic digestion: Perspectives and challenges

Anaerobic digestion (AD) is widely adopted for remediating diverse organic wastes with simultaneous production of renewable energy and nutrient-rich digestate. AD process, however, suffers from instability, thereby adversely affecting biogas production. There have been significant efforts in developing strategies to control the AD process to maintain process stability and predict AD performance. Among these strategies, machine learning (ML) has gained significant interest in recent years in AD process optimization, prediction of uncertain parameters, detection of perturbations, and real-time monitoring. ML uses inductive inference to generalize correlations between input and output data, subsequently used to make informed decisions in new circumstances. This review aims to critically examine ML as applied to the AD process and provides an in-depth assessment of important algorithms (ANN, ANFIS, SVM, RF, GA, and PSO) and their applications in AD modeling. The review also outlines some challenges and perspectives of ML, and highlights future research directions.

Biochar amendment to advance contaminant removal in anaerobic digestion of organic solid wastes: A review

Anaerobic digestion (AD) has been widely applied to convert organic solid wastes into biogas, a renewable energy, and digestate, a bio-fertilizer, to sustain waste management. Nevertheless, several vexing contaminants in OSWs restrict digestate application in agriculture. Biochar has been evidenced to effectively improve AD by promoting organic biodegradation and alleviating the accumulation of inhibitory substances (e.g. ammonia and volatile fatty acids). Furthermore, biochar could advance contaminant removal in AD given its highly porous, conductive and alkaline features. Thus, this review aims to highlight the role of biochar amendment to advance contaminant removal in AD of OSWs. Key contaminants, such as antibiotics, heavy metals, microplastics, polycyclic aromatic hydrocarbons, furfural and 5-hydroxy methyl furfural (5-HMF) that ubiquitously present in OSWs were demonstrated. The underlying mechanisms of biochar to amend the removal of these contaminants by AD were discussed. Furthermore, future perspectives to the development of biochar-assisted AD for OSWs treatment were provided.

Methane production from acetate, formate and H2/CO2 under high ammonia level: Modified ADM1 simulation and microbial characterization

This study evaluated the methanogenic performance of typical substrates (acetate, formate, H₂/CO₂, and glucose) under low and high ammonia levels and the Anaerobic Digestion Model No.1 (ADM1) was extended and modified for better simulation and understanding of the process. Formate-utilizing and hydrogen-utilizing methanogenesis showed stronger ammonia resistance than acetate-utilizing methanogenesis (13-23% vs. 34% decrease in methane production (MP)). Model extension, based on foundational experiments fed with three typical precursors (R² > 0.92), was then validated with glucose degradation experiments, and satisfactory predictions of MP and total volatile fatty acids were obtained (R² > 0.91). Based on the modified ADM1, the carbon fluxes of glucose degradation were determined, and formate-utilizing methanogenesis showed its importance with a 28-34% contribution of the total methanation, becoming the dominant pathway under high ammonia level. Formate-utilizing methanogenesis also had a thermodynamic advantage among the three pathways. 16S rRNA sequencing suggested a homology between the hydrogen-utilizing and formate-utilizing methanogens. Methanobacterium and Methanobrevibacter were found to be key methanogens, and their enrichment under high ammonia level confirmed the stronger ammonia tolerance of formate-utilizing and hydrogen-utilizing methanogenesis. The microbial characterization and modified ADM1 simulations supported each other.

A Review of the State of the Art of Biomethane Production: Recent Advancements and Integration of Renewable Energies

Anaerobic Digestion (AD) is a well-established process that is becoming increasingly popular, especially as a technology for organic waste treatment; the process produces biogas, which can be upgraded to biomethane, which can be used in the transport sector or injected into the natural gas grid. Considering the sensitivity of Anaerobic Digestion to several process parameters, mathematical modeling and numerical simulations can be useful to improve both design and control of the process. Therefore, several different modeling approaches were presented in the literature, aiming at providing suitable tools for the design and simulation of these systems. The purpose of this study is to analyze the recent advancements in the biomethane production from different points of view. Special attention is paid to the integration of this technology with additional renewable energy sources, such as solar, geothermal and wind, aimed at achieving a fully renewable biomethane production. In this case, auxiliary heat may be provided by solar thermal or geothermal energy, while wind or photovoltaic plants can provide auxiliary electricity. Recent advancements in plants design, biomethane production and mathematical modeling are shown in the paper, and the main challenges that these fields must face with are discussed. Considering the increasing interest of industries, public policy makers and researchers in this field, the efficiency and profitability such hybrid renewable solutions for biomethane production are expected to significantly improve in the next future, provided that suitable subsidies and funding policies are implemented to support their development.

Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion

The objective of this study is to apply machine learning models to accurately predict daily biomethane production in an industrial-scale co-digestion facility. The methodology involved applying elasticnet, random forest, and extreme gradient boosting to input-output data from an industrial-scale anaerobic co-digestion (ACoD) facility. The models were used to predict biomethane for 1-day, 3-day, 5-day, 10-day, 20-day, 30-day, and 40-day time horizons. These models were fit on four years of operational data. The results showed that elastic net (a model with assumptions of linearity) was clearly outperformed by random forest and extreme gradient boosting (XGBoost), which had out-of-sample R²values ranging between 0.80 and 0.88, depending on the time horizon. In addition, feature importance and partial dependence analysis demonstrated the marginal and interaction effects on biomethane of selected biowaste inputs. For instance, food waste co-digested with percolate were shown to have strong positive interaction effects. One implication of this study is that XGBoost and random forest algorithms applied to industrial-scale ACoD data provide dependable prediction results and may be a useful complement for experimental and mechanistic/theoretical models of anaerobic digestion, especially where detailed substrate characterization is difficult. However, these models have limitations, and suggestions for deriving additional value from these methods are proposed.

Volatile fatty acid production from mesophilic acidogenic fermentation of organic fraction of municipal solid waste and food waste under acidic and alkaline pH

This study is focused on the effects of pH on the production of volatile fatty acids (VFAs) and their distribution through the acidogenic fermentation of source-sorted organic fraction of municipal solid waste (OFMSW) from a mechanical-biological treatment (MBT) plant, and food waste (FW) from a university canteen. In semi-continuous lab-scale digesters using OFMSW at a hydraulic retention time (HRT) of 3.5 days under acidic conditions (pH 6.0), the VFA concentration in the effluent increased to 9.8-11.5 g L^-1 (VS content of the feedstock between 4.2 and 5.2% w/w), while its individual VFA profiling was similar to the influent which was already pre-fermented (namely, C₂ 35-41%, C₃ 18-22%, C₄ 17-21%, and C₅ 9-12%). When working with the same conditions but using FW as feedstock, an effluent with a VFA concentration up to 11.5 g VFA L^-1 (FW with a VS content of 5.5% w/w) and a stable distribution of C₂ and C₄ acids (up to 60.3% and 12.9%, respectively) but with very low quantities of C₃ and C₅ acids (lower than 1.8 and 2.7%, respectively) was obtained. Anaerobic batch tests using FW revealed that alkaline pH near 9 could lead to higher VFA production with high acetic acid content when compared to pH 6. In the semi-continuous fermenters working at alkaline conditions (pH 9.5-10) using OFMSW and FW, an enhanced solubilization of organic matter was registered with respect to the fermenters working under acidic conditions. This fact was not reflected in a higher VFA production when using OFMSW as feedstock, probably due to free ammonia inhibition, since OFMSW was mixed in the MBT plant with supernatant from anaerobic digestion of this biowaste. However, when using FW, alkaline conditions lead to an enhanced VFA production with respect to the reactor working under acidic conditions, being acetic acid the predominant product, which represented up to 91% of the VFA spectrum obtained.

The effects of anionic and non-ionic surfactant on anaerobic co-digestion of sludge, food wastes and green wastes

Surfactants are widely used and discharged into wastewater treatment plants, which might influence the anaerobic digestion (AD) treatment of municipal waste. In this study, the effects of typical anionic surfactants sodium dodecyl benzene6 sulfonate (SDBS) and non-ionic surfactants APG, on mesophilic anaerobic co-digestion of sludge, food waste, and green waste were investigated. Results indicated that at 5 mg/g, the biogas production was inhibited in SDBS supplemented systems while stimulated in APG-added reactors, with the methane yield of 146.58 L/g VS consumed. At 15 mg/g, the biogas production in both SDBS and APG supplemented reactors was both inhibited. It means the negative or positive effect of APG on AD depends on the dose of APG supplementation. The 16S rRNA gene analysis demonstrated the microbial community structure in the digester was changed due to the addition of surfactant. Bacteroidia significantly increased with the addition of APG and SBDS, while the increase of Clostridia only occurred in APG-added system. The variation of microbial Communities' structure in APG and SDBS-added digesters might give an explanation for the different efficiencies in these two systems. Thus, the effects of surfactants on the efficiency of AD should be considered during the disposal of municipal organic waste.

Modified anaerobic digestion model No.1 (ADM1) for modeling anaerobic digestion process at different ammonium concentrations

Anaerobic digestion (AD) is an established method for sustainable energy production. Anaerobic digestion model No.1 (ADM1) was used to simulate methane production (MP) and volatile fatty acid (VFA) concentrations at different ammonium concentrations. In accordance with the incomplete description of several biochemical reactions and the omission of several reaction processes, ADM1 was modified with the consideration of acetic acid inhibition and valeric acid existence. ADM1_ac (ADM1 added acetic acid inhibition) could obtain better simulation accuracy of MP (goodness-of-fit value = 0.945), and VFA concentrations (goodness-of-fit values > 0.39) were all higher than ADM1_original, but cannot explain the valeric acid production. ADM1_va (ADM1 added valeric acid existence) could achieve better simulation of valeric acid (achieving a breakthrough of zero), nevertheless the accuracy of propionic and butyric acids was poorer than ADM1_ac with differences between experimental and simulation values were 5%-10% lower. With both factors coordinated, MP and VFA concentrations could be simulated accurately by ADM1_ac_va (ADM1 added acetic acid inhibition and valeric acid existence), with the highest goodness-of-fit values (>0.85). The results of a verification experiment with ADM1_ac_va simulation further indicated that acetic acid inhibition and valeric acid as new component were both important in ADM1. PRACTITIONER POINTS: ADM1_ac could simulate MP and acetate, propionate and butyrate concentrations better. ADM1_va could explain the valerate production during AD of glucose. ADM1_ac_va could simulate AD process quite accurately, with the highest goodness-of-fit values (>0.85). Acetate inhibition and valerate existence were both important and should be considered in ADM1.

The roles of free ammonia (FA) in biological wastewater treatment processes: A review

Free ammonia (FA) can pose inhibitory and/or biocidal effects on a variety of microorganisms involved in different biological wastewater treatment process, which is widely presented in wastewater treatment plants (WWTPs) due to the high levels of ammonium in the systems. This review article gives the up-to-date status on several essential roles of FA in biological wastewater treatment processes: the impacts of FA, mechanisms of FA roles, modeling of FA impacts, and implications of FA for wastewater treatment. Specifically, the impacts of FA on both wastewater and sludge treatment lines were firstly summarized, including nitrification, denitrification, anaerobic ammonium oxidation (Anammox), enhanced biological phosphorus removal and anaerobic processes. The involved mechanisms were then analyzed, which indicated FA inhibition can slow specific microbial activities or even reconfigure the microbial community structure, likely due to negative impacts of FA on intracellular pH, specific enzymes and extracellular polymeric substances (EPS), thus causing cell inactivation/lysis. Mathematical models describing the impact of FA on both wastewater and sludge treatment processes were also explored to facilitate process optimization. Finally, the key implications of FA were identified, that is FA can be leveraged to substantially enhance the biodegradability of secondary sludge, which would further improve biological nutrient removal and enhance renewable energy production.

Modified Anaerobic Digestion Model No. 1 for modeling methane production from food waste in batch and semi-continuous anaerobic digestions

A modified Anaerobic Digestion Model No. 1 (ADM1) with optimized kinetic parameters was presented to model methane production in the anaerobic digestion of food waste. Experimental data from batch and semi-continuous fermentations were used to calibrate and verify the model. Modified ADM1 simulation was carried out using AQUASIM 2.0 software. Sensitivity analysis was used to identify and evaluate the most sensitive kinetic parameters during biogas production. The decay constant of microorganisms, the disintegration constant, the hydrolysis constant of carbohydrates, the Monod maximum specific substrate uptake rate, and the half-saturation constants affected biogas production significantly. The optimized values of these parameters were 0.001, 0.16, 3, 1 and 0.23, respectively. Optimization results were validated using batch and semi-continuous experiments. The modified ADM1 well-predicted methane production, with R² values for the validation experiments all above 90%. These results can be used as basic data to simulate methane production in full-scale reactors.