Modeling the dynamic volatile fatty acids profiles with pH and hydraulic retention time in an anaerobic baffled reactor during the startup period

Kinetic study on anaerobic digestion of long-chain fatty acid enhanced by activated carbon adsorption and direct interspecies electron transfer

This study applied granular activated carbon (GAC) to improve the anaerobic digestion of long-chain fatty acid (LCFA). New kinetics were considered to describe the effect of GAC on the LCFA degradation, including i) The adsorption kinetics of GAC for LCFA, ii) The β-oxidation pathway of LCFA, iii) The attached biomass improved by direct interspecies electron transfer (DIET). The developed model simulated the anaerobic digestion of stearic acid, palmitic acid, myristic acid, and lauric acid with 1.00 and 2.00 g l-1 of GAC. The simulation results suggested that adding GAC led to the increase of km,CnGAC and km,acGAC. As the concentration of GAC increased, the values of kinetic parameters increased while the accumulated acetate concentration decreased. Thus, GAC improved the kinetic parameters of the attached syntrophic communities.

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.

Multivariable Robust Regulation of Alkalinities in Continuous Anaerobic Digestion Processes: Experimental Validation

A multivariable adaptive feedback control for highly uncertain continuous anaerobic digestion processes is proposed to regulate the volatile fatty acids (VFA) concentration, the strong ions concentrations, and the total and intermediate alkalinities. The multivariable control scheme includes a Luenberger observer to estimate both the unmeasured variables (i.e., VFA) and unknown microbial growth kinetics. The control approach is designed using an exponential Lyapunov function to resemble the typical exponential biological growth of the involved microbial consortia. Taking into account physicochemical equilibrium, alkalinities are represented as a function of the state variables. As a result, the control problem becomes a regulation problem on alkalinities, and in turn, a tracking control problem on the state variables, with two manipulated variables—the dilution rate and the feed rate of a strong alkali solution—while the state variables’ set-points are given as a function of pH. The implementation of this multivariable control scheme was experimentally tested and validated in a 0.982 m3 pilot plant treating agro-industrial wastewater and demonstrated to be robust in the face of unknown microbial growth kinetics. Results showed the potential for practical application and optimization of industrial digesters.

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system

Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recirculating activated sludge through anaerobic and aerobic (An/Ae) processes. However, few studies have systematically analyzed the optimal hydraulic retention times (HRTs) in anaerobic and aerobic reactions, or whether these are the most appropriate control strategies. In this study, a novel optimization methodology using an improved Q-learning (QL) algorithm was developed, to optimize An/Ae HRTs in a BPR system. A framework for QL-based BPR control strategies was established and the improved Q function, Q^t+1(s_t,s_t+1)=Q^t(s_t,s_t+1)+k·[R(s_t,s_t+1)+γ·maxa^tQ^t(s_t,s_t+1)-Q^t(s_t,s_t+1)] was derived. Based on the improved Q function and the state transition matrices obtained under different HRT step-lengths, the optimum combinations of HRTs in An/Ae processes in any BPR system could be obtained, in terms of the ordered pair combinations of the <current state-transition state>. Model verification was performed by applying six different influent chemical oxygen demand (COD) concentrations, varying from 150 to 600 mg L^-1 and influent P concentrations, varying from 12 to 30 mg L^-1. Superior and stable effluent qualities were observed with the optimal control strategies. This indicates that the proposed novel QL-based BPR model performed properly and the derived Q functions successfully realized real-time modelling, with stable optimal control strategies under fluctuant influent loads during wastewater treatment processes.

Application of IWA Anaerobic Digestion Model No. 1 to simulate butyric acid, propionic acid, mixed acid, and ethanol type fermentative systems using a variable acidogenic stoichiometric approach

IWA Anaerobic Digestion Model No. 1 (ADM1) is the most widely recognised and popular mathematical model for anaerobic digestion processes. However, the application of ADM1 to acidogenic fermentation is limited by its use of constant stoichiometry to describe the formation of products via carbohydrate fermentation. This study presents a modification of ADM1 using a variable acidogenic stoichiometric approach in which the hydrogen partial pressure (p_H2) and pH are used to predict and regulate the acidogenic process. The fermentation of ethanol and its kinetics were introduced into the model structure. Experimental data from mixed acid-type fermentation in a 28.4 L anaerobic baffled reactor (ABR) fed with a sucrose solution with a chemical oxygen demand of 4000 mg L^-1 were used to calibrate the model parameters. Two case studies involving continuous ethanol-type fermentation in an ABR and a continuous stirring tank reactor (CSTR) were used to validate the approach. The modified model achieved good predictions of the experimental data collected from butyric acid, propionic acid, mixed acid, and ethanol-type fermentation in the ABR and CSTR using the standard ADM1 parameter values without any parameter fitting beyond implementation of the variable acidogenic stoichiometry. The p_H2 and pH thresholds in butyric acid, propionic acid, mixed acid, and ethanol-type fermentation could be predicted using this model, which was shown to be a valid mathematical tool for the regulation of fermentation type.

Modelling anaerobic, aerobic and partial nitritation-anammox granular sludge reactors - A review

Wastewater treatment processes with granular sludge are compact and are becoming increasingly popular. Interest has been accompanied by the development of mathematical models. This contribution simultaneously reviews available models in the scientific literature for anaerobic, aerobic and partial nitritation-anammox granular sludge reactors because they comprise common phenomena (e.g. liquid, gas and granule transport) and thus pose similar challenges. Many of the publications were found to have no clearly defined goal. The importance of a goal is stressed because it determines the appropriate model complexity and helps other potential users to find a suitable model in the vast amount of literature. Secondly, a wide variety was found in the model features. This review explains the chosen modelling assumptions based on the different reactor types and goals wherever possible, but some assumptions appeared to be habitual within fields of research, without clear reason. We therefore suggest further research to more clearly define the range of operational conditions and goals for which certain simplifying assumptions can be made, e.g. when intragranule solute transport can be lumped in apparent kinetics and when biofilm models are needed, which explicitly calculate substrate concentration gradients inside granules. Furthermore, research is needed to better mechanistically understand detachment, removal of influent particulate matter and changes in the mixing behaviour inside anaerobic systems, before these phenomena can be adequately incorporated in models. Finally, it is suggested to perform full-scale model validation studies for aerobic and anammox reactors. A spreadsheet in the supplementary information provides an overview of the features in the 167 reviewed models.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH₄⁺, VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R²), the BPANN model demonstrated significant performance with R² reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible.

Efficiency of an upflow anaerobic sludge blanket reactor treating potato starch processing wastewater and related process kinetics, functional microbial community and sludge morphology

Herein, an upflow anaerobic sludge blanket reactor was employed to treat potato starch processing wastewater and the efficacy, kinetics, microbial diversity and morphology of sludge granules were investigated. When organic loading rate (OLR) ranging from 2.70 to 13.27kgCOD/m³.d was implemented with various hydraulic retention times (72h, 48h and 36h), COD removal could reach 92.0-97.7%. Highest COD removal (97.7%) was noticed when OLR was 3.65kgCOD/m³.d, but had declined to 92.0% when OLR was elevated to 13.27kgCOD/m³.d. Methane and biogas production increased from 0.48 to 2.97L/L.d and 0.90 to 4.28L/L.d, respectively. Kinetics and predictions by modified-Gompertz model agreed better with experimental data as opposed to first-order kinetic model. Functional population with highest abundance was Chloroflexi (28.91%) followed by Euryarchaeota (22.13%), Firmicutes (16.7%), Proteobacteria (16.25%) and Bacteroidetes (7.73%). Compared with top sludge, tightly-bound extracellular polymeric substances was high within bottom and middle sludge. Morphology was predominantly Methanosaeta-like cells, Methanosarcina-like cells, rods and cocci colonies.

Functional bacterial and archaeal diversity revealed by 16S rRNA gene pyrosequencing during potato starch processing wastewater treatment in an UASB

Microbial community structure of sludge sampled from an UASB treating potato starch processing wastewater (PSPW) was investigated. Operational taxonomic units revealed at 97% sequence identity tolerance was 2922, 2869 and 3919 for bottom, middle and top sections of the reactor, respectively. Overall abundant phylum observed within the UASB was low-G+C-Gram-positive bacteria affiliated to Firmicutes (26.01%) followed by Chloroflexi (16.70%), Proteobacteria (12.71%), Cloacimonetes (10.72%), Bacteroidetes (7.87%), Synergistetes (9.02%) and Euryarchaeota (8.82%). Whiles Firmicutes had dominated the bottom and top section by 34.01% and 28.64%, respectively, middle section was predominantly Euryarchaeota (24.32%) with major dominance in methanogens affiliated to genus Methanosaeta. The results demonstrated substantial stratification of the microbial community structure along the reactor height with various functional bacterial groups which subsequently allowed degradation of organics in PSPW in sequential mode. The findings herein would provide guidance for optimizing the anaerobic process and operation of the UASB.