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Performance of a digester under different feeding strategies. Left: results of 18 days of operation. Right: zoom in on only 3 days of operation. Performance of a digester under different feeding strategies. Left: results of 18 days of operation. Right: zoom in on only 3 days of operation.
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Modelling in anaerobic digestion will play a crucial role as a tool for smart monitoring and supervision of the process performance and stability. By far, the Anaerobic Digestion Model No. 1 (ADM1) has been the most recognized and exploited model to represent this process. This study aims to propose simple extensions for the ADM1 model to tackle so...
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... biogas production, the biogas composition, and the pH of the digestate for 18 days of operation for each of the assessed feeding strategies are shown in Figure 1. The second column of the figure represents a zoom-in on three operation days in order to afford a more precise consideration of the results. ...
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... Phenols can adsorb onto the sludge with a saturation level of 800-1600 mg/L (Hernandez and Edyvean, 2008). Also, microbial adaptation can reduce the effect inhibitors have on methane production over time, which is not taken into account in the model (Donoso-Bravo et al., 2022). Microbes will be selected in continuous reactors based on their adaptability to the substrate, making microbial adaptation an essential factor in the model. ...
Pyrolysis of biosolids aims to reduce solid volumes and improve energy recovery; however, the pyrolysis liquid (PL) is a by-product that has no good direct application. One idea is to link pyrolysis and anaerobic digestion (AD), in which PL can be valorized for methane production. PL contains various compounds that potentially threaten the stability of AD. This study, therefore, aims to extend the current Anaerobic Digestion Model No.1 (ADM1) and evaluate the influence of phenol, furfural, 5-hydroxymethylfurfural (5-HMF), styrene, and ammonia from PL on AD. Two lab-scale AD reactors were simulated and compared with experimental data: one fed with hydrolyzed sludge and the other fed with an additional stream of PL. The simulation accurately predicts hydrolyzed sludge as substrate, while the simulation of the reactor co-digesting hydrolyzed sludge and PL overestimates methane production. Ammonia, phenol, and styrene were identified as the most significant inhibitors. However, based on the overestimation of methane production, it is clear that the PL has more inhibitors present than those implemented in the model. Simulations further showed that an additional stream of PL increased methane production by 4.3%, even with significant inhibition by the compounds.
... This layer of dirt, which cannot be digested by biomass, will cut down the usable capacity of the digester. The reduction in the working volume has recently been considered in the dynamic modelling of anaerobic digestion by Donoso-Bravo et al. [6], with the ADM1, and by Tsitsimpikou et al. [7], with an alternative model. This buildup, among other things, will lead to the need for maintenance and the cleanup of the digester tank. ...
Anaerobic digestion plays a crucial role in the transition toward a circular economy. Incorporating system supervision through mathematical modelling can enhance control and resilience. This study aims to assess the impact of scheduled digester maintenance on the effectiveness of modelling as a tool for monitoring and control. Data from a pilot-scale plug-flow digester were analyzed using an adapted ADM1 model. The maintenance involved halting the digester and removing sedimented solids. Model calibration indicated solid retention in the first two zones of the reactor, while the hydrolysis coefficient and biogas potential remained at 0.122 d−1 and 100.4 mL CH4/gVS, respectively. The average biogas production decreased from 156 to 109 mL/gVS pre- and post-maintenance. Simulations showed a decline in the model’s predictive accuracy after maintenance. To improve model fit, the initial conditions, solids retention, and kinetic parameters were adjusted. Optimal performance was achieved with khyd at 0.045 d−1 and B0 at 52.28 mL gVS−1, revealing an issue with the digester’s heating system. In conclusion, maintenance can significantly alter digester conditions, requiring model recalibration to maintain its effectiveness as a digital copilot for process supervision.
... Due to the phenomenon's complexity, there is a lack of documentation on the local hydrodynamic behaviour of UASB reactors. A thorough study of flow patterns in UASB reactors would enhance our understanding of these systems [4]. ...
... The co-digestion model is fully described in Carecci et al. [38]. This model was slightly modified in order to: (i) describe a variable-volume operational mode [39]; (ii) include free ammonia gas/liquid mass transfer; (iii) correct the mass transfer coefficient (k L a) for different gases using their diffusivities [40]; (iv) given the high initial concentrations tested with batch activity test, introduce the Haldane kinetics (eq. (1)) for the uptake of single VFAs [41][42][43], in comparison with the original Monod equation in the ADM1 (see paragraph 2.4): ...
... Due to its widespread adoption, the ADM1 model has undergone various modifications. For instance, it has been extended to simulate thermophilic anaerobic digestion of thermally pretreated waste-activated sludge [7], to simulate methane production and volatile fatty acid (VFA) concentrations at different ammonium concentrations [8], for the discontinuous feeding process [9], and for modeling municipal primary sludge hydrolysis [10]. ...
Using renewable energy is increasingly prevalent as part of a global effort to safeguard the environment, with a reduction in CO 2 being one of the primary objectives. A biogas plant provides an opportunity to produce green energy, but its profitability prevents it from being utilized more frequently. A suitable response to this economic issue would be flexible biogas production to exploit fluctuating energy prices. Nevertheless, the complex nature of the anaerobic digestion process that proceeds within the biogas plant and the wide range of substrates that may be utilized as the plant’s feeds make it challenging to achieve flexible biogas production truly. Most plant operators will rely on their experience and intuition to run the plant without knowing exactly how much biogas they will produce with the feed substrate. This work combines a system model estimation and feedback controller to provide an intuitive yet precise feedback control system. The system model estimation represents the biogas plant mathematically, and a total of six distinct approaches have been compared and evaluated. A PT1 model most accurately approximated the step-down and the step-up by the time percentage method, with the Akaike Information Criterion as the primary evaluation criterion for selecting the best model. The downward model was controlled by a discrete PI controller modified with the Root Locus Method and an Anti-Windup scheme, and the upward model was controlled by a state space controller. The quality of the controller was evaluated in both simulation and at the actual biogas plant in Grub, and the controller was able to reduce the biogas production rate approaching the setpoint in the expected period. Furthermore, the developed feedback control system is effortless enough to be installed in many biogas plants.
... Mathematical modeling of biochemical processes is often referred to as the "abstraction of reality", involving a multitude of generalities, simplifications and assumptions to enlighten complex and unknown aspects of the processes [29,30]. The limitations and assumptions and the purpose for which each model was created should be clearly defined to export accurate simulation results [18,31]. ...
... Proper mixing usually enhances CH 4 production by improving the substrate, enzyme and microorganism distribution through the formation of a homogenous mixture, wherein the solids retention time (SRT) equals the HRT [35,36]. Nonetheless, inadequate mixing conditions usually lead to solid stratification, formation of dead zones, and accumulation of material in sections where the agitation system is ineffective and at the bottom of the reactor; thus, SRT differs from HRT [29,35]. On top of this, sedimentation phenomena can occur even at adequate mixing conditions. ...
... On top of this, sedimentation phenomena can occur even at adequate mixing conditions. The precipitated material mostly derives from solid raw materials such as sand, small gravels and other non-degradable substances inserted into the tanks along with substrates, and also from the formation of insoluble iron sulfide precipitates since iron salts and oxides are commonly used for biogas desulfurization [29,37]. ...
The present study focuses on the working volume reduction of anaerobic reactors in biogas plants, which is caused by inorganic material accumulation and inadequate mixing and affects methane production and plant profitability. Precipitation phenomena lead to periodic reactor cleaning processes, which complicate the operation of the plant and increase its operating costs. For this purpose, the bioconversion model (BioModel) was utilized by modifying its conditions to accurately simulate the reduction of the working volume of a biogas plant facing precipitation problems for a study period of 150 days. The modified BioModel exhibited notable results in the prediction of methane production, with an average deviation of 1.97% from the plant’s data. After validation, based on the model results, an equation was set up to predict the optimal reactor cleaning period. Incidentally, the optimal cleaning time was calculated at 5.1 years, which is very close to the period during which the cleaning of the reactors of the studied biogas plant took place (5.5 years). The findings of this research showed that the modified BioModel, along with the developed equation, can be effectively used as a tool for the prediction of the optimal reactor cleaning period.
... Various approaches have been proposed. From a biochemical point of view, these methods include the kinetic simulation of biochemical conversion processes, using mainly the ADM1 model [5][6][7][8] or most advanced related models, such as the Anaerobic Digestion System Model (ADSM) [9] and the theoretical analysis of kinetic parameters [10]. These approaches are useful for screening purposes or long-term continuous monitoring of AD processes [11], however they often introduce high uncertainty when implemented in practice to achieve optimal design and scale-up, due to the complexity of the biochemical processes and the need for accurate data. ...
The detailed physics-based description of anaerobic digesters is characterized by their multiscale and multiphysics nature, with Computational Fluid Dynamics (CFD) simulations being the most comprehensive approach. In practice, difficulties in obtaining a detailed characterization of the involved biochemical reactions hinder its application in the design of novel reactor concepts, where all physics interplays in the reactor must be considered. To solve this limitation, a practical approach is introduced where a calibration step using actual process data was applied for the simplified biochemical reactions involved, allowing us to efficiently manage uncertainties arising when characterizing biochemical reactions with lab scale facilities. A complete CFD modeling approach is proposed for the anaerobic digestion of wastewater, including heat transfer and multiphasic flow. The proposed multiphase model was verified using reference data and, jointly with the biochemical modeling approach, applied to a lab-scale non-conventional anaerobic digester for winery wastewater treatment. The results showed qualitative improvement in predicting methane production when the diameter of the particles was reduced, since larger particles tend to move downwards. The biochemistry of the process could be simplified introducing a preexponential factor of 380 (kmol/m3)(1 – n)/s for each considered chemical reaction. In general, the proposed approach can be used to overcome limitations when using CFD to scale-up optimization of non-conventional reactors involving biochemical reactions.
