Anaerobic Digestion Model No. 1 Research Articles

Biogas Production Optimization in the Anaerobic Codigestion Process: A Critical Review on Process Parameters Modeling and Simulation Tools

Many operational parameters, either discretely or collectively, can influence the biodegradation performance towards enhancing biogas yield and quality. Among the operating parameters, organic loading rate (OLR), inoculum-substrate ratio, and carbon-nitrogen ratio (C/N) are the most critical parameters in the optimization and enhancement of biogas yield. Optimization of the biogas production processes depends on the ability of anaerobic microorganisms to respond to variations in operational parameters such as pH, redox potential, and intermediate products to enhance the biogas yield. This review article focuses on the role of process parameters, kinetic models, artificial intelligence, Aspen Plus (AP), and anaerobic digestion model no. 1 (ADM1) in optimizing biogas yield via an anaerobic codigestion (AcoD) process. The review showed that biomaterials codigestion upgraded biogas yield to the extent of 400%, and organic removal efficiency reached up to 90% compared to a single substrate. In addition, the current work has verified that the kinetic model is the most effective tool for signifying that the hydrolysis phase is the rate-limiting step, whereas AP is the most effective tool in the design and optimization of the AcoD process parameters. The reviewed kinetic and AI models show strong correlation values ranging from 0.931 to 0.9991 and 0.8700 to 0.9998, respectively. The AcoD system involves complex chemical reactions, but AP might have limitations in representing such complex chemical processes with nonideal behavior and complicated reaction mechanisms. The design and optimization of AcoD with reliable input parameters are highly limited or nonexistent. The AcoD process design with AP opens fresh research opportunities, including improved efficiency, finding appropriate retention time, and saving time, as well as finding the optimum biogas yield. This review article gives an insightful understanding of AcoD process parameter optimization and valuable strategies for policy development enhancing sustainability in the biogas sector.

A hybrid approach of anaerobic digestion model no. 1 and machine learning to model and optimize continuous anaerobic digestion processes

Anaerobic digestion is a promising approach to dispose of biodegradable waste and wastewater, generating biogas as an alternative energy resource. This work proposed a so-called M-CADM1 for continuous anaerobic digestion simulation, which combined the machine learning and anaerobic digestion model No.1 (ADM1). The detailed reaction path and intermediate products in different stages of anaerobic digestion are specified in ADM1. The kinetic parameters were modified by machine learning. The characteristics (elemental composition) of feedstocks were used to predict kinetic parameters. A total of 75 biomass samples were used to establish for machine learning models. Five element contents (C, H, O, N, S), feedstock feed rate, and anaerobic digestion temperature were used as the input. The kinetic parameters were set as output. The sensitivities of 17 kinetic parameters were evaluated. 7 kinetic parameters with the highest sensitivities were selected as ADM1 model inputs by sensitivity analysis. The R2 and RMSE were used as the index to evaluated the accuracy of machine learning model. The best R2 and RMSE reached 0.84 and 0.196. The TIC was used as the index to evaluated the accuracy of M-CADM1. By comparing the simulated value with the experimental value, the accuracy of the overall M-CADM1 expressed by TIC of kitchen waste was 0.036. The organic acid content and pH in the reactor were considered as indicators to study the accuracy and stability of the M-CADM1. Trends in organic acids, free ammonia or hydrogen inhibition, and pH were consistent with experimental continuous anaerobic digestion results.

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.

Comparative Analysis of pH Prediction Routines in ADM1 and a Specialized Water Chemistry Simulator

AbstractIn anaerobic technology, pH values are crucial for targeted volatile fatty acid production. While pH dynamics can be modeled using the Anaerobic Digestion Model No. 1 (ADM1), simulation results may be biased. To address this issue, the pH prediction routine of Visual Water, a specialized water chemistry simulator, was validated. Unlike ADM1, it accounts for ionic strength and activities while also providing an automated uncertainty analysis. The analysis revealed Visual Water simulations to better fit measured pH data from acidic solutions in a miniaturized stirred‐tank reactor.

Application of anaerobic digestion model No. 1 on thermophilic anaerobic digestion with microbial immobilization media for biogas production from sugarcane vinasse

Anaerobic Digestion Model no.1 (ADM1) for the "Mixed Reactor Compartment" of AQUASIM with modified kinetic parameters was proven to be effective in predicting the behavior of thermophilic anaerobic digestion of sugarcane vinasse in a continuous stirred tank reactor (CSTR) equipped with unwoven textile as the microbial immobilization media. The modified kinetic parameters implied higher microbial growth rate constants in the CSTR with microbial immobilization media than the CSTR without media. The 16 s rRNA assay confirmed the inferences on the physical interpretation of the kinetic parameters. Microbial immobilization media shifted the initially homogeneous microbial composition in the CSTR’s bulk liquid into methanogenic dominance in the biofilm attached to the media and acidogenic dominance in the liquid bulk after 90 days of operation. The remarkable finding of this study was that the segregated microorganisms indicated a beneficial micro-environment for both acidogenic and methanogenic populations to have favorable conditions for each of them. ADM1 well predicted the effect of this segregation with modified kinetic parameters and confirmed by the 16 s rRNA assay on the reactor’s liquid and biofilm samples.

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±100mg 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.

Modeling of simultaneous carbon and nitrogen removal (SCNR) in an anaerobic/anoxic reactor treating salmon fishery wastewater

Mathematical modeling can help to clarify some aspects of the process that promote the simultaneous removal of organic matter and nitrogen (SCNR) from the fish processing industry wastewater that still require elucidation, such as the competition between methanogenic and denitrifying microorganisms for the substrate, for example. Therefore, the aim of this paper is to present the application of the Anaerobic Digestion Model No. 1 (ADM1) for modeling the SCNR process in a single stage anaerobic/anoxic reactor fed with a mix of a synthetic substrate and a saline protein-rich salmon processing industry. The ADM1 was not specifically developed for the treatment of this wastewater, therefore, initially, a parametric sensitivity analysis was performed to define the most sensitive parameters. The sensitivities were quantified in terms of the variation of measurable process variables under the perturbation of model parameters in their neighborhood domain. After this analysis, the model was calibrated and validated using previously published experimental data. Based on a sensitivity analysis, five parameters of the original ADM1 and of the denitrification processes were estimated using iterative methods, which optimized the parameters with experimental results. Namely: proteins hydrolysis constant (khyd,pr = 0.05 h−1); Monod maximum specific uptake rate for amino acids utilizers (km,aa = 1.87 h−1); half saturation value for amino acids utilizers (KS,aa = 0.23 kg COD m−3); Monod maximum specific uptake rate for acetate utilization rates by the denitrifiers (kno3,ac = 0.09 h−1) and half saturation value for the utilization of the acetate by the denitrifiers (KS,no3,ac = 0.38 kg COD m−3). Simulation with calibrated parameters indicated good agreement with experimental data, with the mean absolute error below 40 %. Considering these optimal values, the complete reduction of the oxidized forms of nitrogen occurred up to 11 h of reaction time. Cross-validation results indicated that the calibrated model was able to predict the experimental results of effluent COD, acetate, propionate, butyrate, nitrate and nitrite flows with good accuracy showing a mean absolute error below 40 % (19 %–36 %).

Enhancing bio-hydrogen and bio-methane production of concentrated latex wastewater (CLW) by Co-digesting with palm oil mill effluent (POME): Batch and continuous performance test and ADM-1 modeling

This study aimed at investigating the biohydrogen and biomethane potential of co-digestion from palm oil mill effluent (POME) and concentrated latex wastewater (CLW) in a two-stage anaerobic digestion (AD) process under thermophilic (55 ± 3 °C) and at an ambient temperature (30 ± 3 °C) conditions, respectively. The batch experiments of POME:CLW mixing ratios of 100:0, 70:30, 50:50, 30:70, and 0:100 was investigated with the initial loadings at 10 g-VS/L. The highest hydrogen yield of 115.57 mLH2/g-VS was obtained from the POME: CLW mixing ratio of 100:0 with 29.0 of C/N ratio. While, the highest subsequent methane production yield of 558.01 mLCH4/g-VS was achieved from hydrogen effluent from POME:CLW mixing ratio of 70:30 0 with 21.8 of C/N ratio. This mixing ratio revealed the highest synergisms of about 9.21% and received maximum total energy of 19.70 kJ/g-VS. Additionally, continuous hydrogen and methane production were subsequently performed in a series of continuous stirred tank reactor (CSTR) and up-flow anaerobic sludge blanket reactor (UASB) to treat the co-substate. The results indicated that the highest hydrogen yield of POME:CLW mixing ratio at 70:30 of 95.45 mL-H2/g-VS was generated at 7-day HRT, while methane production was obtained from HRT 15 days with a yield of 204.52 mL-CH4/g-VS. Thus, the study indicated that biogas production yield of CLW could be enhanced by co-digesting with POME. In addition, the two-stage AD model under anaerobic digestion model no. 1 (ADM-1) framework was established, 9.10% and 2.43% of error fitting of hydrogen and methane gas between model simulation data and experimental data were found. Hence, this research work presents a novel approach for optimization and feasibility for co-digestion of POME with CLW to generate mixed gaseous biofuel potentially.

The application of Anaerobic Digestion Model No. 1 for the optimization of biogas production from maize silage, pig manure, cattle manure, and digestate in a full-scale biogas plant

The main aim of this study was to evaluate the applicability of a mathematical model based on Anaerobic Digestion Model No. 1 (ADM1) for optimizing biogas production during a two-stage anaerobic digestion process in a full-scale biogas plant. Biogas production was analyzed in two variants. Variant I involved two decision variables: the proportion of the gaseous phase in the total volume of each digester within a preset limit of 0.05 to 0.95. Four decision variables were adopted in variant II: total digester volume during the first and second stage of anaerobic digestion, and the proportion of the gaseous phase in each digester. Digester volume was set at 10–1000 m3 for the primary digester, and at 300–5000 m3 for the secondary digester. The proportion of the gaseous phase was also set within the range of 0.05–0.95. Optimal digester volume (131 m3 for the primary digester and 5000 m3 for the secondary digester) and the optimal proportions of the gaseous phase in total digester volume (0.35 and 0.05, respectively) were generated by the modified ADM1xp model. The modeled parameters predicted a 67% increase in methane yields in a two-stage anaerobic digestion process (acidogenesis and methanogenesis).

Enhancing Methane Production through Anaerobic Co-Digestion of Sewage Sludge: A Modified ADM1 Model Approach

The International Water Association’s (IWA) established Anaerobic Digestion Model No. 1 (ADM1) was created to serve as a backup for experimental findings regarding the actual anaerobic digestion process. The previous model idea was adjusted and used to simulate an anaerobic digestion process in this study. Testing procedures, such as benchmark tests and balance checks, were performed in order to verify the accuracy of the implementation. These measures worked in tandem to ensure that the model was implemented flawlessly and without inconsistencies. The primary objective of this article is to construct a method that is based on the ADM1 for evaluating co-digestion and predicting the performance of the digestion process or methane yield based on the analyzed substrates’ physicochemical properties. Additional equations and simulations have been added to the standard model to create tools for evaluating the feasibility of anaerobic co-digestion. The study’s two most intriguing aspects are the optimal mixture and parameter dependence. The adjusted ADM1 is accurate in predicting the measured values of effluent COD, pH, methane, and produced biogas flows with a reasonable degree of accuracy, according to the validation results. This research shows how to use ADM1 in a wastewater treatment plant and other settings where anaerobic digestion is of interest.

Double-edged effect of tar on anaerobic digestion: Equivalent method and modeling investigation

This study investigated the influence of real tar (RT) and synthetic tar (ST) on anaerobic digestion (AD). Mapping relationship was built effectively between ST set and RT set through equivalent method. Both sets performed the double-edged effect on AD: low dosage of tar performed stimulated effect and high dosage caused inhibited effect. Anaerobic Digestion Model No.1 (ADM1) was modified and extended by introducing kinetic parameters (inhibition constant: KI, Monod maximum uptake rate: km, Monod half saturation constant: Ks) of tar to describe related degradation process. Model calibration with ST set data achieved accurate simulation of methane production with satisfactory R2 (0.80–0.97), and model validation was conducted with RT set data. The linkage between kinetic parameters and microbial community was further bridged. The decrease of km_H2 (km of hydrogen) value was less than km_ac (km of acetate) value, corresponding to the less sensitivity of hydrogenotrophic methanogenesis to tar toxicity as reflected by 16S rRNA. Modified ADM1 could help in understanding AD system macroscopically and microscopically.

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.

Application of a simplified ADM1 for full-scale anaerobic co-digestion of cattle slurry and grass silage: assessment of input variability

Mathematical modeling of anaerobic digestion is a powerful tool to predict gas yields and optimize the process. The Anaerobic Digestion Model No. 1 (ADM1) is a widely implemented model for this purpose. However, modeling full-scale biogas plants is challenging due to the extensive substrate and parameter characterization required. This study describes the modification of the ADM1 through a simplification of individual process phases, characteristic components and required parameters. Consequently, the ability of the simplified model to simulate the co-digestion of grass silage and cattle slurry was evaluated using data from a full-scale biogas plant. The impacts of substrate composition (crude carbohydrate, protein and lipid concentration) and variability of carbohydrate degradability on simulation results were assessed to identify the most influential parameters. Results indicated that the simplified version was able to depict biogas and biomethane production with average model efficiencies, according to the Nash-Sutcliffe efficiency (NSE) coefficient, of 0.70 and 0.67, respectively, and was comparable to the original ADM1 (average model efficiencies of 0.71 and 0.63, respectively). The variability of crude carbohydrate, protein and lipid concentration did not significantly impact biogas and biomethane output for the data sets explored. In contrast, carbohydrate degradability seemed to explain much more of the variability in the biogas and methane production. Thus, the application of simplified models provides a reliable basis for the process simulation and optimization of full-scale agricultural biogas plants.

Modification and calibration of anaerobic digestion model 1 to simulate volatile fatty acids production during fermentation of municipal sludge

The present study modified the anaerobic digestion model No.1 (ADM1) to simulate biological volatile fatty acids (VFA) production from municipal sludge. A variable and constant stoichiometric approach was used to modify the ADM1. The model was implemented in the AQUASIM 2.0 modeling platform and calibrated with experimental data from the bench-scale semi-continuous flow acid fermenters to assess the model's performance in simulating the VFA generation under different operating conditions. After parameter estimation, the modified ADM1 simulated experimental results of acetic, propionic, and butyric acids with reasonable accuracy and goodness of fit values ranging from 0.90 to 0.99. The model validation using an independent data set from the calibration data successfully simulated propionic (goodness of fit of 0.89) and butyric acids (goodness of fit of 0.88) but underestimated acetic acid production (goodness of fit of 0.73). The presented results extend the applicability of the ADM1 to simulate VFA during anaerobic fermentation.

Upgrading ADM1 by Addition of Lag Phase Sub-model to Simulate Acidic Inhibition of Methanogenic Reactor

This study is aimed at improving calculation quality for IWA Anaerobic Digestion Model No.1 (ADM1) to simulate acidic failure of the methane fermentation systems and its performance recovery. The methanogen collected from digestate at a municipal wastewater treatment plant was cultivated in a lab-scale continuous reactor receiving acetate as a sole organic source. By varying the influent concentration during 400 days of operation, 6 datasets of acidification events were obtained to simulate the concentrations of acetate, VSS, pH, and the methane production rate. The ADM1 equipped with either pH sub-model or undissociated acetate sub-model could reproduce the deterioration of reactor performance in the acidic failure but totally failed to simulate the recovery in the subsequent lowered volumetric loading rate. The statistical analysis revealed both ADM1 models had relatively low correlation coefficient (Nash-Sutcliffe model efficiency coefficient (NSE)) of 0.31–0.38 although these were considerably improved from that without parameter calibration (NSE = −0.04). To cope with the mismatch, a lag phase sub-model was developed. The sub-model is composed of the remaining relative activity of the microorganism at which the inhibition peaked, and the half-saturation coefficient to express the specific length of lag phase. By adding the sub-model into the calibrated ADM1, NSE was significantly improved to 0.49–0.53.

Modification of anaerobic digestion model No.1 with Machine learning models towards applicable and accurate simulation of biomass anaerobic digestion

This work proposed a so-called M−ADM1 model for anaerobic digestion simulation, which uses machine learning model to predict the kinetic parameters in anaerobic digestion model No.1 (ADM1). A total of 75 biomass samples were used to establish the machine learning model. Inputs used to predict the kinetic parameters included C, H, O, N, S contents, and digestion temperature. The sensitivities of 17 kinetic parameters were evaluated, and 7 kinetic parameters with the highest sensitivities were selected as model outputs. After model optimization, the average R2 for predicting the 7 kinetic parameters reached 0.92, and the root mean square error reached 0.167. The accuracy of the overall M−ADM1 expressed by Theil inequality coefficient of municipal solid waste, kitchen waste, and sludge were 0.0163, 0.0327, and 0.0361, respectively. The results validated the hypothesis that accurately predicting some crucial intermediate parameters using machine learning models could enhance the performance of tradition ADM1.

Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

A growing awareness of global climate change has led to an increased interest in investigating renewable energy sources, such as the anaerobic digestion of biomass. This process utilizes a wide range of microbial communities to degrade biodegradable material in feedstock through a complex series of biochemical interactions. Anaerobic digestion exhibits nonlinear dynamics due to the complex and interacting biochemical processes involved. Due to its dynamic and nonlinear behavior, uncertain feedstock quality, and sensitivity to the process’s environmental conditions, anaerobic digestion is highly susceptible to instabilities. Therefore, in order to model and operate a biogas production unit effectively, it is necessary to understand which parameters are most influential on the model outputs. This also reduces the amount of estimation required. Through a scoping review, the present study analyzes the studies on the application of sensitivity analysis in anaerobic digestion modeling. Both local and global sensitivity analysis approaches were carried out using different mathematical models. The results indicate that anaerobic digestion model no.1 (ADM1) was the most commonly used model for analyzing sensitivity. Both local and global sensitivity analyses are widely employed to investigate the influence of key model parameters such as kinetic, stoichiometric, and mass transfer parameters on model outputs such as biogas production, methane concentration, pH, or economic viability of the plant.

Converting sea weed to biogas energy: Utilization of synergism during co-digestion with maize silage

Circular economy is a system solution framework replacing the traditional linear approach and aims at creating value from waste. Bladderwrack, a brown sea weed is often cast away in huge volumes on the North Sea coast and is perceived as a waste product. Removal of this algae incurs heavy costs for the municipal authorities since deposits of this sea wrack weakens and poses threat to the flood protective dikes and also to the tourism industry due to its unpleasant sight and smell. This study investigated the biogas potential of the nutrient-rich bladderwrack, Fucus vesiculosus, and co-digestion experiments were performed with maize silage. Mathematical simulations were performed with the Anaerobic Digestion Model No. 1 (ADM1) in order to assess optimal operational conditions and understand the reaction kinetics. While bladderwrack and maize silage on an average yielded 178 ml/g FM together during mono-fermentation, the co-digestion experiment provided 236 ml/g FM, clearly demonstrating the synergistic effect with nearly 33% more biogas. This means reduced dependence on a forage crop like maize by substituting it with a waste product like the brown sea weed along with attaining enhanced biogas production. This positive effect is reasoned to be due to the balanced C/N ratio in the digester during co-digestion. Furthermore, bladderwrack supplements trace metals and vitamins to the nutrient deficient maize silage. The ADM1 model indicated active microbial growth and activities and proved its competence in predicting the synergistic impacts with high accuracy.

Biomethanation of Crop Residues to Combat Stubble Burning in India: Design and Simulation Using ADM1 Mathematical Model

Stubble burning in India continues despite the severe consequences on the environment and the massive health crisis in the country. Farmers resort to such practices as a cheap and hasty solution post-harvest, which helps them prepare for their next crops. This study employs a mathematical model, the ADM1 (Anaerobic Digestion Model No. 1), to design a virtual biogas plant in the SIMBA simulation platform. The plant was designed keeping the small-scale farmers in mind, hence, cost-effectiveness, simplicity in design and operation remained a priority. Simulations were performed with different crop leftovers that are widely subjected to on-farm burning in the country such as from rice, wheat, sugarcane, cotton and maize. Simulation trials were performed for each crop residue for nearly two years, to observe the digester performance and possible disruptions over prolonged periods. The optimal feeding ratio and operating conditions for process stability were determined. Simulations revealed generation of nearly 9–10 m3 methane per day, equivalent to 90–100 kWh electricity. Co-fermentation with animal manures was strongly recommended by the model for process stability and to avoid pH disruptions due to organic acid accumulations. Policy makers and farmers are, thus, encouraged to explore a sustainable alternative to generate energy from stubble.

Comprehensive ADM1 Extensions to Tackle Some Operational and Metabolic Aspects in Anaerobic Digestion.

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 some overlooked operational and metabolic aspects. Extensions for the discontinuous feeding process, the reduction of the active working volume, the transport of the soluble compound from the bulk to the cell interior, and biomass acclimation are presented in this study. The model extensions are included by a change in the mass balance of the process in batch and continuous operation, the incorporation of a transfer equation governed by the gradient between the extra- and intra- cellular concentration, and a saturation-type function where the time has an explicit influence on the kinetic parameters, respectively. By adding minimal complexity to the existing ADM1, the incorporation of these phenomena may help to understand some underlying process issues that remain unexplained by the current model structure, broadening the scope of the model for control and monitoring industrial applications.