Anaerobic Digestion Model No. 1 (ADM1) for mathematical modeling of full-scale sludge digester performance in a municipal wastewater treatment plant

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Performance and microbial community composition of full-scale high-rate cascade sludge digestion system via pie-shaped reactor configuration

A full-scale high-rate cascade anaerobic digestion (CAD) system was evaluated for its ability to enhance enzymatic sludge hydrolysis. The system included a newly built digester, innovatively divided into three pie-shaped compartments (500 m3 each), followed by an existing, larger digester (1500 m3). The system treated a mixture of waste activated sludge and primary sludge, achieving a stable total chemical oxygen demand reduction efficiency (56.1 ± 6.8 %), and enhanced sludge hydrolytic enzyme activities at a 14.5-day total solids retention time (SRT). High-throughput sequencing data revealed a consistent microbial community across reactors, dominated by consortia that govern hydrolysis and acidogenesis. Despite relatively short SRTs in the initial reactors of the CAD system, acetoclastic methanogens belonging to Methanosaeta became the most abundant archaea. ‬‬‬‬‬‬‬‬‬‬‬‬‬ This study proves that the CAD system achieves stable sludge reduction, accelerates enzymatic hydrolysis at full-scale, and paves the way for its industrialization in municipal waste sewage sludge treatment.

Modelling of high-rate activated sludge process: Assessment of model parameters by sensitivity and uncertainty analyses

The objective of this study is to develop a mechanistic model to predict the long-term dynamic performance of High-Rate Activated Sludge (HRAS) process, including the removal of carbon (COD), nitrogen (N), and phosphorus (P). The model was formulated with inspiration from Activated Sludge Models No. 1 and 3 (ASM1 and ASM3) to incorporate essential mechanisms, such as adsorption and storage substrate, specific to HRAS systems. A stepwise protocol was followed for calibration with dynamic data from a pilot-scale HRAS plant. Sensitivity analysis identified influential model parameters, including maximum specific growth rate (μ), growth yield (YH), storage yield (YSTO), storage rate (kSTO), decay rate (b), and half saturation of the readily biodegradable substrate for growth (KS1). The calibrated model achieved prediction efficiencies above the normalized Mean Absolute Error (MAE) of 70 % for mixed liquor suspended solids (MLSS), total chemical oxygen demand (TCOD), soluble COD (SCOD), particulate COD (XCOD), total nitrogen (TN), ammonia nitrogen (SNH), total phosphorus (TP), soluble TP (STP), and particulate TP (XTP). Uncertainty analysis revealed that SCOD was underestimated. Based on the dynamic profiles of uncertainty bands and observed data, there is potential for improving the estimation of dynamic behavior in STP. The observed discrepancies may be attributed to variations in wastewater characteristics during the monitoring period, particularly concerning the phosphorus (P) fractions of the readily biodegradable substrate (SS) and soluble inerts (SI), which were not considered as dynamically changing parameters in the model.

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.

Impact of primary treatment methods on sludge characteristics and digestibility, and wastewater treatment plant-wide economics

Biogas production from anaerobic sludge digestion plays a central role for wastewater treatment plants to become more energy-efficient or even energy-neutral. Dedicated configurations have been developed to maximize the diversion of soluble and suspended organic matter to sludge streams for energy production through anaerobic digestion, such as A-stage treatment or chemically enhanced primary treatment (CEPT) instead of primary clarifiers. Still, it remains to be investigated to what extent these different treatment steps affect the sludge characteristics and digestibility, which may also impact the economic feasibility of the integrated systems. In this study, a detailed characterization has been performed for sludge obtained from primary clarification (primary sludge), A-stage treatment (A-sludge) and CEPT. The characteristics of all sludges differed significantly from each other. The organic compounds in primary sludge consisted mainly of 40% of carbohydrates, 23% of lipids, and 21% of proteins. A-sludge was characterized by a high amount of proteins (40%) and a moderate amount of carbohydrates (23%), and lipids (16%), while in CEPT sludge, organic compounds were mainly 26% of proteins, 18% of carbohydrates, 18% of lignin, and 12% of lipids. The highest methane yield was obtained from anaerobic digestion of primary sludge (347 ± 16 mL CH₄/g VS) and A-sludge (333 ± 6 mL CH₄/g VS), while it was lower for CEPT sludge (245 ± 5 mL CH₄/g VS). Furthermore, an economic evaluation has been carried out for the three systems, considering energy consumption and recovery, as well as effluent quality and chemical costs. Energy consumption of A-stage was the highest among the three configurations due to aeration energy demand, while CEPT had the highest operational costs due to chemical use. Energy surplus was the highest by the use of CEPT, resulting from the highest fraction of recovered organic matter. By considering the effluent quality of the three systems, CEPT had the highest benefits, followed by A-stage. Integration of CEPT or A-stage, instead of primary clarification in existing wastewater treatment plants, would potentially improve the effluent quality and energy recovery.

Sustainability for wastewater treatment: bioelectricity generation and emission reduction

This review covers the technological measures of a self-sustainable anaerobic up-flow sludge blanket (UASB) system compared with an aerobic activated sludge process (ASP) for wastewater treatment plants (WWTPs). The ASP requires a huge amount of electricity and chemicals and also results in the emission of carbon. The UASB system, instead, is based on greenhouse gas (GHG) emission reduction and is associated with biogas production for cleaner electricity. WWTPs including the ASP system are not sustainable due to the massive financial power required for clean wastewater. When the ASP system was used, the amount of production was estimated to be 10658.98 tonnes CO₂eq-d^- of carbon dioxide. Whereas it was 239.19 tonnes CO₂eq-d^-1 with the UASB. The UASB system is advantageous over the ASP system as it has a high production of biogas, needs low maintenance, yields a low amount of sludge, and is also a source of electricity that can be used as a power source for the WWTPs. Also, the UASB system produces less biomass, and this helps in reducing costs and maintaining work. Moreover, the aeration tank of the ASP needs 60% of energy distribution; on the other hand, the UASB consumes less energy, approximately 3-11%.

Integrating biokinetics with computational fluid dynamics for energy performance analysis in anaerobic digestion

Anaerobic digestion (AD) is an effective process for decomposing organic matter in wastewater treatment plants (WWTPs) where highly efficient digesters properly mix the sludge. To ensure a uniform substance distribution, a comprehensive modeling method is necessary. Computational fluid dynamics (CFD) helps in the modeling of AD tanks but few studies have focused on integrating hydrodynamics with biokinetics because of complex AD processes. The current study presents a new CFD platform for estimating the biokinetics of WWTPs to assess the energy performance of AD tanks. The presented method is validated by numerical and experimental studies, and facilitates a link between methane production and mixing energy consumption. The on-site settings of the recirculation mixing system in the studied WWTP was able to prepare a uniform mixture of the material. However, reducing mixing rate to decrease energy consumption did not lead to proper mixing quality.

Use of regression models for development of a simple and effective biogas decision-support tool

Anaerobic digestion (AD) is an alternative way to treat manure while producing biogas as a renewable fuel. To increase the efficiency of AD performance, accurate prediction of biogas yield in different working conditions is necessary. In this study, regression models were developed to estimate biogas production from co-digesting swine manure (SM) and waste kitchen oil (WKO) at mesophilic temperatures. A dataset was collected from the semi-continuous AD studies across nine treatments of SM and WKO, evaluated at 30, 35 and 40 °C. Application of polynomial regression models and variable interactions with the selected data resulted in an adjusted R² value of 0.9656, much higher than the simple linear regression model (R² = 0.7167). The significance of the model was observed with the mean absolute percentage error score of 4.16%. Biogas estimation using the final model resulted in a difference between predicted and actual values from 0.2 to 6.7%, except for one treatment which was 9.8% different than observed. A spreadsheet was created to estimate biogas production and other operational factors using substrate loading rates and temperature settings. This user-friendly program could be used as a decision-support tool to provide recommendations for some working conditions and estimation of the biogas yield under different scenarios.

Sewage-Water Treatment and Sewage-Sludge Management with Power Production as Bioenergy with Carbon Capture System: A Review

Sewage-water treatment comprehends primary, secondary, and tertiary steps to produce reusable water after removing sewage contaminants. However, a sewage-water treatment plant is typically a power and energy consumer and produces high volumes of sewage sludge mainly generated in the primary and secondary steps. The use of more efficient anaerobic digestion of sewage water with sewage sludge can produce reasonable flowrates of biogas, which is shown to be a consolidated strategy towards the energy self-sufficiency and economic feasibility of sewage-water treatment plants. Anaerobic digestion can also reduce the carbon footprint of energy sources since the biogas produced can replace fossil fuels for electricity generation. In summary, since the socio-economic importance of sewage treatment is high, this review examined works that contemplate: (i) improvements of sewage-water treatment plant bioenergy production and economic performances; (ii) the exploitation of technology alternatives for the energy self-sufficiency of sewage-water treatment plants; (iii) the implementation of new techniques for sewage-sludge management aiming at bioenergy production; and (iv) the implementation of sewage-water treatment with bioenergy production and carbon capture and storage.

Prediction of biogas production rate from dry anaerobic digestion of food waste: Process-based approach vs. recurrent neural network black-box model

The stability of dry anaerobic digestion (AD) of food waste (FW) as well as the resulting methane gas generation was investigated from the perspective of system dynamics. Various organic loading rates were applied to the system by modifying the water content in the FW feed and solid retention time (SRT). The excessive organic loading due to the accumulation of volatile fatty acids (VFAs) from the feed with 80% water content during the short SRT (15 and 20 d) caused system failure. In contrast, more intermediate materials, such as VFAs, was easily converted into methane at higher water contents. In addition, the biogas production rate of dry AD was effectively predicted based on SRT, soluble chemical oxygen demand, total VFA, total ammonia, and free ammonia using a recurrent neural network-the so-called "black-box" model. This implies the feasibility of applying this data-based black-box model for controlling and optimizing complex biological processes.

Modeling the Performance of Full-Scale Anaerobic Biochemical System Treating Deinking Pulp Wastewater Based on Modified Anaerobic Digestion Model No. 1

The deinking pulp (DIP) is a main resource for paper making, and the wastewater from DIP process needs to be treated. Anaerobic biochemical technique has been widely applied in DIP wastewater treatment, due to the remarkable capability in reducing high chemical oxygen demand (COD). In this study, a mathematical simulation model was established to investigate the performance of a full-scale anaerobic biochemical system for treating DIP wastewater. The model was based on Anaerobic Digestion Model No. 1 (ADM1), which was modified according to the specific anaerobic digestion process for DIP wastewater treatment. The hydrodynamic behavior of a full-scale anaerobic biochemical system was considered in this model. The characteristics of the influent DIP wastewater were assessed, and then, the substrate COD proportion was divided successfully for the necessity of ADM1 applying. The Monte Carlo technique was implemented to distinguish the most sensitive parameters that influenced the model output indicators comprising effluent COD and biogas production. The sensitive parameters were estimated and optimized. The optimized value of k_{_m_pro} is 12.02, K_{_S_pro} is 0.35, k_{_m_ac} is 4.26, K_{_S_ac} is 0.26, k_{_m_h2} is 16.62, and K_{_S_h2} is 3.21 × 10^–5. The model was calibrated with 150 days operation values measured in the field. The subsequent 100 days on-site values were used to validate the model, and the results obtained by the simulations were in good agreement. This study provides a meaningful and theoretical model guidance for full-scale wastewater anaerobic biochemical treatment simulation.

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.

Moving average convergence and divergence indexes based online intelligent expert diagnosis system for anaerobic wastewater treatment process

Anaerobic wastewater treatment process is efficient but unstable due to various disturbances, such as refractory organics and influent organic overloading. Therefore, sensitive and accurate status diagnosis is important for reasonable control to improve the stability of anaerobic process. In this study, an online intelligent expert diagnosis system for anaerobic process was established based on moving average convergence and divergence (MACD) indexes of gas- and liquid-phase parameters, combined with online monitoring system and expert diagnosis database. The effect of this diagnosis system was verified through refractory organics and organic overloading shock experiments. Results showed that this diagnosis system could make rapid, accurate and comprehensive diagnosis, predictions and early-warning. MACD algorithm could enhance pattern recognition capacity of status parameters, overcome the lagging of anaerobic process and filter irregular noisy fluctuations of status parameters. MACD index of H₂ partial pressure is suitable as sensitive early-warning indicator in the initial shock stage.

History and Evolution of Modeling in Biotechnology: Modeling & Simulation, Application and Hardware Performance

Biological systems are typically composed of highly interconnected subunits and possess an inherent complexity that make monitoring, control and optimization of a bioprocess a challenging task. Today a toolset of modeling techniques can provide guidance in understanding complexity and in meeting those challenges. Over the last four decades, computational performance increased exponentially. This increase in hardware capacity allowed ever more detailed and computationally intensive models approaching a "one-to-one" representation of the biological reality. Fueled by governmental guidelines like the PAT initiative of the FDA, novel soft sensors and techniques were developed in the past to ensure product quality and provide data in real time. The estimation of current process state and prediction of future process course eventually enabled dynamic process control. In this review, past, present and envisioned future of models in biotechnology are compared and discussed with regard to application in process monitoring, control and optimization. In addition, hardware requirements and availability to fit the needs of increasingly more complex models are summarized. The major techniques and diverse approaches of modeling in industrial biotechnology are compared, and current as well as future trends and perspectives are outlined.

Influence of Pre-Hydrolysis on Sewage Treatment in an Up-Flow Anaerobic Sludge BLANKET (UASB) Reactor: A Review

The up-flow anaerobic sludge blanket (UASB) process has emerged as a promising high-rate anaerobic digestion technology for the treatment of low- to high-strength soluble and complex wastewaters. Sewage, a complex wastewater, contains 30–70% particulate chemical oxygen demand (CODP). These particulate organics degrade at a slower rate than the soluble organics found in sewage. Accumulation of non-degraded suspended solids can lead to a reduction of active biomass in the reactor and hence a deterioration in its performance in terms of acid accumulation and poor biogas production. Hydrolysis of the CODP in sewage prior to UASB reactor will ensure an increased organic loading rate and better UASB performance. While single-stage UASB reactors have been studied extensively, the two-phase full-scale treatment approach (i.e., a hydrolysis unit followed by an UASB reactor) has still not yet been commercialized worldwide. The concept of treating sewage containing particulate organics via a two-phase approach involves first hydrolyzing and acidifying the volatile suspended solids without losing carbon (as methane) in the first reactor and then treating the soluble sewage in the UASB reactor. This work reviews the available literature to outline critical findings related to the treatment of sewage with and without hydrolysis before the UASB reactor.