Anaerobic Digestion Modelling

CFD modelling and simulation of anaerobic digestion reactors for energy generation from organic wastes: A comprehensive review

Anaerobic digestion (AD) has been recognized as one of the most viable options for the treatment of a wide range of waste materials. Complex structure of wastes is safely broken down to destroy pollutants and pathogens. Biogas is produced as a by-product of this process which is considered as a clean energy resource. However, provision of controlled environment for microbial activities is critical to ensure the required process efficiency. This can only be achieved with the efficient design of controlled vessels used for anaerobic digestion, termed as AD reactors. AD functions such as mixing, hydrodynamics, multiphase interaction, heat transfer, temperature distribution and bio kinetics are significantly affected by the reactor shape, design and configurations, hence making it essential to optimize the reactor design before installation at large scale. Mostly, such optimization is carried out with the help of lab scale experimentations and testing protocols which result in high costs for repeating several design experiments. Computational fluid dynamics (CFD) is an applied mathematical tool which helps to understand and predict the fluid dynamics, heat flow as well as species transport in different domains. This approach contributes to minimize the experimental costs while optimizing the reactor configurations in less time. The current review is presented to summarize and discuss the core characteristics of AD process followed by concerned CFD attributes. Research gaps and critical challenges are identified in different aspects such as reactor design, and configuration, mixing, multiphase flow, heat transfer, biokinetics as well as machine learning approaches.

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant. It is difficult to predict COD in a real time mode, so it is better to use Complex Mathematical Modeling (CMM) for simulating AD process and forecasting output parameters. Therefore, different Machine Learning algorithms, such as Linear Regression, Decision Tree, Random Forest and Artificial Neural Networks, have been used for predicting effluent SCOD using data acquired from in situ anaerobic wastewater treatment system. The result of the predicted data using different algorithms were compared with experimental data of anaerobic system. It was observed that the Artificial Neural Networks is the most effective simulation technique that correlated with the experimental data with the mean absolute percentage error of 10.63 and R2 score of 0.96. This research proposes an efficient and reliable integrated modeling method for early prediction of the water quality in wastewater treatment.

Conceptual system for sustainable and next-generation wastewater resource recovery facilities

Shifting the concept of municipal wastewater treatment to recover resources is one of the key factors contributing to a sustainable society. A novel concept based on research is proposed to recover four main bio-based products from municipal wastewater while reaching the necessary regulatory standards. The main resource recovery units of the proposed system include upflow anaerobic sludge blanket reactor for the recovery of biogas (as product 1) from mainstream municipal wastewater after primary sedimentation. Sewage sludge is co-fermented with external organic waste such as food waste for volatile fatty acids (VFAs) production as precursors for other bio-based production. A portion of the VFA mixture (product 2) is used as carbon sources in the denitrification step of the nitrification/denitrification process as an alternative for nitrogen removal. The other alternative for nitrogen removal is the partial nitrification/anammx process. The VFA mixture is separated with nanofiltration/reverse osmosis membrane technology into low-carbon VFAs and high-carbon VFAs. Polyhydroxyalkanoate (as product 3) is produced from the low-carbon VFAs. Using membrane contactor-based processes and ion-exchange techniques, high-carbon VFAs are recovered as one-type VFA (pure VFA) and in ester forms (product 4). The nutrient-rich fermented and dewatered biosolid is applied as a fertilizer. The proposed units are seen as individual resource recovery systems as well as a concept of an integrated system. A qualitative environmental assessment of the proposed resource recovery units confirms the positive environmental impacts of the proposed system.

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.

Sewage-water treatment with bio-energy production and carbon capture and storage

Typical large-scale sewage-water treatments consume energy, occupy space and are unprofitable. This work evaluates a conceivable two-staged sewage-water treatment at 40,000 m³/d of sewage-water with sewage-sludge (totaling 10kg^COD/m³) that becomes a profitable bioenergy producer exporting reusable water and electricity, while promoting carbon capture. The first stage comprises microbial anaerobic digesters reducing the chemical oxygen demand (COD) by 95% and producing 60%mol methane biogas. The effluent waters enter the subsequent aerobic stage comprising microbial air-fed digesters that extend COD reduction to 99.7%. To simulate the process, up-to-date anaerobic/aerobic digester models were implemented. A biogas-combined-cycle power plant with/without post-combustion carbon capture is designed to match the biogas production, supplying electricity to the process and to the grid. Results comprehend electricity exportation of 13.21 MW (7.92 kWh/t^{Reusable-Water}) with -9.957t^CO2/h of negative carbon emission (-0.6 kg^CO2-Emitted/kg^COD-Removed). The biogas-combined-cycle without carbon capture achieves 21.08 MW of power exportation, while a 37.3% energy penalty arises if carbon capture is implemented. Configurations with/without carbon capture reach feasibility at 125 USD/MWh of electricity price, with respective net present values of 6.86 and 85.07 MMUSD and respective payback-times of 39 and 12 years. These results demonstrate that large-scale sewage-water treatment coupled to biogas-fired combined-cycles and carbon capture can achieve economically feasible bioenergy production with negative carbon emissions.

Decolorization of Synthetic Azo Dyes under Anaerobic Condition in A Continuous Stirred Tank Reactor

Biological treatment for textile wastewater always has a limitation in term of time of reaction and uncertainty along the process. This study focused on the decolorization of synthetic azo dyes in batch reactors with controlled thermotolerant anaerobic conditions. Less-volatile digested sludge collected from a palm oil biogas reactor was used as the organic biodegradation agent for azo dyes. Digested sludge contains high amounts of microbes with uncertain species viable for decolorization purposes. Sodium acetate trihydrate (C2H9NaO5) was used as carbon source and mixed with a specific composition of minimum salt media (MSM) in batch reactors as an additional nutrient. Digested sludge both in mesophilic (35°C) and thermophilic (55°C) conditions were found to be capable of decolorizing 100, 200 and 300 ppm of three types of azo dyes: Reactive Green 19 (45.56%, 69.73%; 63%, 73.49%; 70.02%, 75.92%), Reactive Orange 16 (46.08%, 78.4%; 64.21%, 85.52%; 74.95%, 85.91%) and Reactive Red 120 (29.11%, 85.32%; 63.35%, 87.69%; 72.02%, 89.5%) respectively after 7 days incubation time. Statistical analysis also showed that the anaerobic thermophilic conditions had significantly accelerated the decolorization process. The anaerobic thermophilic environment will be a good factor to include in future textile wastewater treatment plants.

The effect of brewery spent grain application on biogas yields and kinetics in co-digestion with sewage sludge

The present study examines the effect of introducing dried brewery spent grain (BSG), known as the main solid by-product of the brewery industry on biogas yields and kinetics in co-digestion with sewage sludge (SS). The experiment was conducted in semi-continuous anaerobic reactors (supplied once a day) operating under mesophilic conditions (35°C) at different hydraulic retention times (HRT) of 18 and 20 d. In co-digestion runs, the BSG mass to the feed volume ratio was constant and maintained 1:10.The results indicated that the addition of BSG did not influence the biogas production, by comparison with SS mono-digestion (control run). At HRT of 18 d, in the co-digestion run, the average methane yield was 0.27 m³ kg/VS_added, while in the control run the higher value of 0.29 m³ kg/VS_addedwas observed. However, there was no difference in terms of statistical significance. At HRT of 20 d, the methane yield was 0.21 m³ kg/VS_added for both mono- and co-digestion runs. In the BSG presence, the decrease in kinetic constant values was observed. As compared to SS mono-digestion, reductions by 21 and 35% were found at HRT of 20 and 18 d, respectively. However, due to the supplementation of the feedstock with BSG rich in organic compounds, the significantly enhanced energy profits were achieved with the highest value of approx. 40% and related to the longer HRT of 20 d. Importantly, the mono- and co-digestion process proceeded in stable manner. Therefore, the anaerobic co-digestion of SS and BSG might be considered as a cost-effective solution that could contribute to the energy self-efficiency of wastewater treatment plants (WWTPs) and sustainable waste management for breweries.

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.

Economic and Environmental Analysis of Small-Scale Anaerobic Digestion Plants on Irish Dairy Farms

The European Union’s (EU) climate and energy package requires all EU countries to reduce their greenhouse gas (GHG) emissions by 20% by 2020. Based on current trends, Ireland is on track to miss this target with a projected reduction of only 5% to 6%. The agriculture sector has consistently been the single largest contributor to Irish GHG emissions, representing 33% of all emissions in 2017. Small-scale anaerobic digestion (SSAD) holds promise as an attractive technology for the treatment of livestock manure and the organic fraction of municipal wastes, especially in low population communities or standalone waste treatment facilities. This study assesses the viability of SSAD in Ireland, by modelling the technical, economic, and environmental considerations of operating such plants on commercial Irish dairy farms. The study examines the integration of SSAD on dairy farms with various herd sizes ranging from 50 to 250 dairy cows, with co-digestion afforded by grass grown on available land. Results demonstrate feedstock quantities available on-farm to be sufficient to meet the farm’s energy needs with surplus energy exported, representing between 73% and 79% of the total energy generated. All scenarios investigated demonstrate a net CO2 reduction ranging between 2059–173,237 kg CO2-eq. yr−1. The study found SSAD systems to be profitable within the plant’s lifespan on farms with dairy herds sizes of >100 cows (with payback periods of 8–13 years). The simulated introduction of capital subvention grants similar to other EU countries was seen to significantly lower the plant payback periods. The insights generated from this study show SSAD to be an economically sustainable method for the mitigation of GHG emissions in the Irish agriculture sector.

Improving methane production and anaerobic digestion stability of food waste by extracting lipids and mixing it with sewage sludge

Anaerobic digestion (AD) of FW shows instability due to both the presence of high lipids and accumulation of volatile fatty acids. In this study, AD of food waste (FW) was optimized by removing lipids (LRFW) and by co-digestion with sewage sludge (1:1w/w on dry matter). The results obtained showed that lipids extraction increased FW methane yield from 400 to 418mL-gVS_added^-1 under mesophilic conditions (35°C) and from 426 to 531mL-gVS_added^-1 in thermophilic conditions (55°C). Two degradation phases (k₁ and k₂) described FW and LRFW degradation. In the thermophilic, LRFW-k₁ (0.1591d^-1) was slightly higher than that of FW (k₁ of 0.1543d^-1) and in the second stage FW-k₂ of 0.0552d^-1 was higher than that of LRFW (k₂ of 0.0117d^-1). The majority of LRFW was degraded in the first stage. FW and sewage sludge co-digestion reduced VFA accumulation, preventing media acidification and improving process stability.