A comparison of methods for early prediction of anaerobic biogas potential on biologically treated municipal solid waste

Kinetic models in environmental biotechnological processes: Origin, derivation and applications

Environmental biotechnological processes encompass the utilization of microorganisms for various applications, such as wastewater treatment, bioproduct formation, and waste management. Kinetic modeling plays a crucial role in optimizing and designing these processes. This paper provides a comprehensive understanding of the kinetic models used in environmental biotechnological processes, focusing on the kinetics of microbial growth, bioproduct formation, substrate consumption, and pollutant degradation. Firstly, by investigating their origins, derivations, and development, we clarified the theoretical basis and practical implications of key models, such as the Gompertz, Logistic, first-order, Cone, Monod, Andrews, Shepherd, Stover-Kincannon, Grau, and Arrhenius models. Secondly, we highlighted the extension of the models from microbial growth kinetics to bioproduction kinetics, showcasing their versatility and applicability across different domains. In addition, critical parameters within the models were discussed, providing insights into their importance for characterizing and predicting biotechnological processes. Overall, this paper will deepen the understanding of biotechnological kinetic processes and lay the foundation for their practical applications.

Proteinase K impact on anaerobic co-digestion of modified biodegradable plastic and food waste: Step-by-step analysis with microorganism

This study was designed to explore the key impact of Proteinase K (PK) on every step of anaerobic co-digestion. The results of step-by-step experiments indicated that PK promoted the hydrolysis of biodegradable plastic by initiating self-hydrolysis reactions, directly promoting the hydrolysis step of anaerobic co-digestion. Subsequently, PK indirectly promoted the acidogenesis and acetogenesis steps by impacting the proliferation of acid-producing bacteria. Besides, it could also hydrolyze PLA. Thus, the lactic acid content peaked at 255.7 mg/L on the 5th day, representing an increase of 35.9 % compared to the condition without PK. Finally, PK indirectly promoted the methanogenesis step through its impact on the composition of methanogenic bacteria. This led to more food waste being digested into methane, 41.5 % compared to the condition without PK. This work served as an essential foundation for advancing the application of PK modified BP as a replacement for traditional plastics.

Biological methane potentials of food waste of different components: Methane yields, production kinetics, and element balance

This study assessed the methane production from food waste (FW) with dominant components of Meat (MFW), Fruit &Veg (VFW), Grain (GFW), Dairy (DFW), and the mixed feed of these components (MixFW). The high protein and lipid content FW (HPLFW) of MFW, DFW, and MixFW showed the methane yields of 337.0 ± 3.0, 307.4 ± 0.8, and 297.1 ± 1.2 ml-CH4/gCOD, respectively, while those for the high carbohydrate content FW (HCFW) of VFW and GFW were 238.3 ± 1.2 and 171.2 ± 0.3 ml-CH4/gCOD, respectively. A modified two-component kinetic (MTK) model was demonstrated to be the best to describe the methane production kinetics of both HPLFW and HCFW types of feeds. The element balance analysis revealed the element formula of the FW feeds and the methane-conversion organic content. The results obtained from this study showed that the high lipid and animal protein content increased the methane yield and biogas methane composition.

Prediction of the Long-Term Effect of Iron on Methane Yield in an Anaerobic Membrane Bioreactor Using Bayesian Network Meta-Analysis

A method for predicting the long-term effects of ferric on methane production was developed in an anaerobic membrane bioreactor treating food processing wastewater to provide management tools for maximizing methane recovery using ferric based on a batch test. The results demonstrated the accuracy of the predictions for both batch and long-term continuous operations using a Bayesian network meta-analysis based on the Gompertz model. The prediction bias of methane production for batch and continuous operations was minimized, from 11~19% to less than 0.5%. A biochemical methane potential-based Bayesian network meta-analysis suggested a maximum 2.55% ± 0.42% enhancement for Fe2.25. An anaerobic membrane bioreactor improved the methane yield by 2.27% and loading rate by 4.57% for Fe2.25, operating in the sequenced batch mode. The method allowed for a predictable methane yield enhancement based on the biochemical methane potential. Ferric enhanced the biochemical methane potential in batch tests and the methane yield in a continuously operated reactor by a maximum of 8.20% and 7.61% for Fe2.25, respectively. Copper demonstrated a higher methane (18.91%) and sludge yield (17.22%) in batch but faded in the continuous operation (0.32% of methane yield). The enhancement was primarily due to changing the kinetic patterns for the last period, i.e., increasing the second methane production peak (k₇₁), bringing forward the second peak (λ₇, λ₈), and prolonging the second period (k₆₂). The dual exponential function demonstrated a better fit in the last three stages (after the first peak), which implied that syntrophic methanogenesis with a ferric shuttle played a primary role in the last three methane production periods, in which long-term effects were sustained, as the Bayesian network meta-analysis predicted.

Ammonia stress decreased biomarker genes of acetoclastic methanogenesis and second peak of production rates during anaerobic digestion of swine manure

Research shows that anaerobic digestion could acclimate to ammonia stress; however, the acclimation remained unaddressed. In this study, evolution of microbial community, functional gene, and pathway was linked with apparent kinetic and performance in unacclimated inoculum under ammonia stress, to deepen understanding of the acclimation. The second peak in production rate demonstrated crucial kinetic changes under ammonia stress. The methane loss was mainly protein in residual COD. Metagenomic showed initial inhibition in all methane metabolism pathways under ammonia stress, and recovery in acetate uptake was the key to ammonia acclimation. The acclimation was found in alternative pathway of Acetyl-CoA (CH₃CO-S-CoA) synthesis from acetate, accompanying by syntrophic methanogenesis. Ammonia inhibited acetoclastic methanogenesis by competing CH₃-CO-P_i with pta and formed speculative sediment CH₃-CO-PO₄[NH₄]₂. Biomarker of methanogenesis kinetic was suggested as mcr, hdr, and mch. The biomarker could indicate acclimation stages to ammonia, empowering anaerobic digestion by early warning of methane loss.

Existing Empirical Kinetic Models in Biochemical Methane Potential (BMP) Testing, Their Selection and Numerical Solution

Biochemical Methane Potential (BMP) tests are a crucial part of feasibility studies to estimate energy recovery opportunities from organic wastes and wastewater. Despite the large number of publications dedicated to BMP testing and numerous attempts to standardize procedures, there is no “one size fits all” mathematical model to describe biomethane formation kinetic precisely. Importantly, the kinetics models are utilized for treatability estimation and modeling processes for the purpose of scale-up. A numerical computation approach is a widely used method to determine model coefficients, as a replacement for the previously used linearization approach. However, it requires more information for each model and some range of coefficients to iterate through. This study considers existing empirical models used to describe biomethane formation process in BMP testing, clarifies model nomenclature, presents equations usable for numerical computation of kinetic parameters as piece-wise defined functions, defines the limits for model coefficients, and collects and analyzes criteria to evaluate and compare model goodness of fit.