Cellulosic waste degradation by rumen-enhanced anaerobic digestion

Anaerobic digestion of lignocellulosic material is carried out effectively in many natural microbial ecosystems including the rumen. A rumen-enhanced anaerobic sequencing batch reactor was used to investigate cellulose degradation to give analysis of overall process stoichiometry and rates of hydrolysis. The reactor achieved VFA production rates of 207-236 mg COD/L/h at a loading rate of 10 g/L/d. Overloading of the reactor resulted in elevated production of propionic acid, and on occasion, the presence of succinic acid. With improvements in mixing and solids wasting, the anaerobic sequencing batch reactor system could enable full-scale application of the process for treatment of cellulosic waste material.

Anaerobic digestion process for biogas and biomolecules production: microflora identification and characterization

The anaerobic process was efficient in organic matter removal. During the process, an interesting compound as quercetin was produced inside of reactor. Phylogenetic analysis showed the presence of phylotypes affiliated with gamma-Proteobacteria, Choroflexi, and Bacteroidetes. Archaea were represented by phylotypes belonging to the genus Methanosarcina and Methanosaeta.

Bioactive compounds through anaerobic digestion of heterotrophic microalgae residues

Several important biomolecules are available into anaerobically digested effluents that were obtained from the biodiesel production process using heterotrophically grown microalga Chlorella protothecoides. Defatted microalgae residues and crude glycerol may undergo anaerobic digestion, separately and in admixture, providing methane/hydrogen and a digestate exploitable for agriculture applications. Furthermore, industrial interesting bioactive compounds such as polyphenols provided with antioxidant activity can be obtained. Anaerobic process offers a promising chance and can be advantageously combined with algae lipid-extraction techniques in order to make it more sustainable.

Ecology, physiology and performance in high-rate anaerobic digestion

The design demands on water and sanitation engineers are rapidly changing. The global population is set to rise from 7 billion to 10 billion by 2083. Urbanisation in developing regions is increasing at such a rate that a predicted 56% of the global population will live in an urban setting by 2025. Compounding these problems, the global water and energy crises are impacting the Global North and South alike. High-rate anaerobic digestion offers a low-cost, low-energy treatment alternative to the energy intensive aerobic technologies used today. Widespread implementation however is hindered by the lack of capacity to engineer high-rate anaerobic digestion for the treatment of complex wastes such as sewage. This thesis utilises the Expanded Granular Sludge Bed bioreactor (EGSB) as a model system in which to study the ecology, physiology and performance of high-rate anaerobic digestion of complex wastes. The impacts of a range of engineered parameters including reactor geometry, wastewater type, operating temperature and organic loading rate are systematically investigated using lab-scale EGSB bioreactors. Next generation sequencing of 16S amplicons is utilised as a means of monitoring microbial ecology. Microbial community physiology is monitored by means of specific methanogenic activity testing and a range of physical and chemical methods are applied to assess reactor performance. Finally, the limit state approach is trialled as a method for testing the EGSB and is proposed as a standard method for biotechnology testing enabling improved process control at full-scale. The arising data is assessed both qualitatively and quantitatively. Lab-scale reactor design is demonstrated to significantly influence the spatial distribution of the underlying ecology and community physiology in lab-scale reactors, a vital finding for both researchers and full-scale plant operators responsible for monitoring EGSB reactors. Recurrent trends in the data indicate that hydrogenotrophic methanogenesis dominates in high-rate anaerobic digestion at both full- and lab-scale when subject to engineered or operational stresses including low-temperature and variable feeding regimes. This is of relevance for those seeking to define new directions in fundamental understanding of syntrophic and competitive relations in methanogenic communities and also to design engineers in determining operating parameters for full-scale digesters. The adoption of the limit state approach enabled identification of biological indicators providing early warning of failure under high-solids loading, a vital insight for those currently working empirically towards the development of new biotechnologies at lab-scale.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Thermodynamic efficiency of carbon capture and utilisation in anaerobic batch digestion process

Carbon capture and storage (CCS) in the oil and water industries is becoming common and a significant consumer of energy typically requiring 150–450 °C and or several hundred bar pressure [1] particularly in geological deposition. A biological carbon capture and conversion has been considered in conventional anaerobic digestion processes. The process has been utilised in biological mixed culture, where acetoclastic bacteria and hydrogenophilic methanogens play a major key role in the utilisation of carbon dioxide. However, the bio catalytic microorganisms, hydrogenophilic methanogens are reported to be unstable with acetoclastic bacteria. In this work the biochemical thermodynamic efficiency was investigated for the stabilisation of the microbial process in carbon capture and utilisation. The authors observed that a thermodynamic efficiency of biological carbon capture and utilisation (BCCU) had 32% of overall reduction in yield of carbon dioxide with complimentary increase of 30% in yield of methane, while the process was overall endothermic. Total consumption of energy (≈0.33 MJ l−1) was estimated for the carbonate solubility (0.1 mol l−1) in batched BCCU. This has a major influence on microbial composition in the bioreactor. This thermodynamic study is an essential tool to aid the understanding of the interactions between operating parameters and the mixed microbial culture.

Anaerobic retting of banana and arecanut wastes in a plug flow digester for recovery of fiber, biogas and compost

Leaves and leaf sheath of banana and areca husk (Areca catechu) constitute an important component of urban solid waste (USW) in India which are difficult to degrade under normal windrow composting conditions. A successful method of anaerobic digestion built around the fermentation properties of these feedstock has been evolved which uses no moving parts, pretreatment or energy input while enabling recovery of four products: fiber, biogas, compost and pest repellent. An SRT of 27 d and 35 d was found to be optimum for fiber recovery for banana leaf and areca husk, respectively. Banana leaf showed a degradation pattern different from other leaves with slow pectin-1 degradation (80%) and 40% lignin removal in 27 d SRT. Areca husk however, showed a degradation pattern similar to other plant biomass. Mass recovery levels for banana leaf were fiber-20%, biogas-70% (400 ml/g TS) and compost-10%. For areca husk recovery was fiber-50%, biogas-45% (250 ml/g TS) and compost-5%. (C) 2012 Elsevier Inc. All rights reserved.

State Estimation for Anaerobic Digesters using the ADM1

The optimization of full-scale biogas plant operation is of great importance to make biomass a competitive source of renewable energy. The implementation of innovative control and optimization algorithms, such as Nonlinear Model Predictive Control, requires an online estimation of operating states of biogas plants. This state estimation allows for optimal control and operating decisions according to the actual state of a plant. In this paper such a state estimator is developed using a calibrated simulation model of a full-scale biogas plant, which is based on the Anaerobic Digestion Model No.1. The use of advanced pattern recognition methods shows that model states can be predicted from basic online measurements such as biogas production, CH4 and CO2 content in the biogas, pH value and substrate feed volume of known substrates. The machine learning methods used are trained and evaluated using synthetic data created with the biogas plant model simulating over a wide range of possible plant operating regions. Results show that the operating state vector of the modelled anaerobic digestion process can be predicted with an overall accuracy of about 90%. This facilitates the application of state-based optimization and control algorithms on full-scale biogas plants and therefore fosters the production of eco-friendly energy from biomass.