Anaerobic digestion (AD) is a microbially-driven process enabling energy production. Microorganisms are the core of anaerobic digesters and play an important role in the succession of hydrolysis, acidogenesis, acetogenesis, and methanogenesis processes. The diversity of participating microbial communities can provide new information on digester performance for biomass valorization and biofuel production. In this study anaerobic systems were used, operating under mesophilic conditions that realized biodegradation processes of waste wheat straw pretreated with NaOH—a renewable source for hydrogen and methane production. These processes could be managed and optimized for hydrogen and methane separately but combining them in a two-stage system can lead to higher yields and a positive energy balance. The aim of the study was to depict a process of biohydrogen production from lignocellulosic waste followed by a second one leading to the production of biomethane. Archaeal and bacterial consortia in a two-stage system operating with wheat straw were identified for the first time and the role of the most important representatives was elucidated. The mixed cultures were identified by the molecular-biological methods of metagenomics. The results showed that biohydrogen generation is most probably due to the presence of Proteiniphilum saccharofermentans, which was 28.2% to 45.4% of the microbial community in the first and the second bioreactor, respectively. Archaeal representatives belonging to Methanobacterium formicicum (0.71% of the community), Methanosarcina spelaei (0.03%), Methanothrix soehngenii (0.012%), and Methanobacterium beijingense (0.01%) were proven in the methane-generating reactor. The correlation between substrate degradation and biogas accumulation was calculated, together with the profile of fatty acids as intermediates produced during the processes. The hydrogen concentration in the biogas reached 14.43%, and the Methane concentration was 69%. Calculations of the energy yield during the two-stage process showed 1195.89 kWh·t⁻¹ compared to a 361.62 kWh·t⁻¹ cumulative yield of energy carrier for a one-stage process.

Anaerobic digestion (AD) is a widespread biological process treating organic waste for green energy production. In this study, wheat straw and corn stalks without any harsh preliminary treatment were collected as a renewable source to be employed in a laboratory-scale digester to produce biogas/biomethane. Processes parameters of temperature, pH, total solids, volatile solid, concentration of volatile fatty acids (VFA), and cellulose concentration, were followed. The volume of biogas produced was measured. The impact of organic loading was stated, showing that the process at 55 °C tolerated a higher substrate load, up to 45 g/L. Further substrate increase did not lead to biogas accumulation increase, probably due to inhibition or mass transfer limitations. After a 12-day anaerobic digestion process, cumulative volumes of biogas yields were 4.78 L for 1 L of the bioreactor working volume with substrate loading 30 g/L of wheat straw, 7.39 L for 40 g/L and 8.22 L for 45 g/L. The degree of biodegradation was calculated to be 68.9%, 74% and 72%, respectively. A fast, effective process for biogas production was developed from native wheat straw, with the highest quantity of daily biogas production occurring between day 2 and day 5. Biomethane concentration in the biogas was 60%. An analysis of bacterial diversity by metagenomics revealed that more than one third of bacteria belonged to class Clostridia (32.9%), followed by Bacteroidia (21.5%), Betaproteobacteria (11.2%), Gammaproteobacteria (6.1%), and Alphaproteobacteria (5%). The most prominent genera among them were Proteiniphilum, Proteiniborus, and Pseudomonas. Archaeal share was 1.37% of the microflora in the thermophilic bioreactor, as the genera Methanocorpusculum, Methanobacterium, Methanomassiliicoccus, Methanoculleus, and Methanosarcina were the most abundant. A knowledge of the microbiome residing in the anaerobic digester can be further used for the development of more effective processes in conjunction with theidentified consortium.

The main purpose of this study was to identify the microbial communities (bacterial, archaeal and fungal) in a two-stage system of anaerobic bioreactors for the production of hydrogen and methane from the waste substrate—corn steep liquor. Wastes from the food industry are valuable resources with potential in biotechnological production because of their high organic matter contents. In addition, the production of hydrogen and methane, volatile fatty acids, reducing sugars and cellulose content was monitored. Two-stage anaerobic biodegradation processes were performed by microbial populations in the first hydrogen generating bioreactor (working volume of 3 dm³) and in the second methane-generating reactor (working volume of 15 dm³). Cumulative hydrogen yield reached 2000 cm³ or 670 cm³/L a day, while the methane production reached a maximum quantity of 3300 cm³ or 220 cm³/L a day. Microbial consortia in anaerobic digestion systems play an essential role for process optimization and biofuel production enhancement. The obtained results showed the possibility of conducting two separate processes—the hydrogenic (hydrolysis and acidogenesis) and methanogenic (acetogenesis and methanogenesis)—as two stages of anaerobic digestion to favor energy production under controlled conditions with corn steep liquor. The diversity of microorganisms as main participants in the processes in the bioreactors of the two-stage system was followed using metagenome sequencing and bioinformatics analysis. The obtained metagenomic data showed that the most abundant phylum in both bacterial communities was Firmicutes—58.61% and 36.49% in bioreactors 1 and 2, respectively. Phylum Actinobacteria were found in significant quantities (22.91%) in the microbial community in Bioreactor 1, whereas in Bioreactor 2, they were 2.1%. Bacteroidetes are present in both bioreactors. Phylum Euryarchaeota made up 0.4% of the contents in the first bioreactor and 11.4% in the second. As the dominant genera among methanogenic archaea are Methanothrix (8.03%) and Methanosarcina (3.39%), the main fungal representatives were Saccharomyces cerevisiae. New knowledge of anaerobic digestion mediated by novel microbial consortia could be widely used to convert different wastes to green energy.

Experimental studies of two-phase anaerobic digestion of corn steep liquor in semi-continuous automatic and semi-automatic modes of operation of a cascade of two anaerobic bioreactors with monitoring and control systems were performed. Corn steep liquor—a waste product from the process of treating corn grain for starch extraction—was used as a substrate in the process of anaerobic digestion with simultaneous hydrogen and methane production. The daily yields of biohydrogen in bioreactor 1 of the cascade (with a working volume of 8 dm³) are variable. In good operation, they are in the range of 0.7 to 1.0 L of biogas from a 1 dm³ working volume of the bioreactor, and the optimal pH is in the range of 5.0–5.5. The concentration of hydrogen in the biogas from the hydrogen bioreactor 1 is in the range of 14–34.7%. The daily yields of biomethane in bioreactor 2 of the cascade (with a working volume of 80 dm³) vary in the range 0.4 to 0.85 L of biogas from a 1 dm³ working volume of the bioreactor, and the concentration of methane in the biogas from bioreactor 2 is high and remains practically constant (in the range 65–69%). At a dilution rate of 0.4 day⁻¹ and an organic loading rate of 20 gL for bioreactor 1, respectively, and a dilution rate of 0.05 day⁻¹ for bioreactor 2, the best results were obtained. The computer control system is presented. Some energetical considerations were discussed.

Traditional milk products, widely consumed in many countries for centuries, have been drawing renewed attention in recent years as sources of bacteria with possible bioprotective properties. One such product for which only limited information exists is the traditional Bulgarian “katak”. This fermented yogurt-like product, renowned for its taste and long-lasting properties, possesses specific sensory characteristics. In this study, 18 lactic acid bacteria (LABs) were isolated from artisanal samples made in the Northwest part of Bulgaria. A polyphasic taxonomic approach combining classical phenotypic and molecular taxonomic methods, such as multiplex PCR, 16S rDNA sequencing, and MALDI-TOF MS, was applied, leading to the identification of 13 strains. The dominance of Lactiplantibacillus plantarum was confirmed. In vitro tests with the identified strains in model systems showed a promising broad strain-specific spectrum of activity against food-borne and human pathogens (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli). Non-purified Lactobacillus postbiotics, produced during fermentation in skimmed and soya milks and in MRS broth, were estimated as limiting agents of virulence factors. The LAB’s production of lactate, acetate, and butyrate is a promising probiotic feature. A further characterization of the active strains and analysis of the purified post-metabolites are needed and are still in progress.

Artificial intelligence (AI) has emerged as an innovative approach to the computer modeling and optimization of anaerobic digestion (AD) and anaerobic co-digestion (AcoD) processes. AI-based algorithms are ideally suited to capture the complex nonlinear behavior of these processes. Compared to conventional methods and models, AI-based algorithms have made modeling these processes much easier. Various AI algorithms, including multivariate statistical analyses, tree-based machine learning, nature-inspired optimization, support vector machine, and artificial neural networks (ANN) have been widely used to model the AD and AcoD processes. Researchers have successfully used stand-alone and hybrid ANMs to predict biogas yield and composition, as well as for efficient process monitoring and control. Furthermore, the development of advanced optimization algorithms, including genetic algorithms and particle swarm optimization, helps to optimize the ratio of mixing of co-substrates in AcoD and important process parameters (i.e., temperature (T), pH, retention time, total solids and volatile solids). This review discusses AI applications for AD and AcoD process modeling, optimization, prediction of unknown parameters and variables, and real-time monitoring and control. A critical comparison is made with some of the popular mathematical models and algorithms for monitoring and optimization designed on their basis. The review presents also future research directions in this area and some of our own results.