Butanol production via vacuum fermentation: An economic evaluation of operating strategies

Microbial shifts and VFA production in the optimization of anaerobic digestion by thermal hydrolysis coupled with vacuum fermentation

This study investigated a novel thermal-hydrolysis combined with a vacuum fermentation system for high-grade volatile fatty acids (VFA) recovery, and the corresponding changes in the microbial community. Four systems with and without hydrothermal pre-treatment (HTP) and vacuum were mobilized; results revealed that integration of HTP with vacuum has the highest potential in terms of VFA recovery, sludge disintegration, and solid reduction. HTP and vacuum fermentation systems were associated with the highest COD solubilization (45 %), and VFA yield (0.32 g COD/g VSS added). Vacuum fermenters with and without pre-treatment have the highest specific denitrification rates of 7.6 and 7.2 mg NO3-N/g VSS.h, respectively, compared to all other samples and control (acetate). Changes brought about by vacuum fermentation included a shift in the microbial community toward enriching fermenters, mainly Caprothermobacteria and Thermotagea, responsible for VFA production.

Synthetic biology approaches and bioseparations in syngas fermentation

Fossil fuel use drives greenhouse gas emissions and climate change, highlighting the need for alternatives like biomass-derived syngas. Syngas, mainly H2 and CO, is produced via biomass gasification and offers a solution to environmental challenges. Syngas fermentation through the Wood-Ljungdahl pathway yields valuable chemicals under mild conditions. However, challenges in scaling up persist due to issues like unpredictable syngas composition and microbial fermentation contamination. This review covers advancements in genetic tools and metabolic engineering to expand product range, highlighting crucial enabling technologies that expedite strain development for acetogens and other non-model organisms. This review paper provides an in-depth exploration of syngas fermentation, covering microorganisms, gas composition effects, separation techniques, techno economic analysis, and commercialization efforts.

Probabilistic Bayesian Deep Learning Approach for Online Forecasting of Fed-Batch Fermentation

The microbial fermentation process often involves various biological metabolic reactions and chemical processes. The mixed bacterial culture process of 2-keto-l-gulonic acid has strong nonlinear and time-varying characteristics. In this study, a probabilistic Bayesian deep learning approach is proposed to obtain a highly accurate and robust prediction of product formation. The Bayesian optimized deep neural network (BODNN) is utilized as basic model for prediction, the structural parameters of which are optimized. Then, the training datasets are classified into different categories according to the prior evaluation of prediction error. The final forecasting is a weighted combination of BODNN models based on the Bayesian hybrid method. The weights can be interpreted as Bayesian posterior probabilities and are computed recursively. The validation of 95 industrial batches is carried out, and the average root mean square errors are 1.51 and 2.01% for 4 and 8 h ahead prediction, respectively. The results illustrate that the proposed approach can capture the dynamics of fermentation batches and is suitable for online process monitoring.