Modeling and validating Pseudomonas aeruginosa kinetic parameters based on simultaneous effect of bed temperature and moisture content using lignocellulosic substrate in packed-bed bioreactor

Poly(3-hydroxybutyrate) production using supplemented corn-processing byproducts through Cupriavidus necator via solid-state fermentation: Cultivation on flask and bioreactor scale

The widespread adoption of Poly(3-hydroxybutyrate) (PHB) encounters challenges due to its higher production costs compared to conventional plastics. To overcome this obstacle, this study investigates the use of low-cost raw materials and optimized production methods. Specifically, food processing byproducts such as corn germ and corn bran were utilized as solid substrates through solid-state fermentation, enriched with molasses and cheese whey. Employing the One Factor at a Time technique, we examined the effects of substrate composition, temperature, initial substrate moisture, molasses, and cheese whey on PHB production at the flask scale. Subsequently, experiments were conducted at the bioreactor scale to evaluate the influence of aeration. In flask-scale experiments, the highest PHB yield, reaching 4.1 (g/kg Initial Dry Weight Substrate) (IDWS) after 72 hours, was achieved using a substrate comprising a 1:1 mass ratio of corn germ to corn bran supplemented with 20 % (v/w) cheese whey. Furthermore, PHB production in a 0.5-L packed-bed bioreactor yielded a maximum of 8.4 (g/kg IDWS), indicating a more than 100 % increase in yield after 72 hours, with optimal results achieved at an aeration rate of 0.5 l/(kg IDWS. h).

A Critical Evaluation of Recent Studies on Packed-Bed Bioreactors for Solid-State Fermentation

Packed-bed bioreactors are often used for aerobic solid-state fermentation, since the forced aeration supplies O2 and removes metabolic heat from the bed. Motivated by the potential for applications in biorefineries, we review studies conducted on packed-bed bioreactors over the last decade, evaluating the insights these studies provide into how large-scale packed beds should be designed and operated. Many studies have used low superficial air velocities and suffer from preferential airflow, such that parts of the bed are not properly aerated. Moreover, some studies have proposed ineffective strategies, such as reversing the direction of the airflow or introducing air through perforated pipes within the bed. Additionally, many studies have used narrow water-jacketed packed-bed bioreactors, but these bioreactors do not reflect heat removal in wide large-scale packed beds, in which heat removal through the side walls makes a minor contribution. Finally, we conclude that, although some attention has been given to characterizing the porosities, water sorption isotherms and volumetric heat and mass transfer coefficients of substrate beds, this work needs to be extended to cover a wider range of solid substrates, and work needs to be done to characterize how these bed properties change due to microbial growth.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Biosurfactants: The green generation of speciality chemicals and potential production using Solid-State fermentation (SSF) technology

Surfactants are multipurpose products found in most sectors of contemporary industry. Their large-scale manufacturing has been mainly carried out using traditional chemical processes. Some of the chemical species involved in their production are considered hazardous and some industrial processes employing them categorised as "having potential negative impact on the environment". Biological surfactants have therefore been generally accepted worldwide as suitable sustainable greener alternatives. Biosurfactants exhibit the same functionalities of synthetic analogues while having the ability to synergize with other molecules improving performances; this strengthens the possibility of reaching different markets via innovative formulations. Recently, their use was suggested to help combat Covid-19. In this review, an analysis of recent bibliography is presented with descriptions, statistics, classifications, applications, advantages, and challenges; evincing the reasons why biosurfactants can be considered as the chemical specialities of the future. Finally, the uses of the solid-state fermentation as a production technology for biosurfactants is presented.