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This research introduces a fractional high-order sliding mode control (FHOSMC) method that utilises an inverse integral fractional order, 0 < β < 1 , as the high order on the FHOSMC reaching law, exhibiting a novel contribution in the related field of study. The application of the proposed approach into a bioreactor system via diffeomorphism operations demonstrates a notable improvement in the management of the bioreactor dynamics versus classic controllers. The numerical findings highlight an improved precision in tracking reference signals and an enhanced plant stability compared to proportional–integral–derivative (PID) controller implementations within challenging disturbance scenarios. The FHOSMC effectively maintains the biomass concentration at desired levels, reducing the wear of the system as well as implementation expenses. Furthermore, the theoretical analysis of the convergence within time indicates substantial potential for further enhancements. Subsequent studies might focus on extending this control approach to bioreactor systems that integrate sensor technologies and the formulation of adaptive algorithms for real-time adjustments of β -type fractional-orders. [ABSTRACT FROM AUTHOR]

Hemi-pyocyanin (HPC) is a heterocyclic nitrogenous compound with some reported potential medical effects. The current report aimed to investigate the potential use of organic industrial waste for the production of HPC via microbial fermentation. The novel antidiabetic activity of HPC was also accessed and reported in this work. A peanut oil processing by-product (groundnut cake) was screened as the best substrate for Pseudomonas aeruginosa TUN03 conversion to obtain high-yield HPC. This compound was further produced in a 14 L bioreactor system on a large scale (6 L per pilot) and reached higher productivity (35.1 μg/mL) in a shorter time course of cultivation (8 h) compared to fermentation on a small scale in flasks (19.5 μg/mL; 3 days of fermentation). On assessing its activity, HPC demonstrated potent inhibition against α-glucosidase, an antidiabetic enzyme, with a low IC50 value (0.572 mg/mL) and a maximum inhibition rate of 100%. In an in silico study, HPC was found to inhibit α-glucosidase with a good binding energy score (−9.0 kcal/mol) via interaction with amino acids Lys156, Leu313, and Arg315 at the active site, and three bonds (1 H-acceptor and 2 pi-H) were generated. The data from five Lipkin's rules and ADMET-based pharmacokinetics and pharmacology revealed that HPC possesses drug-like properties and good ADMET properties within the required allotted limitations. The data obtained in the current work highlighted the potential application of groundnut cakes for the eco-friendly and scaled-up production of HPC, a new anti-α-glucosidase agent that should be further investigated for type 2 diabetes management. [ABSTRACT FROM AUTHOR]

In the scope of a broader study about wine acetification, previous works concluded that using a single bioreactor hindered simultaneously reaching high productivities with high substrate consumption and the use of two serially arranged bioreactors (TSAB) could achieve such goal. Then, the aim of this work is the optimization, using Karush–Kuhn–Tucker (KKT) conditions, of this TSAB using polynomial models previously obtained. The ranges for the operational variables leading to either maximum and minimum mean rate of acetification of 0.11 ≤ (rA)global ≤ 0.27 g acetic acid·(100 mL·h)−1 and acetic acid production of 14.7 ≤ Pm ≤ 36.6 g acetic acid·h−1 were identified; the results show that simultaneously maximizing (rA)global and Pm is not possible so, depending on the specific objective, different operational ranges must be used. Additionally, it is possible to reach a productivity close to the maximum one (34.6 ≤ Pm ≤ 35.5 g acetic acid·h−1) with an almost complete substrate use [0.2% ≤ Eu2 ≤ 1.5% (v/v)]. Finally, comparing the performance of the bioreactors operating in series and in parallel revealed that the former choice resulted in greater production. [ABSTRACT FROM AUTHOR]

Biological nitrogen removal (BNR) technologies are the most effective approaches for the remediation of environmental nitrogen pollutants from wastewater treatment plants (WWTPs). Presently, research is going on to elucidate the structure and function of BNR microbial communities and optimizing BNR treatment systems to enhance nitrogen removal efficiency. The literature on BNR microbial communities and experimental datasets is not unified across various repositories, while a uniform resource for the collection, annotation, and structuring of these BNR datasets is still unavailable. Herein, we present the Biological Nitrogen Removal Database (BNRdb), an integrated resource containing various manually curated BNR-related data. At present, BNRdb contains 23,308 microbial strains, 46 gene families, 24 enzymes, 18 reactions, 301 BNR treatment datasets, 860 BNR-associated next-generation sequencing datasets, and 6 common BNR bioreactor systems. BNRdb provides a user-friendly interface enabling interactive data browsing. To our knowledge, BNRdb is the first BNR data resource that systematically integrates BNR data from archaeal, bacterial, and fungal communities. We believe that BNRdb will contribute to a better understanding of BNR process and nitrogen bioremediation research. [ABSTRACT FROM AUTHOR]

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling. [ABSTRACT FROM AUTHOR]