The fed-batch cultivation is in many ways a benchmark for fermentation processes, and it has been an attractive choice for the biotechnological production of various products in the past decades. The majority of biopharmaceuticals that are presently undergoing clinical trials or are available on the market are manufactured through fed-batch fermentations. A crucial process parameter in fed-batch cultivations is the substrate feed rate, which directly influences the overall process productivity, product quality and process repeatability; henceforth, effective control of this parameter is imperative for a successful fed-batch fermentation process. Two distinct control strategies can be distinguished—open-loop and closed-loop (feedback) control. Each of these methods has its own set of benefits, limitations and suitability for specific bioprocesses. This article surveys and compares the most popular open- and closed-loop methods for substrate feed rate control in fed-batch fermentations. Emphasis is placed on model-predictive feed rate control (MPC)—a stand out among other methods that offers a promising application perspective. The authors also demonstrate a practical example of the implementation of a robust, flexible MPC solution that is suitable for various cultures and runs on standard computer hardware, thus overcoming one of the main reported MPC drawbacks—high computational requirements.

The biorefinery industry is witnessing a transition from fossil and chemical-based processes to more sustainable practices, with a growing emphasis on using renewable resources. Sophorolipids, a promising group of biosurfactants, present a viable substitute for conventionally produced surfactants. This study focuses on microbial fermentation using yeast and lipid substrate for sophorolipid production. The life cycle assessment (LCA) methodology was employed to identify environmental hotspots of the process and to assess the environmental benefits resulting from the replacement of raw rapeseed cooking oil (base scenario) with waste cooking oil, reduction of process electricity consumption, and increased sophorolipid yield. By compiling scenarios with the lowest environmental impact, a best-case scenario was created. The results revealed that the environmental impact of sophorolipid production could be reduced by 50% in the best-case scenario compared to the base scenario. This research provides valuable insights into the environmental optimization of the fermentation process and through the application of LCA highlights the potential for the reduction of negative environmental impact of sophorolipid production, contributing to the ongoing transition from petroleum oil and petrochemical refineries to sustainable biorefineries.

Crypthecodinium cohnii is a marine heterotrophic dinoflagellate that can accumulate high amounts of omega-3 polyunsaturated fatty acids (PUFAs), and thus has the potential to replace conventional PUFAs production with eco-friendlier technology. So far, C. cohnii cultivation has been mainly carried out with the use of yeast extract (YE) as a nitrogen source. In the present study, alternative carbon and nitrogen sources were studied: the extraction ethanol (EE), remaining after lipid extraction, as a carbon source, and dinoflagellate extract (DE) from recycled algae biomass C. cohnii as a source of carbon, nitrogen, and vitamins. In mediums with glucose and DE, the highest specific biomass growth rate reached a maximum of 1.012 h⁻¹, while the biomass yield from substrate reached 0.601 g·g⁻¹. EE as the carbon source, in comparison to pure ethanol, showed good results in terms of stimulating the biomass growth rate (an 18.5% increase in specific biomass growth rate was observed). DE supplement to the EE-based mediums promoted both the biomass growth (the specific growth rate reached 0.701 h⁻¹) and yield from the substrate (0.234 g·g⁻¹). The FTIR spectroscopy data showed that mediums supplemented with EE or DE promoted the accumulation of PUFAs/docosahexaenoic acid (DHA), when compared to mediums containing glucose and commercial YE.

The Kluyveromyces marxianus yeast recently has gained considerable attention due to its applicability in high-value-added product manufacturing. In order to intensify the biosynthesis rate of a target product, reaching high biomass concentrations in the reaction medium is mandatory. Fed-batch processes are an attractive and efficient way how to achieve high cell densities. However, depending on the physiology of the particular microbial strain, an optimal media composition should be used to avoid by-product synthesis and, subsequently, a decrease in overall process effi-ciency. Thus, the aim of the present study was to optimise the synthetic growth medium and feeding solution compositions (in terms of carbon, nitrogen, phosphorous, magnesium, and calcium concentrations) for high cell density K. marxianus fed‑batch cultivations. Additionally, the biomass yields from the vitamin mixture and other macro/microelements were identified. A model predictive control algorithm was successfully applied for a fed-batch cultivation control. Biomass growth and substrate consumption kinetics were compared with the mathematical model predictions. Finally, 2‑phenylethanol biosynthesis was induced and its productivity was estimated. The determined optimal macronutrient ratio for K. marxianus biomass growth was identified as C:N:P = 1:0.07:0.011. The maximal attained yeast biomass concentration was close to 70 g·L^-1 and the 2-PE biosynthesis rate was 0.372 g·L⁻¹·h⁻¹, with a yield of 74% from 2-phenylalanine.

Plant-based meat alternatives, exemplified by Impossible Foods’ Impossible Burger, offer a sustainable, ethical substitute for traditional meat, closely mimicking the taste and appearance of meat by utilizing soy leghemoglobin (LegH), a 16 kDa holoprotein found in soy plants structurally similar to heme in animal meat. Cultivation medium plays an important role in bioprocess development; however, medium development or optimization can be labor intensive, and thus the use of previously reported media can be enticing. In this study, we explored the expression of recombinant LegH in Pichia pastoris in various reported cultivation media (BSM, BMGY, FM22, D’Anjou, BSM/2, and RDM) and using different feeding approaches (µ-stat and mixed feed with sorbitol). Our findings indicate that optimization techniques tailored to the specific process did not increase LegH yields, highlighting the need to investigate strain-specific strategies. We also utilized the collected process data to create and train a novel artificial neural network-based soft sensor for estimating cell biomass, relying solely on standard bioreactor measurements (such as stirrer speed, dissolved oxygen, O₂ enrichment, base feed, glycerol feed, methanol feed, and reactor volume). This soft sensor proved to be robust and exhibited a strong correlation (3.72% WCW) with experimental data.