Effect of temperature on sugarcane ethanol fermentation: Kinetic modeling and validation under very-high-gravity fermentation conditions

Development of a model for the ethanol concentration limit as a function of temperature and initial substrate concentration using the yeast Saccharomyces cerevisiae

In this study, a model was developed to simulate the effect of temperature (T $T$ ) and initial substrate concentration (S 0 ${S}_{0}$ ) on the ethanol concentration limit (P max ${P}_{\max }$ ) using the yeast Saccharomyces cerevisiae. To achieve this, regressions were performed using data provided by other authors forP max ${P}_{\max }$ to establish a model dependent onT $T$ andS 0 ${S}_{0}$ capable of predicting results with statistical significance. After constructing the model, a response surface was generated to determine the conditions whereP max ${P}_{\max }$ reaches higher values: temperatures between 28°C and 32°C and an initial substrate concentration around 200 g/L. Thus, the proposed model is consistent with the observations that increasing temperatures decrease the ethanol concentration obtained, and substrate concentrations above 200 g/L lead to a reduction in ethanol concentration even at low temperatures such as 28°C.

Enhancing Fermentation Process Monitoring through Data-Driven Modeling and Synthetic Time Series Generation

Soft sensors based on deep learning regression models are promising approaches to predict real-time fermentation process quality measurements. However, experimental datasets are generally sparse and may contain outliers or corrupted data. This leads to insufficient model prediction performance. Therefore, datasets with a fully distributed solution space are required that enable effective exploration during model training. In this study, the robustness and predictive capability of the underlying model of a soft sensor was improved by generating synthetic datasets for training. The monitoring of intensified ethanol fermentation is used as a case study. Variational autoencoders were employed to create synthetic datasets, which were then combined with original datasets (experimental) to train neural network regression models. These models were tested on original versus augmented datasets to assess prediction improvements. Using the augmented datasets, the soft sensor predictive capability improved by 34%, and variability was reduced by 82%, based on R2 scores. The proposed method offers significant time and cost savings for dataset generation for the deep learning modeling of ethanol fermentation and can be easily adapted to other fermentation processes. This work contributes to the advancement of soft sensor technology, providing practical solutions for enhancing reliability and robustness in large-scale production.

Repeated-Batch Ethanol Fermentation from Sweet Sorghum Stem Juice under a Very High Gravity Condition Using a Stirred Tank Bioreactor Coupled with a Column Bioreactor by Immobilized Saccharomyces cerevisiae

The ethanol fermentation efficiency of sweet sorghum stem juice (SSJ) under a very high gravity (VHG) condition (250 g/L of sugar) was improved by immobilized Saccharomyces cerevisiae SSJKKU01, using a stirred tank bioreactor (STR) coupled with a column bioreactor (CR). Dried rattan pieces (as carriers for cell immobilization) at 50% of the working volume of the CR were suitable for use in a batch ethanol fermentation. The average ethanol concentration (PE) and ethanol productivity (QP) of repeated-batch fermentation in the CR for eight successive cycles were 109.85 g/L and 1.88 g/L⋅h, respectively. Then an STR coupled with a CR was applied for repeated-batch ethanol fermentation in two systems. System I was an STR (1.8 L working volume), and System II was an STR (1 L) coupled with a CR, referred to as a CR-F (0.8 L). Both systems were connected to a new CR, called CR-I, containing sterile dried rattan pieces at 50% of its working volume. Active yeast cells were inoculated only into the STR, and the medium circulation rate between bioreactors was 5.2 mL/min. The results showed that at least eight successive cycles could be operated with an average PE of 108.51 g/L for System I and 109.44 g/L for System II. The average QP and SC values of both systems were also similar, with values of 1.87 to 1.88 g/L⋅h and 93 to 94%, respectively. The morphology of the carriers with and without immobilized cells before and after the fermentation was investigated. The obtained results demonstrated that a repeated-batch fermentation by immobilized cells on rattan pieces, using an STR coupled with a CR, was successfully used to produce high levels of ethanol from SSJ under a VHG condition.

Mathematical Modeling of Fed-Batch Ethanol Fermentation Under Very High Gravity and High Cell Density at Different Temperatures

The use of more appropriate kinetic models can assist in improving ethanol fermentation under conditions of very high gravity (VHG) and high cell density (HCD), in order to obtain higher amounts of ethanol in the broth combined with high productivity. The aim of this study was to model fed-batch ethanol fermentation under VHG/HCD conditions, at different temperatures, considering three types of inhibition (substrate, ethanol, and cells). Fermentations were carried out using different temperatures (28 ≤ [Formula: see text] (°C) ≤ 34), inoculum sizes (50 ≤ [Formula: see text] (g L^-1) ≤ 125), and substrate concentrations in the must (258 ≤ [Formula: see text] (g L^-1) ≤ 436). In the proposed model, the cell inhibition power parameter varied with the temperature and inoculum size, while the cell yield coefficient varied with inoculum size and substrate concentration in the must. Hence, it was possible to propose correlations for the cell inhibition power parameter ([Formula: see text]) and for the cell yield coefficient ([Formula: see text]), as functions of the fermentation conditions. Simulations of fed-batch ethanol fermentations at different temperatures, under VHG/HCD conditions, were performed using the proposed correlations. Experimental validation showed that the model was able to accurately predict the dynamic behavior of the fermentations in terms of the concentrations of viable cells, total cells, ethanol, and substrate.

Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors

Various bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L^-1 ·h^-1 and a yield of 0.48 g_ethanol .g_substrate ^-1 , which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L^-1 ·h^-1 and 0.35-0.40 g_ethanol ·g_substrate ^-1 , respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.

High Gravity and Very High Gravity Fermentation of Sugarcane Molasses by Flocculating Saccharomyces cerevisiae: Experimental Investigation and Kinetic Modeling

Substantial progress has been made in ethanol fermentation technology under high gravity (HG) and very high gravity (VHG), which offer environmental and economic benefits. HG and VHG processes increase the productivity of ethanol, reduce distillation costs, and enable higher yields. The aim of the present study was to evaluate the use of sugarcane molasses as the medium component along with flocculating yeasts for fermentation in a fed-batch process employing this promising technology. We evaluated fed-batch fermentation, HG, and VHG involving a molasses-based medium with high concentrations of reducing sugars (209, 222, and 250 g/L). Fermentation of 222 g/L of total reducing sugars achieved 89.45% efficiency, with a final ethanol concentration of 104.4 g/L, whereas the highest productivity (2.98 g/(L.h)) was achieved with the fermentation of 209 g/L of total reducing sugars. The ethanol concentration achieved with the fermentation of 222 g/L of total reducing sugars was close to the value obtained for P'_max (105.35 g/L). The kinetic model provided a good fit to the experimental data regarding the fermentation of 222 g/L. The results revealed that sugarcane molasses and flocculating yeasts can be efficiently used in HG fermentation to reduce the costs of the process and achieve high ethanol titers.

Improvement of ethanol production in fed-batch fermentation using a mixture of sugarcane juice and molasse under very high-gravity conditions

Ethanol fermentation in very high gravity (VHG) saves energy consumption for ethanol distillation. As the technology offers high ethanol yield and low waste generation and it can be operated at low cost, it could be more efficient at an industrial scale than other ethanol production methods. This work studied ethanol production using a fed-batch bioreactor with a working volume of 1.5 L. The main objective of this research was evaluate the effects of temperature, sugar concentration, and cellular concentration using a Central Composite Design (CCD). Experimental conditions were selected using the surface response technique obtained from the CCD, and the results were validated to test the reproducibility. The following operating conditions were selected: temperature of 27.0 °C, sugar concentration 300.0 g/L, and cell concentration 15.0% (v/v). Under these conditions, after 30 h of fermentation the ethanol concentration, productivity and yield were 135.0 g/L, 4.42 g/(L·h) and 90.0%, respectively. All sugar was completely consumed.

Improvement of ethanol production by extractive fed-batch fermentation in a drop column bioreactor

The use of fed-batch extractive fermentation can overcome inhibitory effects caused by the substrate and ethanol to the yeast cells, since it allows regulate the substrate concentration and remove the product as it is produced. The present study describes the modelling and experimental validation of ethanol production in fed-batch extractive fermentation with in situ ethanol removal by oleic acid in a non-conventional drop column bioreactor (DCB) operated under industrial conditions. The model developed using the hybrid Andrews-Levenspiel equation and ethanol distribution coefficient parameter (K_DE) provided an excellent description of the fed-batch extractive ethanol fermentation process with oleic acid. Furthermore, extractive fed-batch fermentation allowed the feed up to 306.6 kg m^-3 of substrate (total reducing sugars), with total ethanol concentration in extractive fermentation in the ranging 100.3-139.8 kg m^-3 (12.7-17.7 ºGL), 19.9-67.2% higher when compared with the conventional process without ethanol removal. Moreover, this process has the advantage of less effluent generated and energy consumption for ethanol recovery when compared to the conventional process.

Advances in yeast alcoholic fermentations for the production of bioethanol, beer and wine

Yeasts have a long-standing relationship with humankind that has widened in recent years to encompass production of diverse foods, beverages, fuels and medicines. Here, key advances in the field of yeast fermentation applied to alcohol production, which represents the predominant product of industrial biotechnology, will be presented. More specifically, we have selected industries focused in producing bioethanol, beer and wine. In these bioprocesses, yeasts from the genus Saccharomyces are still the main players, with Saccharomyces cerevisiae recognized as the preeminent industrial ethanologen. However, the growing demand for new products has opened the door to diverse yeasts, including non-Saccharomyces strains. Furthermore, the development of synthetic media that successfully simulate industrial fermentation medium will be discussed along with a general overview of yeast fermentation modeling.

Formation of aromatic compounds precursors during fermentation of Criollo and Forastero cocoa

There are three main genetic varieties of cocoa (Theobroma cacao L) used in chocolate making: Forastero, Trinitario and Criollo, which are distinguished by their aroma, an attribute that determines their quality. Criollo cocoa is of the highest quality and is used in the manufacture of fine chocolates because of its fruity aroma. The aroma of Criollo cocoa is defined by volatile compounds such as pyrazines and aldehydes, which are formed during roasting of the bean, from aroma precursors (reducing sugars and free amino acids) that are generated inside the bean via enzymatic reactions during fermentation; for this reason, fermentation is the most important process in the value chain. This review discusses the production of aroma precursors of Criollo and Forastero cocoa by studying the kinetics of spontaneous fermentation and the role of starter cultures to produce aroma precursors. Fine aroma precursors produced in the pulp during the fermentation phase will migrate into the bean when it's permeability is improved and then retained during the drying phase. Diffusion of aroma precursors into the cocoa bean may be possible, this process is mathematically characterized by the coefficient of molecular diffusion D, which describe the process of mass transfer via Fick's Second Law. The current state of knowledge is analyzed based on existing research and reports some gaps in the literature, suggesting future research that will be necessary for a better understanding of cocoa fermentation.

Improvement of ethanol and biogas production from sugarcane bagasse using sodium alkaline pretreatments

Sugarcane bagasse was pretreated with sodium carbonate, sodium sulfite, and sodium acetate in concentrations of 0.5 M and 0.25 M, as well as hydrothermal pretreatment, to break down its structural recalcitrance and improve biogas and ethanol production. The pretreatments were conducted at 100, 140, and 180 °C for 1 h. The highest biogas and ethanol production was observed for sugarcane bagasse pretreated with 0.5 M sodium carbonate solution at 140 °C, which was 239 ± 20 Nml CH₄/g VS, and 7.27 ± 0.70 g/l, respectively, containing gasoline equivalents of 164.2 ± 14.3 l/ton of raw bagasse and 147.8 ± 14.2 l/ton of raw bagasse, respectively. The highest gasoline equivalent was obtained for biogas production from the substrate pretreated with 0.5 M sodium sulfite solution at 100 °C (190.2 ± 2.1 l/ton of raw bagasse). In comparison to sodium carbonate and sodium sulfite, sodium acetate had less effect on biofuel production and was comparable with hydrothermal pretreatment. In contradiction to sodium acetate pretreated bagasse, in which increased pretreatment temperature intensified biofuel production, a reduction of biofuel production was observed for sodium carbonate and sodium sulfite pretreatment when temperature was increased from 140 to 180 °C. Besides considerable amounts of biofuel production at the best conditions obtained, over 762 and 543 kilotons of equivalent CO₂ can be reduced annually in Iran by biogas and ethanol production from sugarcane, respectively.