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.

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 (KDE) 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.

Effect of severity factor on the hydrothermal pretreatment of sugarcane straw.

The recalcitrant structures of sugarcane straw and related lignocellulosic biomasses require a pretreatment step to enable a better enzymatic attack during the hydrolysis. Factors like the energy consumption and the formation of inhibitors require the optimization of the pretreatment step. Thus, the influence of different severity factors (SF) on hydrothermal (also called liquid hot water, LHW) pretreatment was evaluated using a factorial design 22 with central point. The obtained results showed that low values of SF (<3.39) did not promote reasonable alteration in the sugarcane straw structures, whereas high SF values (>4.70) resulted in loss of hydrolyzed sugars, generation of inhibitors such as furfural, and formation of pseudo-lignin structures, despite high hemicellulose removal (∼97%). The residence time exhibited low influence on LHW. An optimum condition was found for the process (10 min and 195 °C) with low cellulose solubilization (9.80%) and a reasonable hemicellulose removal (85.45%).

Defining research & development process targets through retro-techno-economic analysis: The sugarcane biorefinery case.

A new approach is reported for techno-economic analysis of lignocellulosic ethanol production. With this methodology, general targets for key process variables can be draw, a valuable feedback for Research & Development teams. An integrated first- and second-generation ethanol from sugarcane biorefinery is presented as a case study for the methodology, with the biomass pretreated by liquid hot water, followed by enzymatic hydrolysis of the cellulose fraction. The hemicellulose fraction may be either fermented or biodigested. The methodology was able to identify the main variables that affect the process global economic performance: enzyme load in the cellulose hydrolysis reactor, cellulose-to-glucose, and xylose-to-ethanol yields. Windows of feasible operation are the graphical output of the methodology, outlining regions to be further explored experimentally. One example of quantitative result is that the maximum feasible enzyme load was 11.3 FPU/gcellulose when xylose is fermented to ethanol and 7.7 FPU/gcellulose when xylose is biodigested.

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study.

The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic beta-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling's T(2) was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.

Comparing the performance of multilayer perceptrons networks and neuro-fuzzy systems for on-line inference of Bacillus megaterium cellular concentrations.

Penicillin G acylase (PGA) is one of the most important enzymes for the pharmaceutical industry. Bacillus megaterium has the advantage of producing extra-cellular PGA. This work compares two neural networks (NNs) architectures for on-line inference of B. megaterium cell mass in an aerated stirred tank bioreactor, during the production of PGA. Nowadays, intelligent computing tools such as artificial NNs and fuzzy logic are commonly applied for state inference and modeling of bioreactors. Combining these two approaches in hybrid, neuro-fuzzy systems, may be advantageous. Our results indicate that a neuro-fuzzy inference system showed a better performance to infer cell concentrations, when compared to multilayer perceptrons networks.

Cephalosporin C production by immobilized Cephalosporium acremonium cells in a repeated batch tower bioreactor.

The industrial production of antibiotics with filamentous fungi is usually carried out in conventional aerated and agitated tank fermentors. Highly viscous non-Newtonian broths are produced and a compromise must be found between convenient shear stress and adequate oxygen transfer. In this work, cephalosporin C production by bioparticles of immobilized cells of Cephalosporium acremonium ATCC 48272 was studied in a repeated batch tower bioreactor as an alternative to the conventional process. Also, gas-liquid oxygen transfer volumetric coefficients, k(L)a, were determined at various air flow-rates and alumina contents in the bioparticle. The bioparticles were composed of calcium alginate (2.0% w/w), alumina ( < 44 micra), cells, and water. A model describing the cell growth, cephalosporin C production, oxygen, glucose, and sucrose consumption was proposed. To describe the radial variation of oxygen concentration within the pellet, the reaction-diffusion model forecasting a dead core bioparticle was adopted. The k(L)a measurements with gel beads prepared with 0.0, 1.0, 1.5, and 2.0% alumina showed that a higher k(L)a value is attained with 1.5 and 2.0%. An expression relating this coefficient to particle density, liquid density, and air velocity was obtained and further utilized in the simulation of the proposed model. Batch, followed by repeated batch experiments, were accomplished by draining the spent medium, washing with saline solution, and pouring fresh medium into the bioreactor. Results showed that glucose is consumed very quickly, within 24 h, followed by sucrose consumption and cephalosporin C production. Higher productivities were attained during the second batch, as cell concentration was already high, resulting in rapid glucose consumption and an early derepression of cephalosporin C synthesizing enzymes. The model incorporated this improvement predicting higher cephalosporin C productivity.