Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: Genetic algorithm

Continuous biomanufacturing with microbes - upstream progresses and challenges

Current biomanufacturing facilities are mainly built for batch or fed-batch operations, which are subject to low productivities and do not achieve the great bioconversion potential of the rewired cells generated via modern biotechnology. Continuous biomanufacturing should be the future directions for high-yield and low-cost manufacturing of various fermentation products. This review discusses the major challenges and the strategies for continuous biomanufacturing with microbes, which include minimizing contamination risk, enhancing genetic stability over a long-term continuous operation, achieving high product titer, rate, and yield simultaneously by decoupling cell growth from product formation, and using modeling approach to accelerate research and development of continuous biomanufacturing. New strain designs and process engineering strategies, including integration with artificial intelligence, are also discussed for intelligent and the next generation of continuous biomanufacturing.

Dynamic Analysis and Control for a Bioreactor in Fractional Order

In this paper, a mathematical model was developed to describe the dynamic behavior of a bioreactor in which a fermentation process takes place. The analysis took into account the bioreactor temperature controlled by the refrigerant fluid flow through the reactor jacket. An optimal LQR control acting in the water flow through a jacket was used in order to maintain the reactor temperature during the process. For the control design, a reduced-order model of the system was considered. Given the heat transfer asymmetry observed in reactors, a model considering the fractional order heat exchange between the reactor and the jacket using the Riemann–Liouville differential operators was proposed. The numerical simulation demonstrated that the proposed control was efficient in maintaining the temperature at the desired levels and was robust for disturbances in the inlet temperature reactor. Additionally, the proposed control proved to be easy to apply in real life, bypassing the singularity problem and the difficulty of initial conditions for real applications that can be observed when considering Riemann–Liouville differential operators.

Behaviour of Aspergillus parasiticus in aflatoxin production as influenced by storage parameters using response surface methodology approach

Aspergillus parasiticus is a pre-harvest and postharvest pathogen that is known to produce aflatoxin; however, it is less studied compared to A. flavus. Inappropriate storage conditions are a cause of food spoilage and growth of mycotoxigenic fungi especially in low moisture foods thus constituting hazards to health. Hence, this study investigated the behaviour of A. parasiticus on aflatoxin production in inoculated wheat flour as influenced by storage conditions using the response surface methodology. Twenty experimental runs consisting of independent variables (incubation temperature (A), time (B) and (C) moisture content) and responses (aflatoxin concentrations, i.e., AFB₁, AFB₂, AFG₁, AFG₂ and AFTOT) were developed. A central composite face-centered design was used with lower and upper limits: A (25-35 °C), B (7-15 days) and C (15-25%), while the non-inoculated wheat flour served as the negative control. Aflatoxin production was determined using High Performance Liquid Chromatography (HPLC) according to standard procedures. Numerical and graphical process variables were optimized, adequate models were predicted and optimal point prediction for aflatoxin concentration was determined. AFG₁ concentrations ranged from 1.10 to 360.06 μg/g, AFG₂ (0.91-446.94 μg/g), AFB₂ (7.95-488.77 μg/g), AFB₁ (17.21-20,666.6 μg/g) and AFTOT (15.91-21,851.09 μg/g). Aflatoxin concentration increased with increase in 'B' and 'A' but decreased with prolonged increase in 'B'. AFB₁ concentrations in A. parasiticus inoculated wheat flour increased at prolonged 'B' and 'A' at constant moisture (12.09%). A reduced cubic model was significantly adequate to describe the relationship between process variables and responses (AFG₁ and AFG₂), cubic model (AFB₁ and AFTOT) and a transformed square root cubic model for AFG₂ concentrations (p ≤ 0.05). 'A' influenced AFG₁ production more than 'C' while 'C' and 'A' had no significant effect on AFG₂ production. Process variables 'AB' influenced AFB₂ concentrations more than 'C' while 'A' had a more significant effect on the AFTOT production than 'B' (p ≤ 0.05). The predicted (R²) and adjusted coefficient of regression (adj R²) were in reasonable agreement. After optimal point prediction and validation, minimum aflatoxin concentration ≤ 0 μg/g could be achieved at the predicted conditions (A = 30.42 °C, B = 10.58 days and C = 14.49%) except in AFG₂ (3.33 μg/g).

Modelling of fermentative bioethanol production from indigenous Ulva prolifera biomass by Saccharomyces cerevisiae NFCCI1248 using an integrated ANN-GA approach

Third generation biomass (marine macroalgae) has been projected as a promising alternative energy resource for bioethanol production due to its high carbon and no lignin composition. However, the major challenge in the technologies of production lies in the fermentative bioconversion process. Therefore, in the present study the predictive ability of an integrated artificial neural network with genetic algorithm (ANN-GA) in the modelling of bioethanol production was investigated for an indigenous marine macroalgal biomass (Ulva prolifera) by a novel yeast strain, Saccharomyces cerevisiae NFCCI1248 using six fermentative parameters, viz., substrate concentration, fermentation time, inoculum size, temperature, agitation speed and pH. The experimental model was developed using one-variable-at-a-time (OVAT) method to analyze the effects of the fermentative parameters on bioethanol production and the obtained regression equation was used as a fitness function for the ANN-GA modelling. The ANN-GA model predicted a maximum bioethanol production at 30 g/L substrate, 48 h fermentation time, 10% (v/v) inoculum, 30 °C temperature, 50 rpm agitation speed and pH 6. The maximum experimental bioethanol yield obtained after applying ANN-GA was 0.242 ± 0.002 g/g RS, which was in close proximity with the predicted value (0.239 g/g RS). Hence, the developed ANN-GA model can be applied as an efficient approach for predicting the fermentative bioethanol production from macroalgal biomass.

Continuous energy recovery and nutrients removal from molasses wastewater by synergistic system of dark fermentation and algal culture under various fermentation types

Synergistic system of dark fermentation and algal culture was initially operated at batch mode to investigate the energy production and nutrients removal from molasses wastewater in butyrate-type, ethanol-type and propionate-type fermentations. Butyrate-type fermentation was the most appropriate fermentation type for the synergistic system and exhibited the accumulative hydrogen volume of 658.3 mL L^-1 and hydrogen yield of 131.7 mL g^-1 COD. By-products from dark fermentation (mainly acetate and butyrate) were further used to cultivate oleaginous microalgae. The maximum algal biomass and lipid content reached 1.01 g L^-1 and 38.5%, respectively. In continuous operation, the synergistic system was stable and efficient, and energy production increased from 8.77 kJ L^-1 d^-1 (dark fermentation) to 17.3 kJ L^-1 d^-1 (synergistic system). Total COD, TN and TP removal efficiencies in the synergistic system reached 91.1%, 89.1% and 85.7%, respectively. This study shows the potential of the synergistic system in energy recovery and wastewater treatment.