Cesar-Arturo Aceves-Lara

Dynamic model of temperature impact on cell viability and major product formation during fed-batch and continuous ethanolic fermentation in Saccharomyces cerevisiae.

The impact of the temperature on an industrial yeast strain was investigated in very high ethanol performance fermentation fed-batch process within the range of 30-47 °C. As previously observed with a lab strain, decoupling between growth and glycerol formation occurred at temperature of 36 °C and higher. A dynamic model was proposed to describe the impact of the temperature on the total and viable biomass, ethanol and glycerol production. The model validation was implemented with experimental data sets from independent cultures under different temperatures, temperature variation profiles and cultivation modes. The proposed model fitted accurately the dynamic evolutions for products and biomass concentrations over a wide range of temperature profiles. R2 values were above 0.96 for ethanol and glycerol in most experiments. The best results were obtained at 37 °C in fed-batch and chemostat cultures. This dynamic model could be further used for optimizing and monitoring the ethanol fermentation at larger scale.

Online estimation of VFA, alkalinity and bicarbonate concentrations by electrical conductivity measurement during anaerobic fermentation

This paper describes the use of electrical conductivity for measurement of volatile fatty acids (VFA), alkalinity and bicarbonate concentrations, during the anaerobic fermentation process. Two anaerobic continuous processes were studied: the first was a laboratory reactor for hydrogen production from molasses and the second was a pilot process for anaerobic digestion (AD) of vinasses producing methane. In the hydrogen production process, the total VFA concentration, but not bicarbonate concentration, was well estimated from the on-line electrical conductivity measurements with a simple linear regression model. In the methane production process, the bicarbonate concentration and the VFA concentration were well estimated from the simultaneous on-line measurements of pH and electrical conductivity by means of non-linear regression with neural network models. Moreover, the total alkalinity concentration was well estimated from electrical conductivity measurements with a simple linear regression model. This demonstrates the use of electrical conductivity for monitoring the AD processes.

Multi-objective particle swarm optimization (MOPSO) of lipid accumulation in Fed-batch cultures

Dynamic optimization of fermentation processes could demand the use of multiple criteria to attain certain objectives, which in most cases are conflicting to each other. The use of Pareto optimal sets supplies the necessary information to take decisions about the trade-offs between objectives. In this work, a multi-objective optimization algorithm based on particle swarm optimization (MOPSO) is used to optimize lipid contents in fermentations with Yarrowia lipolytica. A reduced model was developed to shorten the computation time of MOPSO. A pattern search algorithm was sequentially coupled to MOPSO to execute a dynamic optimization handling physical constraints. Three cases are analyzed to emphasize the response of our control strategy. Simulation results showed that MOPSO - pattern search algorithm achieved high lipid fraction and productivity.

Reduction of Metabolic Models by Polygons Optimization Method Applied to Bioethanol Production with Co-Substrates

Abstract In literature metabolic stoichiometric matrix reduction is based on convex analysis by choosing the greatest triangle. This paper proposes a new methodology for the reduction of metabolic networks based on the concept of convex hull by optimization methods. Different polygons are tested to conjointly minimize the squared error (convex hull - experimental data) and maximize the convex hull area in order to reduce the set of metabolic reactions involved in the model. The advantage of this method relies on its ability to select different geometries in a simple manner with the knowledge of the elementary modes. A cybernetic model implementing the proposed optimization method is tested with data for bioethanol production by Saccharomyces cerevisiae growing on four substrates. Parameter estimation and model validation allow comparing the performance of the chosen polygons for reduction of metabolic pathways.

Dynamic model for isopropanol production by Cupriavidus necator

Abstract The Hybrid Cybernetic Model (HCM) enables the simulation of metabolic fluxes by using Elementary Modes Analysis and taking into account of selected cellular regulations. These latter are represented by cybernetic control variables. In this study, a simplified metabolic network was established in order to isolate a subset of Elementary Modes, representative of the main phenotypic capabilities of the microorganism. An innovative classification of the modes was introduced in the dynamic model, which permitted the selection of the active modes based on the microbial kinetics. The case study presented here is a genetically modified strain of Cupriavidus necator, engineered to produce isopropanol. Available experimental data were used for identification of parameters in the dynamic model. This model can be used in order to predict the value of maximal and minimal product yields when other substrates will be tested.

Experimental and statistical analysis of nutritional requirements for the growth of the extremophile Deinococcus geothermalis DSM 11300

Few studies concerning the nutritional requirements of Deinococcus geothermalis DSMxa011300 have been conducted to date. Three defined media compositions have been published for the growth of this strain but they were found to be inadequate to achieve growth without limitation. Furthermore, growth curves, biomass concentration and growth rates were generally not available. Analysis in Principal Components was used in this work to compare and consequently to highlight the main compounds which differ between published chemically defined media. When available, biomass concentration, and/or growth rate were superimposed to the PCA analysis. The formulations of the media were collected from existing literature; media compositions designed for the growth of several strains of Deinococcaceae or Micrococcaceae were included. The results showed that a defined medium adapted from Holland et al. (Appl Microbiol Biotechnol 72:1074–1082, 2006) was the best basal medium and was chosen for further studies. A growth rate of 0.03xa0h−1 and a final OD600nm of 0.55 were obtained, but the growth was linear. Then, the effects of several medium components on oxygen uptake and biomass production by Deinococcus geothermalis DSMxa011300 were studied using a respirometry-based method, to search for the nutritional limitation. The results revealed that the whole yeast extract in the medium with glucose is necessary toxa0obtain a non-limiting growth of Deinococcus geothermalis DSM 11300 at a maximum growth rate of 0.64xa0h−1 at 45xa0°C.

Determination of Stoichiometric Matrix for Ethanol Production from Xylose by Reduction of Elementary Modes with Ant Colony Systems

Abstract Ethanol production is still based on an old technology with performance that requires innovative culture strategies to optimize productivity, ethanol concentration and conversion yield. Furthermore ethanol production of second generation requires using lignocellulosic materials (constituted by 35 to 45% of xylose). This paper addresses the problem of a determination of a stoichiometric matrix for ethanol production. A new method was proposed in order to simplify the set of elementary modes, an approach of optimization based on natural systems: Ant Colony Systems. The advantage of the presented method comes from the fact that it does not need to calculate distance between a node and a line and that only the knowledge of the coordinates of the elementary modes is necessary. This method was applied to xylose metabolism and a reducing stoichiometric matrix was obtained.

Dynamical metabolic modelling for lipid accumulation by Yarrowia lipolytica growing on glucose

The oleaginous yeast Yarrowia lipolytica has been extensively studied due to its capacity to accumulate great amounts of lipids triggered by the excess of the carbon source and the limitation of nitrogen. However, under these conditions this yeast can also produce citric acid, which decreases lipid conversion yield. Few dynamical models are only available to describe lipid metabolism based on mass balances and different kinetic configurations. Nevertheless, cybernetic modeling has allowed extending optimization through the inclusion of internal regulation to identify the controlling steps and metabolic fluxes. In the present work a dynamic metabolic model was developed and applied with two variations, and was compared with experimental data carried out in fed-batch cultures of yeast Y. lipolytica on glucose as carbon source. Two independent data sets regarding nitrogen limitation and deficiency were used for parameter estimation. Results show a good fit of parameters on describing the dynamics of lipids and citric acid production.

Single and Multiple Faults in System Actuators and Sensors for Ethanol Production

Abstract This paper focuses on the study of a fault detection and isolation system strategy applied to an ethanol production bioprocess that involves a two-stage bioreactor with a cell recycling loop to reach high biomass concentrations. The various actuators and sensors of this type of bioprocess, impose to develop a fault detection and isolation strategy. In this work, we study the case where multiple faults (with a small delay between them) or simultaneous faults can occur. We assume that only one type of faults can occur at a time and we will focus on the actuators and sensors faults. The sensor fault problem will be reformulated to an actuator fault one by introducing a state variable transformation, so that an augmented system is constructed. Thus we will design a nonlinear model based on an adaptive observer method, for detection, isolation and identification of actuator and sensor single and multiple faults. These approaches use the system model and the outputs of the adaptive observers to generate residuals. Residuals are defined in such way to isolate the faulty instrument after detecting the fault. The validity of the method will be tested in simulation in a nonlinear model of a two-stage ethanol bioprocess with a cell recycling loop.

Design and Optimization of a Two-Stage Bioreactor with Cellular Recycling for Intensive Ethanol Production

Abstract This paper focuses on ethanol production optimization in the steady-state when the bioprocess involves a two-stage bioreactor with a cell recycling loop to reach high biomass concentrations. This bioprocess is described by a nonlinear model; several nonlinear optimizations for different configurations of the bioprocess are made with a Sequential Quadratic Programming (SQP) algorithm. Biomass residence times in the two bioreactors ( T B 1 and T B 2 ) are the main parameters of the optimization. These parameters are connected to the volume ratio between the two bioreactors ( V 1 / V 2 ) and the substrate feed concentration ( S f 1 and S f 2 ). The optimal configuration has been found for a volume ratio in the range 3.5 to 4.5 with the volume of the second bioreactor remaining constant. This configuration allows ethanol production higher than 2 kg/h, with a cellular viability greater than 80 %, a null glucose residual concentration and an ethanol titer of 11.6° GL.