Search

International audience; Considering the increasing impact of the environmental concerns in the current worldwide policy, the application of biological processes, namely the bio-fixation of CO2 by microalgae, represents a promising solution and an increasingly attractive strategy. Indeed, these photosynthetic microorganisms have the great capacity to fix and tolerate high CO2 concentrations converting it to biomass and highly valuable molecules. Thus, modeling of microalgae growth represents an essential tool for the optimization of the carbon dioxide consumption in engineered systems such as photobioreactors. In this context, the main goal of this work is the identification of the growth model parameters of Chlorella vulgaris, the model organism used in this study. The growth model developed in this study takes into account the combined influence of light intensity and the total inorganic carbon available per cell. First, an experimental campaign of batch culture was carried out in a well-stirred lab-scale photobioreactor under optimal conditions. Finally, model results of biomass dynamics and total inorganic carbon evolution over time are compared with data of batch and continuous cultures, confirming the accuracy of the identified model parameters.

International audience; Microalgae consume carbon dioxide for their growth and to produce secondary metabolites. One of the control system challenges is to maximize this carbon dioxide consumption by controlling microalgae growth, and by maintaining its growth rate at an optimal value. However, there is no such thing as a microalgae growth rate sensor. This paper proposes a software sensor for the specific growth rate and biomass concentration of a microalgae culture in a continuous photobioreactor using the measurements of Total Inorganic Carbon in the medium. Numerical simulations are included to illustrate the performance of the proposed estimation strategy.

Microalgae biotechnology has been focusing on the use of algae in the production of high value compounds. During the last few decades, the intense research effort has been aiming at improving new controls and supervising tools as well as on a good process understanding. This requirement involves a large diversity and a better accessibility to process measurements. Probes or sensors are required to control the process. They are however relatively limited. Classical photobioreactors are usually equipped with temperature, dissolved oxygen and pH probes. These sensors actually provide very little online information on cell growth, viability, metabolic state and production. For the time being, sensors reliability cannot meet industrial bioprocessing requirements. In this context software sensors show numerous potentialities. The central axis of this work is the development of an extended Kalman filter (EKF) for the estimation of biomass concentration based on a dynamic process model in combination with total inorganic carbon measurement. A microalga Porphyridium purpureum was used as a model organism in this study. Numerical simulations and real-life experiments (batch and continuous mode) have been carried out and corresponding results are given in order to highlight the performance of the proposed estimator.

In this study, a model of a continuous pilot photobioreactor for microalgae production is proposed. Three aspects have been studied: the modelling of kinetic growth, the gas-liquid transfer and the hydrodynamics in the photobioreactor. The modelling of each aspect has been developed with the dynamic simulation software SpeedUp, after experimental studies, then validated step-by-step. The connection of these three aspects aims to predict and optimise biomass production of the pilot plant.

In this work, simple models for batch reactor runaway with one-phase and homogeneous pressure relief are examined. Five flow models are detailed for the simulation of one and two-phase flows. Results from the different flow models are then compared. The effect of phase composition on the predictions of a hybrid two-phase flow model are illustrated. Finally, the use of dynamic simulation for the design of pressure relief equipment is described.

A neural network has been used to study steel transformation curves. Some problems occurred like non-linearity of the phenomena, measurements uncertainties, lack of examples in the database or variables interdependencies. Solutions for these problems are successively presented, leading to a fractal neural network able to learn differential equations, qualitiative information and to treat noisy experimental measuremnts. Some validation results are presented.

Physical analysis gives a powerful help in conditioning chemical engineering problems. The present paper demonstrates with transient problems that taking into account physical considerations in formulating them improves their solving, either analytically or numerically. It concludes that using extensive differential variables is more effective than intensive ones.

To assist the design engineer to more flexible and controllable, near optimum cost, heat exchanger networks, a computer program, called CAD-HEN (Computer Aided Design of Heat Exchanger Networks), based on thermodynamic principles, was developed. Dynamic simulation and response analysis of the heat exchanger network can be done by the SpeedUp dynamic process simulator by using a specific model. CAD-HEN handles supertargeting, synthesis, and optimization of a heat exchanger network. The targeting and optimization procedures are graphical and interactive. The user views various plots, such as Total Cost Target profile and Grand Composite Curves. An automatic synthesis algorithm, based on the vertical heat transfer model is used to generate in the stream grid environment a near minimum surface network which is optimized with a simplified loop breaking algorithm. A specific model for SpeedUp was developed in order to proceed at dynamic simulation and detailed control study of an existing heat exchanger network.

We have carried out simulations of steel transformation curves with Neural Networks. For this purpose, it was necessary to use special learning techniques because of the non-ideal nature of the industrial experimental results (insufficient in precision and quantity) that we used. The consideration of examples not used in the learning process led us to introduce knowledge of various types and sources in the Neural Network. It was noted that knowledge that an “expert” might use was particularly helpful for a Neural Network.

A Coupled-System approach (SyDCoD) for student training tool to fault diagnosis in a distillation column has been developed. The basic methodology based on the coupling between symbolic-numeric computing integration in which the symbolical computing is represented by a knowledge base of diagnosis and the numerical computing is represented by a simulation. The Knowledge Base approach involves a structure hierarchical diagnosis and structure pattern-matching by categorisation of component malfunction. The hierarchy is a collection of small individual components coordinated to achieve at an overall diagnosis. This fault hierarchy is particularly effective in handling multiple symptoms and multiple fault situations. This methodology give to the students or the student operators better initiation and better understanding on the failure diagnosis in the distillation column plant. The coupled system problem-solver strategy gives more posibilities to students how to choose the strategy to solve their problem.

International audience; In reactive extrusion, the extruder is used as a solvent-free continuous chemical reactor able to process highly viscous materials. The chemical transformation of biopolymers by reactive extrusion appears as a very promising technology. Although punctual applications in this field have already been achieved on a laboratory or pilot scale, the amount of work to carry out is still considerable. A wide range of reactions and raw materials may be explored, and the reactions achieved on a laboratory scale have to be optimized and transposed to an industrial scale. Process modelling and simulation constitute useful tools for process understanding, development, optimization and scale-up. Although reactive extrusion modelling has interested many authors, it still remains a challenge because of the complex geometry and the strong coupling between operating parameters, flow conditions, material rheological behavior and reaction kinetics. A steady-state mathematical model for a biopolymer oxidation process by reactive extrusion is here proposed. The model is based on a hybrid approach combining chemical engineering methods and simplified continuum mechanics laws. The combination of these two approaches enables to simplify the calculations related to chemical reactions while ensuring a predictive character. The flexible structure of the model enabled its implementation within a global process simulator. A method to minimize the amount of experimental data required for model parameter adjustment is also presented. The model was validated by experiments conducted on a semi-pilot corotating twin-screw extruder. Even if it may be refined, the model proposed already constitutes a useful tool for later research work dealing with the development, modelling and simulation of chemical reactions in corotating twin-screw extruders.

This paper presents a nonlinear predictive control (NMPC) strategy applied to the continuous microalgae cultivation process in a closed photobioreactor. The photo-bioreactor system is programmed to operate in a constant biomass density mode, in order to maintain the culture at the optimal population density and sustain high biomass production levels. This objective is achieved by regulating the biomass density while tracking a reference culture medium feeding profile determined off-line. The proposed NMPC approach is validated in simulation, and performances and benefits for the cell cultivations are compared to those obtained when applying a generic model control (GMC) law.

International audience; This paper addresses a study of the regulation of the biomass density in a closed microalgal photobioreactor by using a linearizing control approach. The photobioreactor system was programmed to operate in a constant biomass density mode, in order to maintain the culture at the optimal population density and sustaining high biomass production levels. The designed control law uses an input-output linearizing control in an inner loop, a proportional integral Derivative (P.I.D.) regulator has been added to cancel steady errors. In addition, this PID-regulator is supplied with an anti-windup compensator. Thus, the proposed strategy takes the internal dynamics into account and stabilizes them simultaneously with the control of the input-output behaviour of the system. Commonly, the output of the system is the biomass concentration. However, in this study, we propose to consider a new output: the ratio between two state variables, which is equivalent to the total inorganic carbon available quantity per cell. This new quantity is shown to lead to best regulation performance. We illustrate our approach with numerical simulations and show its benefits for the cell cultivations, mainly for ensuring achievement of the culture with regards to classical experiments problems.

International audience; BACKROUND: Spirulina platensis (Toliara, Madagascar) provides a food supplement which can fight against malnutrition and food insufficiency in Madagascar. In this country, the current production from three open basins is not sufficient to cover needs, and presents drawbacks such as low productivity and possible contamination. Thus cultivation of S. platensis in a closed photobioreactor opens the possibility of extending this microalga production. In this study, the influence of colour and intensity of the light on S. platensis growth and protein content was investigated in a bubble column.RESULTS: Growth kinetics were obtained for four colours (green, white, red and blue) and four intensities (400, 800, 1000 and 1200 lux) of light. The influence of light colour on Spirulina growth was discussed. The highest productivity (183.6mg L(-1) d(-1)) and concentration (2643mg L(-1)) were obtained for green light at 1200 lux. The protein content was 58%.CONCLUSION: Using green light allows improvement of S. platensis growth. Performance obtained with the closed system was higher than that reported in the literature.

International audience; The first use of microalgae by humans dates back 2000 years to the Chinese, who used Nostoe to survive during famine. However, microalgal biotechnology only really began to develop in the middle of the last century. Nowadays, there are numerous commercial applications of microalgae. For example, (i) microalgae can be used to enhance the nutritional value of food and animal feed owing to their chemical composition, (ii) they play a crucial role in aquaculture and (iii) they can be incorporated into cosmetics. Moreover, they are cultivated as a source of highly valuable molecules. For example, polyunsaturated fatty acid oils are added to infant formulas and nutritional supplements and pigments are important as natural dyes. Stable isotope biochemicals help in structural determination and metabolic studies. Future research should focus on the improvement of production systems and the genetic modification of strains. Microalgal products would in that way become even more diversified and economically competitive.

Simulation and optimization of multi-stage adiabtic catalytic reactors is now well developed in chemical industries, design and companies and universities. In a first part we present briefly a program package developed in our laboratory, first for chemical industry, and secondly for chemical engineering education. In this presentation we show how it is possible to take into account new possibilities in direct optimization procedures and new terms in objective functions. In a second part we examine design problems of optimization, including many sub-problems in the definition of objective functions. Then we try to show how it is possible to sensibilize our students on the possibilities of choice for decision variables by using this package.