2,639 research outputs found

    Silicon carbide equipments for process intensification of silicon reactions.

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    Bluestar Silicones, one of the worldwide leaders in silicones chemistry, proposes a R&D project, aiming to design new equipment for the transposition of batch to continuous processes. The safety and environmental issues linked to this type of chemicals, and the productivity targets as well require innovative technologies characterized by a fair corrosion resistance and high heat and mass transfer performances. A preliminary prototype of heat exchanger reactor made of silicon carbide plates has been developed by the LGC in collaboration with a SME specialist of SiC, Boostec. It has allowed the pilot feasibility with some reactions of industrial interest for a Bluestar Silicones to be highlighted. Now, it is necessary to pursue this effort and beyond the feasibility step to go on up to the design of an industrial reactor. This project corresponds to a programme of innovative process development in order to design cleaner, safer and less consuming devices

    Predictive functional control for the temperature control of a chemical batch reactor

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    A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system

    Multi-rate data fusion for state and parameter estimation in (Bio-)chemical process engineering

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    For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes

    Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis

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    This paper presents the application of Iterative Nonlinear Model Predictive Control, INMPC, to a semibatch chemical reactor. The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes. The proposed controller combines the good qualities of Model Predictive Control (MPC) with the possibility of learning from past batches, that is the base of Iterative Control. It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law. A stability proof with unitary control horizon is given for nonlinear plants that are affine in control and have linear output map. The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear non-iterative MPC controller. The controller has applications in repetitive disturbance rejection, because they do not modify the model for control purposes. In this application, some experiments with a disturbance in inlet water temperature has been performed, getting good results.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0

    Heat exchanger/reactors (HEX reactors): Concepts, technologies: State-of-the-art

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    Process intensification is a chemical engineering field which has truly emerged in the past few years and is currently rapidly growing. It consists in looking for safer operating conditions, lower waste in terms of costs and energy and higher productivity; and away to reach such objectives is to develop multifunctional devices such as heat exchanger/reactors for instance. This review is focused on the latter and makes a point on heat exchanger/reactors. After a brief presentation of requirements due to transposition from batch to continuous apparatuses, heat exchangers/reactors at industrial or pilot scales and their applications are described

    Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

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    This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Furthermore, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers’ family achieves convergence to timevarying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. In addition, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.This work was supported by the National University of La Plata (Project 2012-2015), the Agency for the Promotion of Science and Technology ANPCyT (PICT2007-00535) and the National Research Council CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain; and by the project FEDER of the European Union.De Battista, H.; Picó Marco, JA.; Garelli, F.; Navarro Herrero, JL. (2012). Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess and Biosystems Engineering. 35(9):1-11. https://doi.org/10.1007/s00449-012-0752-yS111359Aborhey S, Williamson D (1978) State amd parameter estimation of microbial growth process. Automatica 14:493–498Bastin G, Dochain D (1986) On-line estimation of microbial specific growth rates. Automatica 22:705–709Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier, AmsterdamBejarano F, Fridman L (2009) Unbounded unknown inputs estimation based on high-order sliding mode differentiator. In: Proceedings of the 48th IEEE conference on decision and control, pp 8393–8398Corless M, Tu J (1998) State and input estimation for a class of uncertain systems. Automatica 34(6):757–764Dabros M, Schler M, Marison I (2010) Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass. Bioprocess Biosyst Eng 33:1109–1118Davila A, Moreno J, Fridman L (2010) Variable gains super-twisting algorithm: a lyapunov based design. In: American control conference (ACC), 2010, pp 968–973Dávila J, Fridman L, Levant A (2005) Second-order sliding-mode observer for mechanical systems. IEEE Transact Automatic Control 50(11):1785–1789De Battista H, Picó J, Garelli F, Vignoni A (2011) Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. J Process Control 21:1049–1055Dochain D (2001) Bioprocess control. Wiley, HobokenDochain D (2003) State and parameter estimation in chemical and biochemical processes: a tutorial. J Process Control 13(8):801–818Edwards C, Spurgeon S, Patton R (2000) Sliding mode observers for fault detection and isolation. Automatica 36(2):541–553Evangelista C, Puleston P, Valenciaga F, Fridman L (2012) Lyapunov designed super-twisting sliding mode control for wind energy conversion optimization. Indus Electron IEEE Transact. doi: 10.1109/TIE.2012.2188256Farza M, Busawon K, Hammouri H (1998) Simple nonlinear observers for on-line estimation of kinetic rates in bioreactors. Automatica 34(3):301–318Fridman L, Davila J, Levant A (2008) High-order sliding modes observation. In: International workshop on variable structure systems, pp 203–208Fridman L, Levant A (2002) Sliding mode control in engineering, higher-order sliding modes. Marcel Dekker, Inc., New York, pp 53–101Fridman L, Shtessel Y, Edwards C, Yan X (2008) Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems. Int J Robust Nonlinear Control 18(3–4):399–412Gauthier J, Hammouri H, Othman S (1992) A simple observer for nonlinear systems: applications to bioreactors. IEEE Transact Automatic Control 37(6):875–880Gnoth S, Jenzsch M, Simutis R, Lubbert A (2008) Control of cultivation processes for recombinant protein production: a review. Bioprocess Biosyst Eng 31(1):21–39Hitzmann B, Broxtermann O, Cha Y, Sobieh O, Stärk E, Scheper T (2000) The control of glucose concentration during yeast fed-batch cultivation using a fast measurement complemented by an extended kalman filter. Bioprocess Eng 23(4):337–341Kiviharju K, Salonen K, Moilanen U, Eerikainen T (2008) Biomass measurement online: the performance of in situ measurements and software sensors. J Indus Microbiol Biotechnol 35(7):657–665Levant A (1998) Robust exact differentiation via sliding mode technique. Automatica 34(3):379–384Levant A (2003) Higher-order sliding modes, differentiation and output-feedback control. Int J Control 76(9/10):924–941Lubenova V, Rocha I, Ferreira E (2003) Estimation of multiple biomass growth rates and biomass concentration in a class of bioprocesses. Bioprocess Biosyst Eng 25:395–406Moreno J, Alvarez J, Rocha-Cozatl E, Diaz-Salgado J (2010) Super-twisting observer-based output feedback control of a class of continuous exothermic chemical reactors. In: Proceedings of the 9th IFAC international symposium on dynamics and control of process systems, pp 719–724. Leuven, BelgiumMoreno J, Osorio M (2008) A Lyapunov approach to second-order sliding mode controllers and observers. In: Proceedings of the 47th IEEE conference on decision and control. Cancún, México, pp 2856–2861Moreno J, Osorio M (2012) Strict Lyapunov functions for the super-twisting algorithm. IEEE Transact Automatic Control 57:1035–1040Navarro J, Picó J, Bruno J, Picó-Marco E, Vallés S (2001) On-line method and equipment for detecting, determining the evolution and quantifying a microbial biomass and other substances that absorb light along the spectrum during the development of biotechnological processes. 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Special section on hybrid systems: modeling, simulation and optimizationSchenk J, Balaszs K, Jungo C, Urfer J, Wegmann C, Zocchi A, Marison I, von Stockar U (2008) Influence of specific growth rate on specific productivity and glycosylation of a recombinant avidin produced by a Pichia pastoris Mut + strain. Biotecnol Bioeng 99(2):368–377Shtessel Y, Taleb M, Plestan F (2012) A novel adaptive-gain supertwisting sliding mode controller: Methodol Appl Automatica (in press)Soons Z, van Straten G, van der Pol L, van Boxtel A (2008) On line automatic tuning and control for fed-batch cultivation. Bioprocess Biosyst Eng 31(5):453–467Utkin V, Poznyak A, Ordaz P (2011) Adaptive super-twist control with minimal chattering effect. In: Proceedings of 50th IEEE conference on decision and control and European control conference. Orlando, pp 7009–7014Veloso A, Rocha I, Ferreira E (2009) Monitoring of fed-batch E. coli fermentations with software sensors. 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    Methodology of Supervision by Analysis of Thermal Flux for Thermal Conduction of a Batch Chemical Reactor Equipped with a Monofluid Heating/Cooling System

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    We present the thermal behavior of a batch reactor to jacket equipped with a monofluid heating/cooling system. Heating and cooling are provided respectively by an electrical resistance and two plate heat exchangers. The control of the temperature of the reaction is based on the supervision system. This strategy of management of the thermal devices is based on the usage of the thermal flux as manipulated variable. The modulation of the monofluid temperature by acting on the heating power or on the opening degrees of an air-to-open valve that delivers the monofluid to heat exchanger. The study shows that the application of this method for the conduct of the pilot reactor gives good results in simulation and that taking into account the dynamics of the various apparatuses greatly improves ride quality of conduct. In addition thermal control of an exothermic reaction (mononitration) shows that the consideration of heat generated in the model representation improve the results by elimination any overshooting of the set-point temperature

    Neural network applications in polymerization processes

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    Neural networks currently play a major role in the modeling, control and optimization of polymerization processes and in polymer resin development. This paper is a brief tutorial on simple and practical procedures that can help in selecting and training neural networks and addresses complex cases where the application of neural networks has been successful in the field of polymerization.401418Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq
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