A method for the reconstruction of unknown non-monotonic growth functions in the chemostat

We propose an adaptive control law that allows one to identify unstable steady states of the open-loop system in the single-species chemostat model without the knowledge of the growth function. We then show how one can use this control law to trace out (reconstruct) the whole graph of the growth function. The process of tracing out the graph can be performed either continuously or step-wise. We present and compare both approaches. Even in the case of two species in competition, which is not directly accessible with our approach due to lack of controllability, feedback control improves identifiability of the non-dominant growth rate.Comment: expansion of ideas from proceedings paper (17 pages, 8 figures), proceedings paper is version v

Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters

This paper deals with the tuning of observer-based estimators. Initially, these algorithms were designed for estimating on-line kinetic parameters, like specific growth rates, in bioprocesses, and have proved to be very successful in practical applications. Here a systematic tuning approach that allows a decoupled estimation of each parameter and the assignment of the estimator dynamics independently of the process dynamics is proposed. The presented approach is illustrated on an animal cell culture example in numerical simulation and with real-life data

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

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. Patent ES20010001757, EP20020751179Neeleman Boxtel (2001) Estimation of specific growth rate from cell density measurements. Bioprocess Biosyst Eng 24(3):179–185November E, van Impe J (2002) The tuning of a model-based estimator for the specific growth rate of Candidautilis. Bioprocess Biosyst Eng 25:1–12Park Y, Stein J (1988) Closed-loop, state and input observer for systems with unknown inputs. Int J Control 48(3):1121–1136Perrier M, de Azevedo SF, Ferreira E, Dochain D (2000) Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters. Control Eng Pract 8:377–388Picó J, De Battista H, Garelli F (2009) Smooth sliding-mode observers for specific growth rate and substrate from biomass measurement. J Process Control 19(8):1314–1323. 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. Bioprocess Biosyst Eng 32(3):381–388Venkateswarlu C (2004) Advances in monitoring and state estimation of bioreactors. J Sci Indus Res 63:491–498Zamboni N, Fendt S, Rühl M, Sauer U (2009) 13c-based metabolic flux analysis. Nat Protocols 4:878–892Zorzetto LFM, Wilson JA (1996) Monitoring bioprocesses using hybrid models and an extended kalman filter. Comput Chem Eng 20(Suppl 1):S689–S69

Sensor Selection and Optimization for Health Assessment of Aerospace Systems

Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service. These research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, dependable sensor suite to address system health assessment requirements

A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient

<p>Abstract</p> <p>Background</p> <p>An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties.</p> <p>Results</p> <p>We propose a procedure to estimate the evolution of the metabolic fluxes that is structured as follows: 1) measure the concentrations of extracellular species and biomass, 2) convert this data to measured fluxes and 3) estimate the non-measured fluxes using the Flux Spectrum Approach, a variant of Metabolic Flux Analysis that overcomes the difficulties mentioned above without assuming optimal behaviour. We apply the procedure to a real problem taken from the literature: estimate the metabolic fluxes during a cultivation of CHO cells in batch mode. We show that it provides a reliable and rich estimation of the non-measured fluxes, thanks to considering measurements uncertainty and reversibility constraints. We also demonstrate that this procedure can estimate the non-measured fluxes even when there is a lack of measurable species. In addition, it offers a new method to deal with inconsistency.</p> <p>Conclusion</p> <p>This work introduces a procedure to estimate time-varying metabolic fluxes that copes with the insufficiency of measured species and with its intrinsic uncertainty. The procedure can be used as an off-line analysis of previously collected data, providing an insight into the dynamic behaviour of the organism. It can be also profitable to the on-line monitoring of a running process, mitigating the traditional lack of reliable on-line sensors in industrial environments.</p

Genotoxic effect induced by hydrogen peroxide in human hepatoma cells using comet assay

Background: Hydrogen peroxide is a common reactive oxygen intermediate generated by variousforms of oxidative stress. Aims: The aim of this study was to investigate the DNA damage capacity ofH2O2 in HepG2 cells. Methods: Cells were treated with H2O2 at concentrations of 25 μM or 50 μM for5 min, 30 min, 40 min, 1 h or 24 h in parallel. The extent of DNA damage was assessed by the cometassay. Results: Compared to the control, DNA damage by 25 μM and 50 μM H2O2 increasedsignificantly with increasing incubation time up to 1 h, but it was not increased at 24 h. Conclusions:Our Findings confirm that H2O2 is a typical DNA damage inducing agent and thus is a good modelsystem to study the effects of oxidative stress. DNA damage in HepG2 cells increased significantlywith H2O2 concentration and time of incubation but later decreased likely due to DNA repairmechanisms and antioxidant enzyme