This work deals with some sliding mode (SM) and adaptive sliding-mode control (SMC) strategies for a class of nonlinear biotechnological processes. First, a dynamical SM-based feedback strategy is designed in order to ensure the asymptotic output stabilization of nonlinear bioprocesses. The control law design is done by means of a combination between the exact linearization approach and the SMC. Second, an adaptive SMC strategy is derived for this class of bioprocesses. In order to deal with the parametric uncertainties of the bioprocesses, the adaptive form of the SMC law is designed by means of direct, overparameterized adaptive control techniques available for linearizable systems. The paper also presents the implementation of the proposed control strategies for two typical bioprocesses belonging to the studied nonlinear class. The first prototype process takes place into a Continuous Stirred Tank Bioreactor, and the second is a lipase production process that takes place inside a Fed-Batch Bioreactor.

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We present an investigation consisting of single walled carbon nanotubes (SWCNTs) based cryogenic temperature sensors, capable of measuring temperatures in the range of 2–77 K. Carbon nanotubes (CNTs) due to their extremely small size, superior thermal and electrical properties have suggested that it is possible to create devices that will meet necessary requirements for miniaturization and better performance, by comparison to temperature sensors currently available on the market. Starting from SWCNTs, as starting material, a resistive structure was designed. Employing dropcast method, the carbon nanotubes were deposited over pairs of gold electrodes and in between the structure electrodes from a solution. The procedure was followed by an alignment process between the electrodes using a dielectrophoretic method. Two sensor structures were tested in cryogenic field down to 2 K, and the resistance was measured using a standard four-point method. The measurement results suggest that, at temperatures below 20 K, the temperature coefficient of resistance average for sensor 1 is 1.473%/K and for sensor 2 is 0.365%/K. From the experimental data, it can be concluded that the dependence of electrical resistance versus temperature can be approximated by an exponential equation and, correspondingly, a set of coefficients are calculated. It is further concluded that the proposed approach described here offers several advantages, which can be employed in the fabrication of a microsensors for cryogenic applications.

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This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production, which is carried out in continuous stirred tank bioreactors. Firstly, an indirect adaptive control structure based on the nonlinear process model is derived by combining a linearizing control law with a new parameter estimator. This estimator is used for on-line estimation of the bioprocess unknown kinetics, avoiding the introduction of a state observer. Secondly, a tuning procedure of estimator design parameters is achieved by stability analysis of the control scheme. The effectiveness and performance of estimation and control algorithms are illustrated by numerical simulations applied in the case of a lactic fermentation bioprocess for which kinetic dynamics are strongly nonlinear, time varying, and completely unknown, and not all the state variables are measurable.

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The growth rates of the microorganisms in bioreactors are described by complex kinetic expressions, and the modelling and estimation of such kinetics are essential for the design of control strategies. This study deals with the online estimation of kinetic rates of a baker's yeast process, taking place inside a fed-batch bioreactor. This bioprocess is widely used in bioindustry; its model is highly non-linear and, furthermore, the available online measurements are missing and the reaction kinetics is not perfectly known. The unknown kinetics is estimated by using non-linear observers, based on high-gain approach. Two high-gain observers are designed and implemented: one for the specific growth rates and the other for the reaction rates of the baker's yeast bioprocess. The multiple estimation schemes do not require any model for the kinetic rates. The tuning of the proposed observers is reduced to the calibration of a single parameter. An observer-based estimator is also implemented in order to use it for comparisons. Numerical simulations are included to test the behaviour and the performance of the proposed observers.