A novel nonlinear sliding mode observer to estimate biomass for lactic acid production

This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus. A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.

Robust model-based control of a packed absorption column for the natural gas sweetening process

The sweetening units are the most important in natural gas processing. Packed bed absorption columns are widely used in the sweetening process; however, their operation and control are not simple due to their highly non-linear behavior derived from their distributed nature and interaction between multiple physical phenomena. In this work, two robust model-based control schemes are implemented to regulate the CO2 concentration at the outlet of a packed bed absorption column in the gas sweetening process. The model of an industrial-scale absorption column and the structure of the controllers, i) control based on modeling error compensation (MEC) ideas, and ii) nonlinear model predictive control (NMPC) are described. Numerical results show that the proposed robust model-based controllers can regulate the controlled variable to the desired reference despite external disturbances, set-point changes, and uncertainties in the absorption column model.

Design of a Robust sliding mode controller for bioreactor cultures in overflow metabolism via an interdisciplinary approach

Microorganism culture is highly complex due to the different metabolic pathways, which are very complex. A metabolic phenomenon called overflow is a challenge to overcome in automatic control tasks of microorganism cultures. In this study, a nonlinear algorithm by sliding modes (sliding mode nonlinear control, SMNC) is proposed for the robust regulation of a fed-batch bioreactor in the presence of parametric and system perturbations. A control design is obtained using Lyapunov functions by techniques to propose a control law such that it is robust, only the output signals (biomass and volume) are used, and the reaction rates do not have to be wholly known. Therefore, a novel and simple controller capable of solving the above problems is obtained.