Linearizing Control Based on Adaptive Observer for Anaerobic Continuous Sulphate Reducing Bioreactors with Unknown Kinetics

Anaerobic reactors are a typical example of processes that exhibit non-linear behavior and, also time varying parameters; hence their operation is known to be difficult to model and control. In contrast to modeling approaches, in practice linear controllers are widely employed for industrial processes because of their easy implementation and manipulation by plant operators; nevertheless linear approaches are not robust when the operating conditions change suddenly and/or strong disturbances are present. In order to introduce robust controllers to these processes, this paper addresses the tracking problem for the substrate (sulphate) control in a class of continuous bioreactors. An experimentally corroborated bioreactor model serves as benchmark problem for advanced non-linear analysis and control techniques; taking into account system non-linearities, stability and performance objectives over large operating regions. It is considered that, as it is common in practice, the rate of substrate consumption exhibits uncertainty. Results show that the proposed controller exhibits better dynamic performance than a classical Proportional-Integral control tuned using the methodology suggested by Internal Model Control

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Controlling continuous bioreactor via nonlinear feedback: modelling and simulations approach

The aim of this work is to present a class of nonlinear controller with an exponential-type feedback in order to regulate the sulfate mass concentration via the input flow in a continuous stirred tank bioreactor of an anaerobic sulfate-reducing process. The corresponding kinetic terms in the bioreactor’s modeling are modeled by unstructured modeling approach, which was experimentally corroborated. A sketch of proof of the closed-loop stability of the considered system is done under the framework of Lyapunov theory. Numerical experiments are conducted to show the performance of the proposed methodology in comparison with a well-tuned sigmoid controller

Controlling continuous bioreactor via nonlinear feedback: modelling and simulations approach

The aim of this work is to present a class of nonlinear controller with an exponential-type feedback in order to regulate the sulfate mass concentration via the input flow in a continuous stirred tank bioreactor of an anaerobic sulfate-reducing process. The corresponding kinetic terms in the bioreactor’s modeling are modeled by unstructured modeling approach, which was experimentally corroborated. A sketch of proof of the closed-loop stability of the considered system is done under the framework of Lyapunov theory. Numerical experiments are conducted to show the performance of the proposed methodology in comparison with a well-tuned sigmoid controller

Partial Control of a Continuous Bioreactor: Application to an Anaerobic System for Heavy Metal Removal

This work presents a control strategy for a continuous bioreactor for heavy metal removal. For this aim, regulation of the sulfate concentration, which is considered the measured and controlled state variable, allowed diminishing the cadmium concentration in the bioreactor, where the corresponding controller was designed via nonlinear bounded function. Furthermore, a nonlinear controllability analysis was done, which proved the closed-loop instability of the inner or uncontrolled dynamics of the bioreactor. A mathematical model, experimentally corroborated for cadmium removal, was employed as a benchmark for the proposed controller. Numerical experiments clearly illustrated the successful implementation of this methodology; therefore, cadmium removal amounted to more than 99%, when the initial cadmium concentration was up to 170 mg/L in continuous operating mode

Synthesis of CdS Nanocrystals by Employing the By-Products of the Anaerobic Respiratory Process of Desulfovibrio alaskensis 6SR Bacteria

A novel methodology for the direct synthesis of CdS nanoparticles, using a biological agent that avoids the extracellular processing, and the results of the characterization of CdS nanocrystals are presented. The by-products of the anaerobic respiratory process of Desulfovibrio alaskensis 6SR along with aqueous solutions of Cd salts were successfully employed to produce CdS nanocrystals with mixed cubic and hexagonal phases. Nanocrystal size has a narrow size distribution with little dependence on the Cd concentration. Both the presence of the crystallographic cubic phase and the crystalline order decrease as Cd concentration increases. The band gap values obtained from optical transmission measurements are lower than those of the bulk crystal. Raman spectroscopy characterization agrees with electron transmission microscopy images and X-ray diffraction results indicating that the method promotes the formation of high structural quality nanocrystals when low concentrations of the Cd salt are used