Christian G. Feudjio Letchindjio

Bio: Christian G. Feudjio Letchindjio is an academic researcher from University of Mons. The author has contributed to research in topics: Observer (quantum physics) & Filter (signal processing). The author has an hindex of 2, co-authored 10 publications receiving 25 citations.

Micro-algae productivity optimization using extremum-seeking control

Abstract: This study investigates a real-time optimization strategy for micro-algae continuous processes. The objective is to determine the optimal dilution rate maximizing the biomass productivity on the basis of the on-line measurement of biomass concentration and minimum prior process knowledge. Two different micro-algae strains (with different growth rates) are considered in this study: Dunaliela tertiolecta and Isochyris galbana. The extremum seeking control shows good performances in both cases. Practical tuning guidelines can be derived based on the strain growth dynamics.

An experimental application of extremum seeking control to cultures of the microalgae Scenedesmus obliquus in a continuous photobioreactor

Abstract: Summary The cultivation of microalgae in photobioreactors (PBRs) is important in various sectors such as food, bioenergy, pigments, and cosmetics. Productivity optimization can be achieved without ...

Model-Free Extremum Seeking Control of Bioprocesses: A Review with a Worked Example

Abstract: Uncertainty is a common feature of biological systems, and model-free extremum-seeking control has proved a relevant approach to avoid the typical problems related to model-based optimization, e.g., time- and resource-consuming derivation and identification of dynamic models, and lack of robustness of optimal control. In this article, a review of the past and current trends in model-free extremum seeking is proposed with an emphasis on finding optimal operating conditions of bioprocesses. This review is illustrated with a simple simulation case study which allows a comparative evaluation of a few selected methods. Finally, some experimental case studies are discussed. As usual, practice lags behind theory, but recent developments confirm the applicability of the approach at the laboratory scale and are encouraging a transfer to industrial scale.

An Extremum Seeking Strategy Based on Block-Oriented Models: Application to Biomass Productivity Maximization in Microalgae Cultures

Abstract: This paper proposes an adaptive slope seeking strategy targeting any reachable operating point—including extremum—on the input/output map of a general dynamic single input single output (SISO) syst...

Novel Approaches to Online Process Optimization Under Uncertainty: Addressing the limitations of current industrial practice

Abstract: In the face of growing competition, and increased necessity to focus on sustainability and energy efficiency, there is a clear need to optimize the day-to-day operation of many industrial processes. One strategy for online process optimization is to use model-based real-time optimization (RTO). Despite the motivation and the potential, real-time optimization is not as commonly used in practice as one would expect. This thesis takes a detailed look at the different challenges that impede practical implementation of real-time optimization and aims to address some of these challenges. In brief, this thesis presents novel algorithms and methods ranging from simple control structures, to model-free and model-based optimization and more complex scenario-based economic model predictive control. One of the fundamental limiting factors of traditional steady-state RTO is the steady-state wait time. This essentially discards transient measurements, which otherwise contains useful information. In part I of this thesis, we propose different approaches to use transient measurements for steady-state optimization, with the goal of minimizing the steady-state wait time. Moreover, different algorithms to real-time optimization that do not require the need to solve numerical optimization problems are proposed, thus alleviating many of the computational challenges which impede practical application of traditional RTO approaches. First, we propose a “hybrid” approach, where the model adaptation is done with transient measurements and dynamic models, and the optimization is performed using steady-state models. To further simplify the steady-state optimization, we then convert the hybrid RTO approach into a feedback RTO approach. Here, the transient measurements are used to estimate the steady-state gradient, which is controlled to a constant setpoint of zero using feedback controllers. The steadystate gradient is estimated using a novel method based on linearizing the nonlinear dynamic model around the current operating point. To address the cost of developing models, we demonstrate the use of classical controllers where the economic objectives are translated into control objectives. We also provide a systematic approach to switch between different active constraint regions using selectors. For the unconstrained degrees of freedom, we then propose a novel extremum seeking scheme using transient measurements for a class of Hammerstein systems, where the linear dynamics are fixed. We show that the proposed approach converges significantly faster than the classical extremum seeking scheme, and provide robust stability margins. Part I concludes by showing that the different methods work in different time scales, and by hierarchically combining the different approaches, one can handle a wider class of uncertainties.