Levente Bodizs

Preferential Estimation via Tuning of the Kalman Filter

Abstract Estimation problems have been traditionally formulated so as to minimize the estimation error of the full state vector. However, in applications that involve the tracking of only a few unmeasured variables, it is sufficient to limit the attention along certain directions in state space. This way, it is hoped that better accuracy can be obtained along the desired directions, possibly at the cost of poorer estimates along the other directions. This problem, termed preferential estimation, is formally formulated in this paper as a least-squares minimization problem. Using calibration measurements of the preferred variables, the above mentioned problem is solved numerically via tuning of the Kalman filter. The approach is illustrated in simulation on the optimization of a penicillin fermentation process, where preferential estimation is used successfully to reduce the error in tracking a single unmeasured variable, the substrate concentration.

Optimal Feeding Strategy for Bioreactors with Biomass Death

Abstract Optimization of bioreactors, and especially the maximization of product yield, has been studied extensively in the literature. It has been shown that, in many cases, the optimal solution corresponds to keeping the substrate concentration constant at a value that maximizes the instantaneous yield. However, in the presence of biomass death, keeping the substrate concentration constant at this value may lead to biomass extinction, i.e. no active biomass left in the reactor. In such a case, the optimal solution arises from a compromise between avoiding extinction and increasing the instantaneous yield. In addition, if the maintenance term depends on the amount of inactive biomass, the optimal solution requires a time-varying substrate concentration. These issues are illustrated via the optimization of the batch filamentous fungi fermentation

Parameter Identification to Enforce Practical Observability of Nonlinear Systems

The sensitivity of measurements to unmeasured state variables strongly affects the rate of convergence of a state estimator. To overcome potential observability problems, the approach has been to identify the model parameters so as to reach a compromise between model accuracy and system observability. An objective function that weighs the relative importance of these two objectives has been proposed in the literature. However, this scheme relies on an extensive heuristic search to select the weighting coefficients. This paper proposes an objective function that is the product of measures of these two objectives, thus alleviating the need for the trial-and-error selection of the weighting coefficient. The proposed identification procedure is evaluated using both simulated and experimental data, and with different observer structures.

Preferential Estimation for Uncertain Linear Systems at Steady State: Application to Filamentous Fungal Fermentation

State estimation is a widely used concept in the control community, and the literature mostly concentrates on the estimation of all states. However, in soft sensor problems, the emphasis is on estimating a few soft outputs as accurately as possible. The concept of preferential estimation consists of estimating these soft outputs more accurately than the other states. The main question is whether or not the accuracy along the soft outputs can be improved, possibly at the detriment of other states. This papers shows that, though preferential estimation is not possible for linear systems with perfect model information and gaussian process and measurement noises, it is indeed possible for linear systems with model uncertainty. The theoretical concepts are illustrated on a filamentous fungal fermentation.

Industrial experience with oxygen control of a fed-batch filamentous fungal fermentation

Abstract Fed-batch filamentous fungal fermentations at the industrial level are operated today either in an open-loop manner or through simple PI control, both with limited performance. The main challenge presented by such an operation is that, due to the presence of uncertainties of the inoculum, the process can go into oxygen limitation that severely affects production. In this paper, a cascade control strategy for regulation of the dissolved oxygen is presented, which incorporates available auxiliary measurements to improve performance. This strategy is formulated based on the investigation of the structural elements of a simplified process model developed from experimental data. Experimental results confirm the efficiency of the proposed control strategy.