Xavier Hulhoven

Hybrid full horizon-asymptotic observer for bioprocesses

The full horizon observer is a stochastic nonlinear observer that does not require any parameter tuning and whose optimal feature results directly from the identification cost function of the initial conditions. The efficiency of this observer is, however, strongly dependent on the model quality. On the other hand, the asymptotic observer does not require a kinetic model. However, its convergence is function of the experimental conditions. The aim of this study is to build a hybrid observer which allows to jointly estimate the state and identify on-line the confidence in the kinetic model. Simulations of fed-batch bacterial cultures show very satisfactory results.

Monitoring and control of a bioprocess for malaria vaccine production

Based on genetic manipulations, new strains of S. cerevisae are developed, which can be used for the production of pharmaceuticals. In this study, attention is focused on yeast fed-batch cultures dedicated to the production of a malaria vaccine. The efficient operation of this bioprocess requires on-line monitoring and regulation of the ethanol concentration at a low level (so as to maximize biomass productivity). This paper reports on the development of software sensors for the on-line reconstruction of biomass and ethanol, which are based on simple feedforward neural networks making only use of conventional bioprocess instrumentation (stirrer speed, base addition for pH regulation, etc.). This paper also discusses the design of a robust RST control strategy for regulating the ethanol concentration, which ensures setpoint tracking and asymptotic disturbance rejection. Robustification is achieved through the use of Youla parametrisation and on-line adaptation. This control strategy only requires the a priori knowledge about one yield coefficient and one on-line measurement sensor (i.e. an ethanol probe or the proposed software sensor). The software sensor and controller are tested successfully in real-case experimental runs.

Stochastic full horizon – asymptotic hybrid observer applied to a simulated cell culture

Abstract In an attempt to combine the advantages of an exponential state observer (i.e. fast convergence with an accurate model) and an asymptotic one (i.e. convergence without any knowledge about the kinetic model), a stochastic hybrid observer, which compares the observations made by the two observers has been developed (Hulhoven et al ., 2003). This comparison is made in order to perform a test on the process model quality and to provide a state estimation that evolves, accordingly, between the state estimation provided by the exponential observer and the one from the asymptotic one. In this contribution, the stochastic hybrid observer is established by using a full horizon observer as the exponential observer. The performances of this hybrid observer, are then tested on a simulated fed-batch cell culture.

Experimental study of neural network software sensors in yeast and bacteria fed-batch processes

Nowadays, on-line bioprocess monitoring is still a delicate task due to the lack of on-line measurements of the key components of a culture. In this study the use of artificial neural networks (NNs) as a basis to develop software sensors is investigated. Particularly attention is focused on the use of standard signals, such as those coming from pH or oxygen regulation, to infer information on the evolution of biomass or products of yeast and bacteria fed-batch cultures. The selection of informative signals is achieved through principal component analysis (PCA). Radial basis function (RBF) NNs are then used to estimate the component concentrations of interest. This work is based on extensive experimental studies, considering different cell strains and bioreactor scales. The results of our tests demonstrate the flexibility of NN software sensors in industrial environments.

Stochastic Hybrid Observer for Bioprocess State Estimation

Abstract State observers provide estimates of non-measured variables based on a mathematical model of the process and some available hardware sensor signals. On the one hand, exponential observers, such as the full horizon observer (FHO) or Kalman filters, have an adjustable rate of convergence, but strongly rely on the accuracy of the process model. On the other hand, asymptotic observers use a state transformation in order to avoid using the (usually uncertain) kinetic model, but have a rate of convergence imposed by the process dilution rate. In an attempt to combine the advantages of both techniques, a hybrid observer is developed. The principle of this hybrid observer is to compare the observations made by the two observers in order to perform a test on the process model quality and to provide, accordingly, a state estimation that evolves between the two above-mentioned limit observations (observations from an exponential or the asymptotic observer).

Maximum likelihood adaptive observer for bioprocesses

Abstract A particularity of cell culture processes is the relatively restricted number of valuable and accurate measurements for process control. Software sensors are an interesting solution in response to this problem since it provides non measured state estimation combining the available measurements to a mathematical model. But, due to the complexity of cell culture processes, the mathematical model itself may present some uncertainties particularly in the kinetic description. Such a difficulty has lead to the development of adaptive observers which are designed to jointly estimate state variables and model parameters. However those observers may become particularly difficult to design and to tune as the process complexity increases. In this contribution, an adaptive observer based on the theory of the full horizon and the asymptotic observers is proposed.