Jens E. Haag

Modeling and simulation of a SMB chromatographic process designed for enantioseparation

Abstract This paper focuses on modeling of a simulated moving bed process (SMB) dedicated to the separation of racemic mixtures. In the first approach, a true moving bed model is derived, which assumes an equivalent counter-current movement of the solid phase. The good agreement between the model and the real system is demonstrated with experimental results. Then, a more rigorous approach is developed, which considers the system as an arrangement of static chromatographic columns and takes into account periodic switching. Attention is focused on model formulation and numerical solution techniques in order to develop efficient dynamic simulation programs.

Macroscopic modelling and identification of an anaerobic waste treatment process

Abstract Anaerobic fermentation is an important process used for recycling solid organic waste, which leads to a significant reduction of the waste volume with the production of biogas as a positive side effect. For state observation and control purposes, a mathematical representation of the process is required. However, anaerobic fermentation is far too complex to be described in full metabolic details, due to the variety of responsible microorganisms and the unknown and time-varying waste composition. The level of complexity of the description is limited by the amount and quality of available experimental data, which can be used for model identification. In practice, the derivation of a dynamic process model involves the following steps: (i) the selection of suitable macroscopic reaction schemes and kinetic structures, (ii) the estimation of the unknown model parameters from experimental data by minimizing a maximum-likelihood criterion, (iii) the estimation of the unknown measurement variances, (iv) the estimation of the covariance matrix of the parameter estimates and (v) the validation of the obtained model. In this study, attention is focused on these several steps, and a dynamic model of a complex anaerobic process is inferred from infrequent measurements of global variables. The experimental data are obtained from six experiments carried out in a small-scale continuous bioreactor under different feed and (controlled) acidity conditions.

Experimental Modeling of a Biofilter for Combined Adsorption and Nitrification

Abstract Biofilters consisting of packed bed columns are efficient devices for purification of ground- or wastewater from solid and toxic components. In this work, a biofilter for nitrification is equipped with a porous bed of Manganese dioxide particles, which offers the support for the formation of a biofilm and which, in addition, adsorbs ammonium. Experiments are performed to study the distributed parameter behavior of key state variables, e.g. biomass and nitrogenous components, and a dynamic model described by partial differential equations is derived. Several unknown model parameters are estimated from measurements collected in the course of these experiments. The resulting model is in good agreement with the experimental data.

Continuous-Discrete Observer Design for a CHO-K1 Cell Culture in Suspension

Bioprocess models are often uncertain due to the lack of experimental data for structure selection and parameter estimation. Maximum-likelihood parameter estimation techniques, which take the measurement errors into account, allow confidence intervals in the identified parameters to be evaluated. In turn, this information can be advantageously exploited in the design of (more) robust state estimators (or software sensors) for process monitoring and control. In this paper, the formulation of continuous-discrete Kalman filters and receding horizon observers are extended to include a posteriori knowledge on model parameter uncertainties. These extended observers/filters are then successfully applied to a real-case study, i.e., animal cell cultures in perfusion mode.

Systematic model identification of complex bioprocesses: application to a CHO-K1 cell culture

Abstract Modeling and parameter identification are important prerequisites for state estimation and control of complex biological systems in bioengineering. Due to the great variety of cell lines and the rapidity of developments in genetics, biosystems have to be investigated in a fast and efficient way to identify the major metabolic phenomena. This paper proposes a systematic modeling procedure, which enables the description of the most important experimental settings and biological effects in terms of stoichiometry, kinetics and metabolic regulation. This procedure is successfully applied to the real case of a perfused mammalian cell culture of non-transfected CHO-K1 cells in suspension.

A Fast Computational Procedure for the Predetermination of Parameters in Non-Linear Bioprocess Models

Abstract In this study, a semi-analytical computational procedure, which allows bioprocess model parameters to be quickly evaluated from experimental data, is developed and illustrated with an application example. The approximate model can be used to investigate the qualitative behavior of key components of interest and to check modeling assumptions. The approximate model parameter values can also be used as starting points for more rigorous identification methods.

Modelling and Identification of a Methanisation Process in Waste Treatment

Abstract A simple kinetic model of a methanisation process is presented including hydrolysis, production of volatile fatty acids and methane. Based on a detailed description of this bioprocess, a simplified model is derived accounting for two types of biomass. The unknown model parameters are estimated from the main biochemical variables, e.g. Chemical Oxygen Demand (COD), Total Organic Carbon (TOC) and measurements of mass concentration, recorded at low sampling rates from experiments in continuous mode with various pa, initial and feed conditions.

Modeling and Simulation of a SMB Chromatographic Process Designed for Enantioseparation

Abstract This paper focuses on modeling of a simulated moving bed process dedicated to the separation of racemic mixtures. In a first approach, a true moving bed model is derived, which assumes an equivalent counter-current movement of the solid phase. The good agreement between the model and the real system is demonstrated with experimental results. Then, a more rigorous approach is developed, which considers the system as an arrangement of static chromatographic columns and takes into account periodic switching. Attention is focused on model formulation and numerical solution techniques in order to develop an efficient dynamic simulation program.