Distribution-Based Identification of Yield Coefficients in a Baker’s Yeast Bioprocess

A distribution-based identification procedure for estimation of yield coefficients in a baker’s yeast bioprocess is proposed. This procedure transforms a system of differential equations to a system of algebraic equations with respect to unknown parameters. The relation between the state variables is represented by functionals using techniques from distribution theory. A hierarchical structure of identification is used, which allows obtaining a linear algebraic system of equations in the unknown parameters. The coefficients of this algebraic system are functionals depending on the input and state variables evaluated through some test functions from distribution theory. First, only some state equations are evaluated throughout test functions to obtain a set of linear equations in parameters. The results of this first stage of identification are used to express other parameters by linear equations. The process is repeated until all parameters are identified. The performances of the method are analyzed by numerical simulations

Modelling and robust control of a flexible beam Quanser experiment

In this paper, the robust optimal model matching control design method is applied to a flexible beam Quanser experiment. The neglected nonlinearities of the real experiment in the controller design step lead to important differences between the simulation and experimental results, including vibrations of the real beam and/or stationary errors due to insensibility zone. In order to eliminate these tracking errors and reduce the vibrations of the beam we proposed an improved control law by using some blocks with variable structure. Because some of the model’s parameters are imprecisely known, an identification procedure is needed. The paper presents a subspace identification method, which is a relatively new approach used for the state space model identification. Finally, the performance of the proposed controller is illustrated by some experimental results

On bond graph modelling of thermo-chemical processes

The paper presents an approach of the Bond Graph modelling applied to thermo-chemical processes. The proposedwork focused on combustion process kinetics with respect to reactant and reactor input data. The model provides informationon the time variation of the heat of reaction, reaction products concentration, and reactants concentration / accumulation, basedon global mass and energy balance of the process. The basic reaction between solid carbon and oxygen was considered to modelthe combustion solid fuel. The model can be used as base for the development of multi-component combustion reactions with enhancedthermal transfer

Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations