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A comparison of robust model predictive control techniques for a continuous bioreactor

机译:连续生物反应器鲁棒模型预测控制技术的比较

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Biotechnology industry is expanded rapidly due to the progress in the understanding of bio-systems and the increased demand for products. Since bioprocess dynamics are almost always affected by physical parameter variations and external disturbances, the need for robust control techniques is of major importance in order to ensure the desired behavior of the process. The overall process equilibrium is guaranteed if all quantities in the bioreactor remain into prescribed ranges. In recent years, closed-loop control methods have been used in order to cope with uncertainty and an important number of constraints imposed by the physical system. For this purpose, predictive control is a quite promising technique. In the present paper three robust model predictive control (RMPC) techniques are used in order to regulate the substrate concentration and the biomass production in a bioreactor. These techniques are applied to a continuous bioreactor in which the pH changes are considered as disturbances while the air pressure is ignored by the process model. For the simulation purposes a linearized model of the system has been used in which the uncertainty is described in the form of a disturbance term. The effectiveness of the methods is illustrated by means of simulation results.
机译:由于对生物系统的了解和对产品需求的增加,生物技术产业迅速发展。由于生物过程动力学几乎总是受到物理参数变化和外部干扰的影响,因此对于确保过程的理想行为,鲁棒控制技术的需求至关重要。如果生物反应器中的所有数量都保持在规定的范围内,则可以确保总体过程平衡。近年来,已经使用闭环控制方法来应对不确定性和物理系统施加的大量约束。为此,预测控制是一种很有前途的技术。在本文中,使用三种鲁棒模型预测控制(RMPC)技术来调节生物反应器中的底物浓度和生物量产生。这些技术应用于连续的生物反应器,在该反应器中,pH值的变化被视为干扰因素,而过程模型忽略了气压。出于仿真目的,已使用系统的线性化模型,其中以干扰项的形式描述了不确定性。仿真结果说明了该方法的有效性。

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