Cimander Christian, Mandenius Carl-Fredrik
Division of Biotechnology, Department of Physics and Measurement Technology, Linköping University, 58183 Linköping, Sweden.
Bioprocess Biosyst Eng. 2004 Dec;26(6):401-11. doi: 10.1007/s00449-003-0327-z. Epub 2003 Sep 5.
A multivariate bioprocess control approach, capable of tracking a pre-set process trajectory correlated to the biomass or product concentration in the bioprocess is described. The trajectory was either a latent variable derived from multivariate statistical process monitoring (MSPC) based on partial least squares (PLS) modeling, or the absolute value of the process variable. In the control algorithm the substrate feed pump rate was calculated from on-line analyzer data. The only parameters needed were the substrate feed concentration and the substrate yield of the growth-limiting substrate. On-line near-infrared spectroscopy data were used to demonstrate the performance of the control algorithm on an Escherichia coli fed-batch cultivation for tryptophan production. The controller showed good ability to track a defined biomass trajectory during varying process dynamics. The robustness of the control was high, despite significant external disturbances on the cultivation and control parameters.
本文描述了一种多变量生物过程控制方法，该方法能够跟踪与生物过程中生物质或产物浓度相关的预设过程轨迹。该轨迹要么是基于偏最小二乘（PLS）建模的多变量统计过程监测（MSPC）得出的潜在变量，要么是过程变量的绝对值。在控制算法中，底物进料泵速率根据在线分析仪数据计算得出。所需的唯一参数是底物进料浓度和生长限制底物的底物产率。在线近红外光谱数据用于证明该控制算法在大肠杆菌分批补料培养生产色氨酸过程中的性能。在不同的过程动态变化中，控制器显示出良好的跟踪定义生物质轨迹的能力。尽管培养和控制参数受到显著的外部干扰，但控制的鲁棒性仍然很高。
