计算甘油代谢目标函数的一种新算法

通量平衡分析是代谢工程研究中能够定量预测细胞内通量分布情况的有效工具,然而其预测准确性的前提是需要设计合理的代谢目标函数。研究了克雷伯氏杆菌歧化甘油生产1,3-丙二醇的代谢目标函数计算问题。该问题的数学形式是一个双层规划模型,首先应用线性规划的对偶理论将其转化为等价的单层非线性优化问题,然后采用非线性优化算法对其进行求解。与已有的研究结果相比,所提方法获得了克雷伯氏杆菌歧化甘油代谢的全局最优通量分布情况。     

一种工业过程稳态优化控制算法

针对带有输出关联约束的工业过程,提出一种确定其稳态优化控制的算法.首先通过对数变换将原问题转化为一个等价而且可在对数空间求解的优化控制问题;然后为避免事先选择一个合适罚系数的困难,在算法中引入了目标函数的线性化形式.该优化算法不仅能收敛到正确的系统最优解,而且可用现有的二次规划算法计算.应用简单的滤波技术,改善了算法在有量测噪声情况下的性能.仿真结果表明,所提出的优化算法是有效的.     

色氨酸生物合成的稳态优化

本文研究了色氨酸生物合成的稳态优化.针对色氨酸生物合成过程的三维非线性动力系统,建立了使色氨酸消耗和分泌的综合项最大的稳态优化控制模型.根据IOM方法,将非线性优化问题近似转化为线性优化问题来求解,最后得到了一个稳定而且鲁棒的稳态,在该稳态下,色氨酸产率提高为基本稳态时的9倍,这一数值结果表明了优化算法的实用性和有效性.     

一类生物系统的参数辨识方法及其应用

针对S-型生物系统的参数辨识问题,给出了以极小化误差函数为优化目标的参数辨识模型。基于修正配置和B样条插值,提出了一种有效的参数辨识模型求解方法。计算研究表明,该方法可以获得更为精确的参数辨识结果。将该方法应用到甘油生物发酵系统中,进一步验证了该方法的有效性。     

甘油生物歧化为1,3-丙二醇过程的优化控制

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input con-straints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation (AI-SOPE) technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter.When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential of the approach presented for the optimizing control of the bio-dissimilation process of glyc-erol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.     

甘油间歇生物歧化过程的动态优化

研究了甘油间歇生物歧化过程的动态优化。针对甘油间歇生物歧化过程的非线性过量动力学系统,首先在终端时刻是固定和可变两种情况下,分别以终端时刻产物1,3-丙二醇的浓度、终端时刻产物1,3-丙二醇的产率为目标函数,提出了三个动态优化模型;然后基于有限元配置法将甘油间歇生物歧化过程的状态方程转化为代数方程,从而得到动态优化模型的非线性规划形式。最后对提出的三个动态优化模型进行求解,并对所得优化结果进行了分析与讨论。与已有结果相比,获得了更高的1,3-丙二醇产率。     

甘油生物歧化为1,3-丙二醇过程的H∞控制

A robust controller is designed by using the bilinear transformation and H∞ mixed sensitivity method for bio-dissimilation process of glycerol to 1,3-propanediol. Under the controller the system works near an optimal steady-state for the volumetric productivity of 1,3-propanediol attaining its maximization. The design procedure is carried out by tuning the transformation parameter and DC gain of the performance weighted function, which is an iterative and optimal search process. Simulation results are presented which show that the designed robust controller not only ensures the robust stability of the system in face of the parametric variations in the model, but also makes the system have a favourable robust tracking performance. The validity of the proposed H∞ controller has been tested.     

甘油生物歧化为1,3-丙二醇过程的优化控制

An iterative optimization strategy is proposed and applied to the steady state optimizing control of the bio-dissimilation process of glycerol to 1,3-propanediol in the presence of model-plant mismatch and input constraints. The scheme is based on the Augmented Integrated System Optimization and Parameter Estimation （AI- SOPE） technique, but a linearization of some performance function in the modified model-based optimization problem of AISOPE is introduced to overcome the difficulty of determining an appropriate penalty parameter. When carrying out the iterative optimization, the penalty coefficient is set to a larger value at the current iteration than at the previous iteration, which can promote the evolution rate of the iterative optimization. Simulation studies illustrate the potential ofthe approach presented for the optimizing control of the bioTdissimilation process of glycerol to 1,3-propanediol. The effects of measurement noise, measured and unmeasured disturbances on the proposed algorithm are also investigated.     

生化系统稳态优化的一种新算法

针对一类生化系统的稳态优化问题, 在已有间接优化方法(IOM)的线性优化问题中引入一个反映S–系统解和原模型解一致性的等式约束, 应用Lagrangian乘子法将修正后的非线性优化问题转化为一个等价的线性优化问题, 提出了一种改进的稳态优化新算法. 该优化算法不仅可以收敛到正确的系统最优解, 而且可用现有的线性规划算法去计算. 最后将算法应用于几个生化系统的稳态优化中, 结果表明, 本文提出的优化算法是有效的.