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.     

离散变量结构优化的改进组合型遗传算法

对离散组合型法进行改进,提出了一种新的初始点产生办法,提高了离散组合型法的局部寻优能力.把离散组合型算法作为组合型操作算子融合到遗传算法中,构造一种新的离散变量结构优化算法-组合型遗传算法.运用模拟退火技术构造惩罚函数来改造适值函数,使算法更稳定地收敛于全局可行最优解.与基本遗传算法和组合型算法的计算结果比较证明,改进的组合型遗传算法具有局部搜索快和全局性好的双重特点,是可行且有效的离散变量结构优化设计方法.