微生物发酵过程建模与优化控制

针对微生物发酵参数通常不是最优的，容易造成发酵单位低和产量波动大的问题，采用人工神经网络和遗传算法对微生物发酵过程进行优化控制。建立了维生素B12发酵过程多步预估神经网络模型，对每个发酵阶段的不同的最优操作条件，分别寻优。基于此模型，遗传算法仿真寻优得到了每个发酵阶段的pH值和温度最优轨线。实际应用表明，此轨线应用到生产过程后，使发酵单位得到明显提高。

Modeling and Optimization Control for the Microbial Fermentation Process

To the problem that microbial fermentation parameters are not usually optimal and easily lead to low fermentation unit and high output fluctuation, a method to optimize and control the fermentation process is presented using neural network and genetic algorithm. Neural networks are applied to model the Vitamin B12 fermentation process. The models are able to realize multi-step pre-estimate. The microbial fermentation usually goes step by step. The best operating conditions of each step are different, so the optimization of each step needs to be sought respectively . Based on the models, genetic algorithm gained the optimal trajectories of temperature and pH by simulating. Putting the optimal operating conditions into practice makes the fermentation unit an obvious increase.