%0 Journal Article
%T Optimization of fermentation medium for Acinetobacter sp. DNS32 by response surface methodology and artificial neural network<br>响应面法和神经网络优化Acinetobacter sp. DNS32发酵基质
%A Wang Yang
%A Wang Zhigang
%A Wang Xi
%A Guo Huosheng
%A Meng Dongfang
%A Zhang Ying
%A <br>王洋
%A 王志刚
%A 王溪
%A 郭火生
%A 孟冬芳
%A 张颖
%J 环境工程学报
%D 2013
%I 
%X The aim of this research was to increase the biomass production of atrazine-degrading Acinetobacter sp. DNS32 by adopting response surface methodology (RSM) and genetic algorithm based on artificial neural network (ANN-GA) to optimize the three important fermentation medium compositions, respectively. According to RSM, these three optimized compositions were composed as follows: corn flour 39.494 g/L, soybean flour 25.638 g/L and K2HPO4 3.265 g/L. The predicted and verifiable values by RSM were 7.079×108CFU/mL and 7.194×108CFU/mL, respectively. The maximum biomass concentration predicted by hybrid ANN-GA was 7.199×108CFU/mL at the optimum level of medium variables as follows: corn flour 39.650 g/L, soybean flour 25.500 g/L and K2HPO4 2.624 g/L, while the experimentally measured value was 7.244×108CFU/mL. Finally, according to the above results, the optimized medium composition was: corn flour 39.650 g/L, soybean flour 25.50 g/L, CaCO3 3.000 g/L, K2HPO4 2.624 g/L, MgSO4·7H2O 0.200 g/L and NaCl 0.200 g/L. After medium optimization, the biomass yeild of atrazine-degrading strain DNS32 increased by 36.6% than that using non-optimized medium. The results showed that RSM and ANN-GA were feasible to optimize the fermentation medium for the production of atrazine-degrading strain DNS32, and ANN-GA had a much better optimizing ability and modeling ability.
%K atrazine-degrading strain DNS32
%K fermentation medium
%K response surface methodology
%K artificial neural network
%K genetic algorithm
%K optimization<br>阿特拉津降解菌DNS32
%K 发酵培养基
%K 响应面法
%K 人工神经网络
%K 遗传算法
%K 优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=3FF3ABA7486768130C3FF830376F43B398E0C97F0FF2DD53&cid=92E6F4267FD4CBCB51B1E49E014D8054&jid=3567BD61129AA59043F5DE01F8815DB5&aid=4D6BB6A422263A452996F48A55EEDA35&yid=FF7AA908D58E97FA&vid=DF92D298D3FF1E6E&iid=0B39A22176CE99FB&sid=4198A31627C9B2A6&eid=FC6FCA5A7559F1FB&journal_id=1673-9108&journal_name=环境工程学报&referenced_num=0&reference_num=21