To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria ...To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant （K） of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number （N0） and initial mean activity of population （A0）. When No is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds No, though both parameters could shorten the lag phase by increasing their values.展开更多
Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new ...Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new century. In order to get a deep insight into this field, some notes on the direct searches for non-smooth optimization problems are made. The global convergence vs. local convergence and their influences on expected solutions for simulation-based stochastic optimization are pointed out. The sufficient and simple decrease criteria for step acceptance are analyzed, and why simple decrease is enough for globalization in direct searches is identified. The reason to introduce the positive spanning set and its usage in direct searches is explained. Other topics such as the generalization of direct searches to bound, linear and non-linear constraints are also briefly discussed.展开更多
基金Project(50321402) supported by the Science Fund for Creative Research Groups of China project(2004CB619204) sup-ported by the National Key Fundamental Research Development Programof China
文摘To reveal the low growth rate of Acidithiobacillus ferrooxidans, a stochastic growth model was proposed to analyze growth curves of these bacteria in a batch culture. An algorithm was applied to simulate the bacteria population during lag and exponential phase. The results show that the model moderately fits the experimental data. Further, the mean growth constant （K） of growth curves is obtained by fitting the logarithm of the simulating population data versus the generation numbers with the different initial population number （N0） and initial mean activity of population （A0）. When No is 300 and 700 respectively, the discrepancy of K value is only 0.91%, however, A0 is 0.34 and 0.38 respectively, the discrepancy of K value is 19.53%. It suggests that the effect of A0 on the lag phase exceeds No, though both parameters could shorten the lag phase by increasing their values.
基金supported by the Key Foundation of Southwest University for Nationalities(09NZD001).
文摘Many difficult engineering problems cannot be solved by the conventional optimization techniques in practice. Direct searches that need no recourse to explicit derivatives are revived and become popular since the new century. In order to get a deep insight into this field, some notes on the direct searches for non-smooth optimization problems are made. The global convergence vs. local convergence and their influences on expected solutions for simulation-based stochastic optimization are pointed out. The sufficient and simple decrease criteria for step acceptance are analyzed, and why simple decrease is enough for globalization in direct searches is identified. The reason to introduce the positive spanning set and its usage in direct searches is explained. Other topics such as the generalization of direct searches to bound, linear and non-linear constraints are also briefly discussed.
