摘要
针对甘油间歇发酵过程的参数辨识问题,首先建立了以各代谢物的浓度误差与斜率误差之和为目标函数的参数辨识动态优化模型,然后将其近似转化为静态非线性规划问题,最后应用遗传算法求解上述得到的非线性优化问题,并与已有文献进行了结果比较分析。
A dynamic optimization model for parameter identification of batch fermentation process of glycerol was first established. Its objective function is the sum of the least-square error and slop error of all metabolite concentrations. Then the proposed dynamic optimization model was transformed into a static nonlinear programming problem. Finally,a genetic algorithm was applied to solve the obtained nonlinear optimization problem. A comparation between the attained and exiting results was presented.
出处
《重庆理工大学学报（自然科学）》
CAS
2016年第10期76-80,共5页
Journal of Chongqing University of Technology：Natural Science
基金
国家自然科学基金项目(11101051
11371071)
辽宁省自然科学基金项目(2015020038)
辽宁省高等学校创新团队支持计划项目(LT2014024)
辽宁省大学生创新创业训练计划项目(201510167000012)
关键词
参数辨识
间歇发酵过程
优化模型
遗传算法
parameter identification
batch fermentation process
optimization model
genetic algorithm
