The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculati...The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.展开更多
A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and ...A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.展开更多
目的:由于强化流加培养工艺(intensified fed batch of ultra-high seeding density,uHSD-IFB)的接种密度与运行密度较高,传统低密度培养工艺的流加策略往往不能提供充足的营养物质用于该过程的细胞维持与产物表达,最终导致过程产率低...目的:由于强化流加培养工艺(intensified fed batch of ultra-high seeding density,uHSD-IFB)的接种密度与运行密度较高,传统低密度培养工艺的流加策略往往不能提供充足的营养物质用于该过程的细胞维持与产物表达,最终导致过程产率低、经济性下降;通过优化流加培养基以及补料方案,成功建立CHO细胞强化流加培养过程,从而提高目的蛋白产量。方法:以一株表达单克隆抗体的CHO-K1细胞株为研究对象,通过代谢动力学与化学计量学分析,设计出以葡萄糖为控制模型的两阶段动态反馈流加策略,并结合实验设计(design of experiment,DoE)筛选并优化流加培养基中关键微量元素的营养浓度。结果:优化设计后的uHSD-IFB过程有效缓解了uHSD-IFB过程营养物质的耗竭与代谢副产物累积之间的矛盾,实现了超高接种密度培养工艺的细胞生长与产物合成的目的;累积产量相较于优化前提高了95%,日体积产量提高了约97%。结论:该补料策略有助于快速建立高细胞密度、高产物表达的高接种密度强化流加培养过程。展开更多
基金Item Sponsored by National Natural Science Foundation of China(50474016)
文摘The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.
基金Item Sponsored by National Natural Science Foundation of China(50074026)
文摘A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.
文摘目的:由于强化流加培养工艺(intensified fed batch of ultra-high seeding density,uHSD-IFB)的接种密度与运行密度较高,传统低密度培养工艺的流加策略往往不能提供充足的营养物质用于该过程的细胞维持与产物表达,最终导致过程产率低、经济性下降;通过优化流加培养基以及补料方案,成功建立CHO细胞强化流加培养过程,从而提高目的蛋白产量。方法:以一株表达单克隆抗体的CHO-K1细胞株为研究对象,通过代谢动力学与化学计量学分析,设计出以葡萄糖为控制模型的两阶段动态反馈流加策略,并结合实验设计(design of experiment,DoE)筛选并优化流加培养基中关键微量元素的营养浓度。结果:优化设计后的uHSD-IFB过程有效缓解了uHSD-IFB过程营养物质的耗竭与代谢副产物累积之间的矛盾,实现了超高接种密度培养工艺的细胞生长与产物合成的目的;累积产量相较于优化前提高了95%,日体积产量提高了约97%。结论:该补料策略有助于快速建立高细胞密度、高产物表达的高接种密度强化流加培养过程。
