生物制品学是以现代生物技术为核心的一门新的独立学科,其研究成果在全球重大、流行性疾病的预防、治疗、诊断中发挥了不可忽视的作用。本研究以培养创新型生物技术人才为目标,在生物制品学课程中开展了以互联网+学习平台为基础的“自...生物制品学是以现代生物技术为核心的一门新的独立学科,其研究成果在全球重大、流行性疾病的预防、治疗、诊断中发挥了不可忽视的作用。本研究以培养创新型生物技术人才为目标,在生物制品学课程中开展了以互联网+学习平台为基础的“自主初探+团队协作+师生探讨”相结合的以团队为基础的翻转课堂(flipped classroom and team-based learning,FC-TBL)教学模式,并从教学设计、教学资源平台建设、教学评价体系建立3个方面总结了该教学模式的实践经验,以期为相关教学研究提供参考。展开更多
Samples(25500)were collected from a selective catalytic reduction(SCR)denitrification system in a fluid catalytic cracking unit and preprocessed using the quartile method and the K-nearest neighbors interpolation meth...Samples(25500)were collected from a selective catalytic reduction(SCR)denitrification system in a fluid catalytic cracking unit and preprocessed using the quartile method and the K-nearest neighbors interpolation method to remove outliers.Using the Pearson correlation coefficient and LightGBM feature score method,13 key operational variables were identified and used to establish a model to predict outlet nitrogen oxide(NO_(x))concentration in an SCR system with backpropagation neural network,long short-term memory(LSTM)and LSTM-attention fully connected(FC)model,respectively.The LSTM-attention FC model showed better accuracy and generalization capability compared with other models.Its mean square error,mean absolute error,and coefficient of determination on the training and test datasets were 11.32 and 12.51,3.65%and 3.97%,and 0.96 and 0.94,respectively.Furthermore,a combination of the LSTM-attention FC model with a genetic algorithm used to optimize four feature variables including ammonia pressure compensation,inlet pressure,gas inlet upper temperature,and outlet ammonia concentration.The outlet NO_(x)concentration could be controlled below 80±3 mg/m^(3),and the ammonia slip concentration could be controlled below 0.1 mg/m^(3),demonstrating that the optimization model can provide effective guidance for reducing NO_(x)emissions and ammonia slip of SCR systems.展开更多
文摘生物制品学是以现代生物技术为核心的一门新的独立学科,其研究成果在全球重大、流行性疾病的预防、治疗、诊断中发挥了不可忽视的作用。本研究以培养创新型生物技术人才为目标,在生物制品学课程中开展了以互联网+学习平台为基础的“自主初探+团队协作+师生探讨”相结合的以团队为基础的翻转课堂(flipped classroom and team-based learning,FC-TBL)教学模式,并从教学设计、教学资源平台建设、教学评价体系建立3个方面总结了该教学模式的实践经验,以期为相关教学研究提供参考。
基金This work was supported by the SINOPEC:Development of Remote Diagnosis Technology for FCC Flue Gas Desulfurization and Denitrification(320076).
文摘Samples(25500)were collected from a selective catalytic reduction(SCR)denitrification system in a fluid catalytic cracking unit and preprocessed using the quartile method and the K-nearest neighbors interpolation method to remove outliers.Using the Pearson correlation coefficient and LightGBM feature score method,13 key operational variables were identified and used to establish a model to predict outlet nitrogen oxide(NO_(x))concentration in an SCR system with backpropagation neural network,long short-term memory(LSTM)and LSTM-attention fully connected(FC)model,respectively.The LSTM-attention FC model showed better accuracy and generalization capability compared with other models.Its mean square error,mean absolute error,and coefficient of determination on the training and test datasets were 11.32 and 12.51,3.65%and 3.97%,and 0.96 and 0.94,respectively.Furthermore,a combination of the LSTM-attention FC model with a genetic algorithm used to optimize four feature variables including ammonia pressure compensation,inlet pressure,gas inlet upper temperature,and outlet ammonia concentration.The outlet NO_(x)concentration could be controlled below 80±3 mg/m^(3),and the ammonia slip concentration could be controlled below 0.1 mg/m^(3),demonstrating that the optimization model can provide effective guidance for reducing NO_(x)emissions and ammonia slip of SCR systems.
