使用200 m L固定床反应器,在专用催化剂作用下,考察了工艺条件对催化裂化(FCC)轻汽油原料催化裂解生产低碳烯烃反应性能的影响。结果表明:在专用催化剂作用下,FCC轻汽油原料经催化裂解可制得低碳烯烃中的乙烯、丙烯和丁烯,并且产物中的m...使用200 m L固定床反应器,在专用催化剂作用下,考察了工艺条件对催化裂化(FCC)轻汽油原料催化裂解生产低碳烯烃反应性能的影响。结果表明:在专用催化剂作用下,FCC轻汽油原料经催化裂解可制得低碳烯烃中的乙烯、丙烯和丁烯,并且产物中的m(丙烯)/m(乙烯)大于1.40,远高于其经蒸汽热裂解增产丙烯工艺下的0.43;反应温度对C_(≥5)液相产物组成中的芳烃收率增幅影响很大;较高的质量空速不利于低碳烯烃的生成;去离子水的存在及增多,不仅促进丙烯的生成,还有利于抑制催化剂结焦生炭。在反应温度为500℃、液时质量空速为1.0 h^(-1)、反应压力为0.13 MPa、注水量[m(去离子水)/m(FCC轻汽油原料)]为40%的催化裂解优化工艺条件下,丙烯、乙烯、丁烯收率分别达4.44%,9.87%,8.27%,m(丙烯)/m(乙烯)达2.22。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
文摘使用200 m L固定床反应器,在专用催化剂作用下,考察了工艺条件对催化裂化(FCC)轻汽油原料催化裂解生产低碳烯烃反应性能的影响。结果表明:在专用催化剂作用下,FCC轻汽油原料经催化裂解可制得低碳烯烃中的乙烯、丙烯和丁烯,并且产物中的m(丙烯)/m(乙烯)大于1.40,远高于其经蒸汽热裂解增产丙烯工艺下的0.43;反应温度对C_(≥5)液相产物组成中的芳烃收率增幅影响很大;较高的质量空速不利于低碳烯烃的生成;去离子水的存在及增多,不仅促进丙烯的生成,还有利于抑制催化剂结焦生炭。在反应温度为500℃、液时质量空速为1.0 h^(-1)、反应压力为0.13 MPa、注水量[m(去离子水)/m(FCC轻汽油原料)]为40%的催化裂解优化工艺条件下,丙烯、乙烯、丁烯收率分别达4.44%,9.87%,8.27%,m(丙烯)/m(乙烯)达2.22。
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.
