考察了α-Ga_(2)O_(3)、β-Ga_(2)O_(3)和γ-Ga_(2)O_(3)对CO_(2)氧化乙苯脱氢制苯乙烯反应的催化性能,并通过N_(2)吸附、XRD、NH_(3)-TPD、CO_(2)-TPD、^(71)Ga MAS NMR和TGA等多种方法对不同晶相Ga_(2)O_(3)催化剂进行表征,探究了催...考察了α-Ga_(2)O_(3)、β-Ga_(2)O_(3)和γ-Ga_(2)O_(3)对CO_(2)氧化乙苯脱氢制苯乙烯反应的催化性能,并通过N_(2)吸附、XRD、NH_(3)-TPD、CO_(2)-TPD、^(71)Ga MAS NMR和TGA等多种方法对不同晶相Ga_(2)O_(3)催化剂进行表征,探究了催化剂晶相结构与催化性能的关联。结果表明,Ga_(2)O_(3)的晶相结构与CO_(2)氧化乙苯脱氢性能密切相关,γ-Ga_(2)O_(3)表现出最佳的催化性能,在550℃乙苯转化率达54.7%,苯乙烯选择性为98.6%,循环使用6次后,催化活性仍无明显降低。γ-Ga_(2)O_(3)具有最多的中强酸位、最高的四配位Ga(Ⅳ)百分比和最大的比表面积与孔体积,有利于乙苯的吸附活化、C—H键的解离和催化剂容碳能力的提升,从而使其表现出最佳的催化性能。展开更多
In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the ...In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.展开更多
文摘考察了α-Ga_(2)O_(3)、β-Ga_(2)O_(3)和γ-Ga_(2)O_(3)对CO_(2)氧化乙苯脱氢制苯乙烯反应的催化性能,并通过N_(2)吸附、XRD、NH_(3)-TPD、CO_(2)-TPD、^(71)Ga MAS NMR和TGA等多种方法对不同晶相Ga_(2)O_(3)催化剂进行表征,探究了催化剂晶相结构与催化性能的关联。结果表明,Ga_(2)O_(3)的晶相结构与CO_(2)氧化乙苯脱氢性能密切相关,γ-Ga_(2)O_(3)表现出最佳的催化性能,在550℃乙苯转化率达54.7%,苯乙烯选择性为98.6%,循环使用6次后,催化活性仍无明显降低。γ-Ga_(2)O_(3)具有最多的中强酸位、最高的四配位Ga(Ⅳ)百分比和最大的比表面积与孔体积,有利于乙苯的吸附活化、C—H键的解离和催化剂容碳能力的提升,从而使其表现出最佳的催化性能。
基金supported by the National Science Foundation of China(62263020)the Key Project of Natural Science Foundation of Gansu Province(25JRRA061)+1 种基金the Key R&D Program of Gansu Province(23YFGA0061)the Scientific Research Initiation Fund of Lanzhou University of Technology(061602).
文摘In the production processes of modern industry,accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring“safe,stable,long-term,full load and optimal”operation of the production process.The benzene-to-ethylene ratio control system is a complex system based on anMPC-PID doublelayer architecture.Taking into consideration the interaction between levels,coupling between loops and conditions of incomplete operation data,this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology.Firstly,according to the results of the pre-assessment of the system layers and loops bymultivariate statisticalmethods,seven characteristic parameters that have a significant impact on the health state of the system are identified.Next,aiming at the problem of incomplete assessment data set due to the uneven distribution of actual system operating health state,the original unbalanced dataset is augmented using aWasserstein generative adversarial network with gradient penalty term,and a complete dataset is obtained to characterise all the health states of the system.On this basis,a new deep learning-based health assessment framework for the benzeneto-ethylene ratio control system is constructed based on traditionalmultivariate statistical assessment.This framework can overcome the shortcomings of the linear weighted fusion related to the coupling and nonlinearity of the subsystem health state at different layers,and reduce the dependence of the prior knowledge.Furthermore,by introducing a dynamic attention mechanism(AM)into the convolutional neural network(CNN),the assessment model integrating both assessment and traceability is constructed,which can achieve the health assessment and trace the non-optimal factors of the complex control systems with the double-layer architecture.Finally,the effectiveness and superiority of the proposed method have been verified by the benzene-ethylene ratio control system of the alkylation process unit in a styrene plant.
