Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based ...Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.展开更多
Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.H...Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.展开更多
This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is va...This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is validated from soft tissue cutting. Then, based on the acquired input-output data pairs, a method for fuzzy system modeling is presented, that is, after partitioning each input space equally and giving the premises and the total number of fuzzy rules, the consequent parameters and the fuzzy membership functions (MF) of the input variables are learned and optimized via a neurofuzzy modeling technique. Finally, a haptic scalpel implemented with the established cutting model is described. Preliminary results show the feasibility of the haptic display system for real-time interaction.展开更多
随着国际海事卫星通信系统(international maritime satellite communication system,INMARSAT)的不断发展,卫星地面站(satellite earth station,SES)的可用性评估具有重要意义。首先选取可靠性、维修性和保障性为指标,构建可靠性、维...随着国际海事卫星通信系统(international maritime satellite communication system,INMARSAT)的不断发展,卫星地面站(satellite earth station,SES)的可用性评估具有重要意义。首先选取可靠性、维修性和保障性为指标,构建可靠性、维修性与保障性(reliability,maintainability and supportability,RMS)模型进行SES可用性评估;其次,以SES设备为研究对象,根据马尔可夫链得到设备有效性概率,并进一步推导出系统可靠性;然后,建立排队图解协调技术(queue-graphical evaluation and review technique,Q-GERT)网络以描述SES设备的维修保障过程,并基于AnyLogic软件进行建模仿真;最后,通过算例进行应用研究,基于RMS模型进行SES可用性评估,验证了该模型和评估方法的有效性与实用性。展开更多
In this paper,we present a review of the current literature on distributed(or partially decentralized) control of chemical process networks.In particular,we focus on recent developments in distributed model predictive...In this paper,we present a review of the current literature on distributed(or partially decentralized) control of chemical process networks.In particular,we focus on recent developments in distributed model predictive control,in the context of the specific challenges faced in the control of chemical process networks.The paper is concluded with some open problems and some possible future research directions in the area.展开更多
针对定性符号有向图(signed directed graph,SDG)在化工过程系统中建模复杂度高、故障分辨率低、容易忽略部分变量等问题,提出一种基于复杂网络理论构建层次SDG网络模型并识别关键节点的方法。首先利用层次分析法对化工过程系统划分递...针对定性符号有向图(signed directed graph,SDG)在化工过程系统中建模复杂度高、故障分辨率低、容易忽略部分变量等问题,提出一种基于复杂网络理论构建层次SDG网络模型并识别关键节点的方法。首先利用层次分析法对化工过程系统划分递阶层次结构,建立基于子系统的系统SDG网络模型,选取度中心性、接近中心性等多个节点重要性评价指标,采用主成分分析法确定各指标权重并利用逼近理想排序法(technique for order preference by similarity to an ideal solution,TOPSIS)多属性决策方法得到节点重要性的综合评价值,初步识别关键节点所在的子系统;然后建立子系统的SDG模型并细化为有向网络,采用Leader Rank算法对节点重要性进行排序,进而在子系统网络模型中确定关键节点的位置。案例计算结果表明该方法可以有效地降低建模的复杂性,提高关键节点识别的全面性和准确性,从而改善化工过程系统的安全稳定性。展开更多
基金National Innovation and Entrepreneurship Program for College Students(202218213001)Science and Technology Innovation Strategy of Guangdong Province(Science and Technology Innovation Cultivation of University Students 2020329182130C000002).
文摘Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage students.To address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture interaction.Methods The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine.By establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was created.Using code script logic,particle systems,as well as other systems,chemical phenomena were simulated.Furthermore,we created an online teaching platform using streaming media and databases to address the problems of distance teaching.Results The proposed system was evaluated against two mainstream products in the market.In the experiments,the proposed system outperformed the other products in terms of fidelity and practicality.Conclusions The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
基金supported by the National Natural Science Foundation of China(21978203,21676183).
文摘Process optimization in equation-oriented(EO)modeling environments favors the gradient-based optimization algorithms by their abilities to provide accurate Jacobian matrices via automatic or symbolic differentiation.However,computational inefficiencies including that in initial-point-finding for Newton type methods have significantly limited its application.Recently,progress has been made in using a pseudo-transient(PT)modeling method to address these difficulties,providing a fresh way forward in EO-based optimization.Nevertheless,research in this area remains open,and challenges need to be addressed.Therefore,understanding the state-of-the-art research on the PT method,its principle,and the strategies in composing effective methodologies using the PT modeling method is necessary for further developing EO-based methods for process optimization.For this purpose,the basic concepts for the PT modeling and the optimization framework based on the PT model are reviewed in this paper.Several typical applications,e.g.,complex distillation processes,cryogenic processes,and optimizations under uncertainty,are presented as well.Finally,we identify several main challenges and give prospects for the development of the PT based optimization methods.
基金Supported by National Natural Science Foundation of P. R. China (60273028)
文摘This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is validated from soft tissue cutting. Then, based on the acquired input-output data pairs, a method for fuzzy system modeling is presented, that is, after partitioning each input space equally and giving the premises and the total number of fuzzy rules, the consequent parameters and the fuzzy membership functions (MF) of the input variables are learned and optimized via a neurofuzzy modeling technique. Finally, a haptic scalpel implemented with the established cutting model is described. Preliminary results show the feasibility of the haptic display system for real-time interaction.
文摘随着国际海事卫星通信系统(international maritime satellite communication system,INMARSAT)的不断发展,卫星地面站(satellite earth station,SES)的可用性评估具有重要意义。首先选取可靠性、维修性和保障性为指标,构建可靠性、维修性与保障性(reliability,maintainability and supportability,RMS)模型进行SES可用性评估;其次,以SES设备为研究对象,根据马尔可夫链得到设备有效性概率,并进一步推导出系统可靠性;然后,建立排队图解协调技术(queue-graphical evaluation and review technique,Q-GERT)网络以描述SES设备的维修保障过程,并基于AnyLogic软件进行建模仿真;最后,通过算例进行应用研究,基于RMS模型进行SES可用性评估,验证了该模型和评估方法的有效性与实用性。
基金supported by Australian Research Council(ARC)Discovery Project(No.DP130103330)
文摘In this paper,we present a review of the current literature on distributed(or partially decentralized) control of chemical process networks.In particular,we focus on recent developments in distributed model predictive control,in the context of the specific challenges faced in the control of chemical process networks.The paper is concluded with some open problems and some possible future research directions in the area.
文摘针对定性符号有向图(signed directed graph,SDG)在化工过程系统中建模复杂度高、故障分辨率低、容易忽略部分变量等问题,提出一种基于复杂网络理论构建层次SDG网络模型并识别关键节点的方法。首先利用层次分析法对化工过程系统划分递阶层次结构,建立基于子系统的系统SDG网络模型,选取度中心性、接近中心性等多个节点重要性评价指标,采用主成分分析法确定各指标权重并利用逼近理想排序法(technique for order preference by similarity to an ideal solution,TOPSIS)多属性决策方法得到节点重要性的综合评价值,初步识别关键节点所在的子系统;然后建立子系统的SDG模型并细化为有向网络,采用Leader Rank算法对节点重要性进行排序,进而在子系统网络模型中确定关键节点的位置。案例计算结果表明该方法可以有效地降低建模的复杂性,提高关键节点识别的全面性和准确性,从而改善化工过程系统的安全稳定性。
