Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)mo...Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)model to represent allowable variations of the CSTR-DC-recycle chemical processes.The NOZ is a geometric space containing all safe operating points of the CSTR-DC-recycle chemical processes,so that it is an effective model for process monitoring.The novelty of the proposed method is to establish the NOZ model based on boundary points.The boundary points make it possible to capture the actual geometric space irrespective of the space shape.In contrast,existing methods represent the NOZ of processes by fixed mathematical models such as ellipsoidal and convex-hull models;they are not suitable for the CSTR-DC-recycle chemical processes whose NOZs cannot be exactly defined by fixed mathematical structures.Simulated case studies based on Aspen Hysys software are given to illustrate the proposed method.展开更多
The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is ...The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.展开更多
A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solvin...A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.展开更多
With the 3D chemical transport model OSLO CTM2, the valley of total column ozone over the Tibetan Plateau in summer is reproduced. The results show that when the ozone valley occurs and develops, the transport process...With the 3D chemical transport model OSLO CTM2, the valley of total column ozone over the Tibetan Plateau in summer is reproduced. The results show that when the ozone valley occurs and develops, the transport process plays the main part in the ozone reduction, but the chemical process partly compensates for the transport process. In the dynamic transport process of ozone, the horizontal transport process plays the main part in the ozone reduction in May, but brings about the ozone increase in June and July. The vertical advective process gradually takes the main role in the ozone reduction in June and July. The effect of convective activities rises gradually so that this effect cannot be overlooked in July, as its magnitude is comparable to that of the net changes. The effect of the gaseous chemical process brings about ozone increases which are more than the net changes sometimes, so the chemical effect is also important.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
The recent works on the development of computational mass transfer （CMT） method and its applications in chemical process simulation are reviewed. Some development strategies and challenges in future research are als...The recent works on the development of computational mass transfer （CMT） method and its applications in chemical process simulation are reviewed. Some development strategies and challenges in future research are also discussed.展开更多
Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software e...Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.展开更多
To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variabl...To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.展开更多
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient ...The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.展开更多
Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identific...Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.展开更多
In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and en...In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.展开更多
Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing.Due to the variations in material,equipment and environment,the process variables monitored by sensors contai...Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing.Due to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes.Despite much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode condition.To overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern information.The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition.The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method.Compared with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.展开更多
Due to the scale effect, the uniform distribution of reagents in continuous flow reactor becomes bad when the channel is enlarged to tens of millimeters. Microfluidic field strategy was proposed to produce high mixing...Due to the scale effect, the uniform distribution of reagents in continuous flow reactor becomes bad when the channel is enlarged to tens of millimeters. Microfluidic field strategy was proposed to produce high mixing efficiency in large-scale channel. A 3D spiral baffle structure(3SBS) was designed and optimized to form microfluidic field disturbed by continuous secondary flow in millimeter scale Y-shaped tube mixer(YSTM). Enhancement effect of the 3SBS in liquid-liquid homogeneous chemical processes was verified and evaluated through the combination of simulation and experiment. Compared with 1 mm YSTM, 10 mm YSTM with 3SBS increased the treatment capacity by 100 times, shortened the basic complete mixing time by 0.85 times, which proves the potential of microfluidic field strategy in enhancement and scale-up of liquid-liquid homogeneous chemical process.展开更多
Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been w...Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.展开更多
Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches...Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.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
Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource pr...Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource process data,resulting in a low accuracy of existing prediction technology.For that reason,a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory(Attention-based Bi-LSTM)is proposed in this study.First,multisource process data,such as temperature,pressure,flow rate,and liquid level,are preprocessed for denoising.Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations,thus establishing a complex network model oriented to the chemical production process.Second,network structure entropy is introduced to reduce the dimensions of the multisource process data.Moreover,a 1D relative risk sequence is acquired by maxemin deviation standardization to judge whether the chemical process is in a steady state.Finally,an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences.In that way,the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production.A case study based on the Tennessee Eastman process(TEP)is conducted.The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions.Also,the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.展开更多
Based on frequency response and convex optimization,a novel optimal control system was developed for chemical processes.The feedforward control is designed to improve the tracking performance of closed loop chemical s...Based on frequency response and convex optimization,a novel optimal control system was developed for chemical processes.The feedforward control is designed to improve the tracking performance of closed loop chemical systems.The parametric model is not required because the system directly utilizes the frequency response of the loop transfer function,which can be measured accurately.In particular,the extremal values of magnitude and phase can be solved according to constrained quadratic programming optimizer and convex optimization.Simulation examples show the effectiveness of the method.The design method is simple and easily adopted in chemical industry.展开更多
The catalytic properties of non-reducible metal oxides have intrigued continuous interest in the past decades.Often time,catalytic studies of bulk non-reducible oxides focused on their high-temperature applications ow...The catalytic properties of non-reducible metal oxides have intrigued continuous interest in the past decades.Often time,catalytic studies of bulk non-reducible oxides focused on their high-temperature applications owing to their weak interaction with small molecules.Hereby,combining ambient-pressure scanning tunneling microscopy(AP-STM),AP X-ray photoelectron spectroscopy(AP-XPS)and density functional theory(DFT)calculations,we studied the activation of CO and CO_(2)on ZnO,a typical nonreducible oxide and major catalytic material in the conversion of C1 molecules.By visualizing the chemical processes on ZnO surfaces at the atomic scale under AP conditions,we showed that new adsorbate structures induced by the enhanced physisorption and the concerted interaction of physisorbed molecules could facilitate the activation of CO and CO_(2)on ZnO.The reactivity of ZnO towards CO could be observed under AP conditions,where an ordered(2×1)–CO structure was observed on ZnO(1010).Meanwhile,chemisorption of CO_(2)on ZnO(1010)under AP conditions was also enhanced by physisorbed CO_(2),which minimizes the repulsion between surface dipoles and causes a(3×1)–CO_(2)structure.Our study has brought molecular insight into the fundamental chemistry and catalytic properties of ZnO surfaces under realistic reaction conditions.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
基金partially funded by the National Natural Science Foundation of China(62273215)。
文摘Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)model to represent allowable variations of the CSTR-DC-recycle chemical processes.The NOZ is a geometric space containing all safe operating points of the CSTR-DC-recycle chemical processes,so that it is an effective model for process monitoring.The novelty of the proposed method is to establish the NOZ model based on boundary points.The boundary points make it possible to capture the actual geometric space irrespective of the space shape.In contrast,existing methods represent the NOZ of processes by fixed mathematical models such as ellipsoidal and convex-hull models;they are not suitable for the CSTR-DC-recycle chemical processes whose NOZs cannot be exactly defined by fixed mathematical structures.Simulated case studies based on Aspen Hysys software are given to illustrate the proposed method.
文摘The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in University of China (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013).
文摘A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.
文摘With the 3D chemical transport model OSLO CTM2, the valley of total column ozone over the Tibetan Plateau in summer is reproduced. The results show that when the ozone valley occurs and develops, the transport process plays the main part in the ozone reduction, but the chemical process partly compensates for the transport process. In the dynamic transport process of ozone, the horizontal transport process plays the main part in the ozone reduction in May, but brings about the ozone increase in June and July. The vertical advective process gradually takes the main role in the ozone reduction in June and July. The effect of convective activities rises gradually so that this effect cannot be overlooked in July, as its magnitude is comparable to that of the net changes. The effect of the gaseous chemical process brings about ozone increases which are more than the net changes sometimes, so the chemical effect is also important.
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 Science Foundation of China（20736005）.ACKNOWLEDGEMENTSThe authors acknowledge the assistance from thestaff in the State Key Laboratories of Chemical Engineering （Tianjin University）.
文摘The recent works on the development of computational mass transfer （CMT） method and its applications in chemical process simulation are reviewed. Some development strategies and challenges in future research are also discussed.
文摘Signed direct graph (SDG) theory provides algorithms and methods that can be applied directly to chemical process modeling and analysis to validate simulation models, and is a basis for the development of a software environment that can automate the validation activity. This paper is concentrated on the pretreatment of the model validation. We use the validation scenarios and standard sequences generated by well-established SDG model to validate the trends fitted from the simulation model. The results are helpful to find potential problems, assess possible bugs in the simulation model and solve the problem effectively. A case study on a simulation model of boiler is presented to demonstrate the effectiveness of this method.
基金Supported by the National Natural Science Foundation of China(21576143)
文摘To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.
基金Supported by Major State Basic Research Development Program of China （2012CB720500）, National Natural Science Foundation of China （Key Program： Ul162202）, National Science Fund for Outstanding Young Scholars （61222303）, National Natural Science Foundation of China （21276078, 21206037） and the Fundamental Research Funds for the Central Universities.
文摘The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. 13enetic algorithm （GA） has been proved to be a teasibte method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm （HIGA） for solving dynamic optimization problems is proposed to overcome these defects. Simplex method （SM） is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Ganssian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
基金Financial support for carrying out this work was provided by the Shandong Provincial Key Research and Development Program(2018YFJH0802)。
文摘Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.
基金Supported by Dalian University of Technology, the US National Science Foundation （No.CTS-0407494） and the Texas Advanced Technology program （No.003581-0044-2003）
文摘In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.
基金support from the National Science and Technology Innovation 2030 Major Project of the Ministry of Science and Technology of China(2018AAA0101605)the National Natural Science Foundation of China(21878171)。
文摘Intelligent fault recognition techniques are essential to ensure the long-term reliability of manufacturing.Due to the variations in material,equipment and environment,the process variables monitored by sensors contain diverse data characteristics at different time scales or in multiple operating modes.Despite much progress in statistical learning and deep learning for fault recognition,most models are constrained by abundant diagnostic expertise,inefficient multiscale feature extraction and unruly multimode condition.To overcome the above issues,a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the selection of relevant fault pattern information.The triplet loss optimization is adopted to increase the discrimination capability of the model under the multimode condition.The benchmarks CSTR simulation and Tennessee Eastman process are utilized to verify and illustrate the feasibility and efficiency of the proposed method.Compared with other common models,AMCNN shows its outstanding fault diagnosis performance and great generalization ability.
基金supported by the National Key Research and Development Program of China (2021YFC2101900 and 2019YFA0905000)National Natural Science Foundation of China (21908094, 21776130 and 22078150)+1 种基金Nanjing International Joint Research and Development Project (202002037)Top-notch Academic Programs Project of Jiangsu Higher Education Institutions。
文摘Due to the scale effect, the uniform distribution of reagents in continuous flow reactor becomes bad when the channel is enlarged to tens of millimeters. Microfluidic field strategy was proposed to produce high mixing efficiency in large-scale channel. A 3D spiral baffle structure(3SBS) was designed and optimized to form microfluidic field disturbed by continuous secondary flow in millimeter scale Y-shaped tube mixer(YSTM). Enhancement effect of the 3SBS in liquid-liquid homogeneous chemical processes was verified and evaluated through the combination of simulation and experiment. Compared with 1 mm YSTM, 10 mm YSTM with 3SBS increased the treatment capacity by 100 times, shortened the basic complete mixing time by 0.85 times, which proves the potential of microfluidic field strategy in enhancement and scale-up of liquid-liquid homogeneous chemical process.
基金Supported by the Major State Basic Research Development Program of China(2012CB720500)the National Natural Science Foundation of China(Key Program:U1162202)+2 种基金the National Science Fund for Outstanding Young Scholars(61222303)the National Natural Science Foundation of China(61174118,21206037)Shanghai Leading Academic Discipline Project(B504)
文摘Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.
基金support of the National Key Research and Development Program of China(2021YFB4000505).
文摘Fault detection and diagnosis(FDD)plays a significant role in ensuring the safety and stability of chemical processes.With the development of artificial intelligence(AI)and big data technologies,data-driven approaches with excellent performance are widely used for FDD in chemical processes.However,improved predictive accuracy has often been achieved through increased model complexity,which turns models into black-box methods and causes uncertainty regarding their decisions.In this study,a causal temporal graph attention network(CTGAN)is proposed for fault diagnosis of chemical processes.A chemical causal graph is built by causal inference to represent the propagation path of faults.The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations.Experiments in the Tennessee Eastman(TE)process and the green ammonia(GA)process showed that CTGAN achieved high performance and good explainability.
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 the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406)the National Key Research&Development Program of China(2021YFB3301100).
文摘Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource process data,resulting in a low accuracy of existing prediction technology.For that reason,a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory(Attention-based Bi-LSTM)is proposed in this study.First,multisource process data,such as temperature,pressure,flow rate,and liquid level,are preprocessed for denoising.Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations,thus establishing a complex network model oriented to the chemical production process.Second,network structure entropy is introduced to reduce the dimensions of the multisource process data.Moreover,a 1D relative risk sequence is acquired by maxemin deviation standardization to judge whether the chemical process is in a steady state.Finally,an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences.In that way,the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production.A case study based on the Tennessee Eastman process(TEP)is conducted.The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions.Also,the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.
基金Supported by the National Natural Science Foundation of China（51205133） Natural Science Foundation of Shanghai（11ZR1409000） Ph.D.Programs Foundation of Ministry of Education of China（20110074120007）
文摘Based on frequency response and convex optimization,a novel optimal control system was developed for chemical processes.The feedforward control is designed to improve the tracking performance of closed loop chemical systems.The parametric model is not required because the system directly utilizes the frequency response of the loop transfer function,which can be measured accurately.In particular,the extremal values of magnitude and phase can be solved according to constrained quadratic programming optimizer and convex optimization.Simulation examples show the effectiveness of the method.The design method is simple and easily adopted in chemical industry.
基金financially supported by the Ministry of Science and Technology of China(2018YFA0208603)the National Natural Science Foundation of China(21972144,91545204,91845109,91945302,22002090)+2 种基金the Chinese Academy of Sciences(QYZDJSSW-SLH054)the K.C.Wong Education(GJTD-2020-15)supported by ME2 project under contract no.11227902 from National Natural Science Foundation of China。
文摘The catalytic properties of non-reducible metal oxides have intrigued continuous interest in the past decades.Often time,catalytic studies of bulk non-reducible oxides focused on their high-temperature applications owing to their weak interaction with small molecules.Hereby,combining ambient-pressure scanning tunneling microscopy(AP-STM),AP X-ray photoelectron spectroscopy(AP-XPS)and density functional theory(DFT)calculations,we studied the activation of CO and CO_(2)on ZnO,a typical nonreducible oxide and major catalytic material in the conversion of C1 molecules.By visualizing the chemical processes on ZnO surfaces at the atomic scale under AP conditions,we showed that new adsorbate structures induced by the enhanced physisorption and the concerted interaction of physisorbed molecules could facilitate the activation of CO and CO_(2)on ZnO.The reactivity of ZnO towards CO could be observed under AP conditions,where an ordered(2×1)–CO structure was observed on ZnO(1010).Meanwhile,chemisorption of CO_(2)on ZnO(1010)under AP conditions was also enhanced by physisorbed CO_(2),which minimizes the repulsion between surface dipoles and causes a(3×1)–CO_(2)structure.Our study has brought molecular insight into the fundamental chemistry and catalytic properties of ZnO surfaces under realistic reaction conditions.
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.
