Block copolymers can yield a diverse array of nanostructures.Their assembly structures are influenced by their inherent structures,and the wide variety of structures that can be prepared especially becomes apparent wh...Block copolymers can yield a diverse array of nanostructures.Their assembly structures are influenced by their inherent structures,and the wide variety of structures that can be prepared especially becomes apparent when one considers the number of routes available to prepare block copolymer assemblies.Some examples include self-assembly,directed assembly,coupling,as well as hierarchical assembly,which can yield assemblies having even higher structural order.These assembly routes can also be complemented by processing techniques such as selective crosslinking and etching,the former technique leading to permanent structures,the latter towards sculpted and the combination of the two towards permanent sculpted structures.The combination of these pathways provides extremely versatile routes towards an exciting variety of architectures.This review will attempt to highlight destinations reached by LIU Guojun and coworkers following these pathways.展开更多
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
The rational utilization of nuclear energy is crucial in current global energy system.Using a flame denitrificationreactor,this study develops uranium trioxide(UO_(3)),a critical intermediate product in the nuclear fu...The rational utilization of nuclear energy is crucial in current global energy system.Using a flame denitrificationreactor,this study develops uranium trioxide(UO_(3)),a critical intermediate product in the nuclear fuel cycle,and systematically characterizes its physicochemical properties.The UO_(3) products are comprehensively examined to assess their suitability for downstream nuclear industry applications.Our results indicates that high-quality UO_(3) products can be obtained using flamedenitrificationreactor at temperatures between 440℃ and 480℃.This study reveals the considerable potential of UO_(3) production via flamedenitrification,marking a significantadvancement towards enhanced nuclear fuel cycle systems.展开更多
This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 30...This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 300 Nm^(3)/d,we developed an integrated simulation-optimization framework using Aspen HYSYS 14.0 to systematically evaluate the effects of critical operating parameters—absorption pressure,MDEA concentration,flow rate,temperature,number of trays,and reboiler duty—on methane purity and energy consumption.The key finding is the identification of an optimal parameter set:absorption pressure of 1200 kPa,MDEA concentration of 20mol%,lean flow rate of 2.5 kmol/h,temperature of 298.15 K,20 absorber trays,10 regenerator trays,and a reboiler duty of 4 kW,which enabled the product gas to achieve a high CH4 concentration of 97mol%,compliant with pipeline standards.A detailed energy consumption analysis revealed that the reboiler is the most energy-intensive unit,accounting for 75.40%of the total 5.29 kW energy consumption,followed by the gas compressor(23.38%).The specific energy consumption for CH4 recovery and the Energy Consumption Index(ECI)were quantified at 0.8852 kWh/kg CH_(4)and 6.82,respectively.This work provides a validated optimization strategy and critical energy breakdown,offering practical guidance for enhancing the technical and economic viability of small-scale,centralized biogas purification systems.展开更多
The application of high pressure favors many chemical processes, providing higher yields or improved rates in chemical reactions and improved solvent power in separation processes, and allowing activation barriers to ...The application of high pressure favors many chemical processes, providing higher yields or improved rates in chemical reactions and improved solvent power in separation processes, and allowing activation barriers to be overcome through the increase in molecular energy and molecular collision rates. High pressures-up to millions of bars using diamond anvil cells-can be achieved in the laboratory, and lead to many new routes for chemical synthesis and the synthesis of new materials with desirable thermody- namic, transport, and electronic properties. On the industrial scale, however, high-pressure processing is currently limited by the cost of compression and by materials limitations, so that few industrial processes are carried out at pressures above 25 MPa. An alternative approach to high-pressure processing is pro- posed here, in which very high local pressures are generated using the surface-driven interactions from a solid substrate. Recent experiments and molecular simulations show that such interactions can lead to local pressures as high as tens of thousands of bars （1 bar=1×10^5 Pa）, and even millions of bars in some cases. Since the active high-pressure processing zone is inhomogeneous, the pressure is different in dif- ferent directions. In many cases, it is the pressure in the direction parallel to the surface of the substrate （the tangential pressure） that is most greatly enhanced. This pressure is exerted on the molecules to be processed, but not on the solid substrate or the containing vessel. Current knowledge of such pressure enhancement is reviewed, and the possibility of an alternative route to high-pressure processing based on surface-driven forces is discussed. Such surface-driven high-pressure processing would have the advantage of achieving much higher pressures than are possible with traditional bulk-phase processing, since it eliminates the need for mechanical compression. Moreover, no increased pressure is exerted on the containing vessel for the process, thus eliminating concerns about materials failure.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
Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization h...Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization has resulted, in growing worldwide competitions in tradi.tional chemical .process industry. In order to keep competitive in the global marketplace, each company must optimize its production management and set up a reactive system for market fluctuation. Scheduling is the core of production management in chemical processes. The goal of this paper is to review the recent developments in this challenging area. Classifications of batch scheduling problems and optimization methods are introduced. A comparison of six typical models is shown in a general benchmark example from the literature. Finally, challenges and applications in future research are discussed.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
Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.Howe...Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.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
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
This perspectives article is intended highlight the growing importance and emergence of shale gas as an energy resource and as a source of chemicals. Over the next decades huge amounts of newly discovered deposits of ...This perspectives article is intended highlight the growing importance and emergence of shale gas as an energy resource and as a source of chemicals. Over the next decades huge amounts of newly discovered deposits of trapped gas are expected to be produced not only in the USA but elsewhere providing a wealth of methane and ethane not only used for energy production, but also for conversion to lower hydrocarbon chemicals. This manuscript seeks to focus on the potential of trapped natural gas around the world. The potential new volumes of trapped gas within shale or other mineral strata coming to the marketplace offer a tremendous opportunity if scientists can invent new, cost effective ways to convert this methane to higher value chemicals. Understanding how to selectively break a single C-H bond in methane while minimizing methane conversion to C02 is critical.展开更多
The pilot scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, ...The pilot scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, the biological function of the process was discussed. The results of optimal running showed that in the reaction tank, the concentration of mixed liquor suspended solid(MLSS) was 2 g/L, hydraulic retention time(HRT) was 35 min, dosage of liquid polyaluminium chloride(PAC) was 60 mg/L, and the concentration of polyacrylamide(PAM) was 0 5 mg/L. The effluent average concentrations of COD Cr , TP, SS and BOD 5 were 50 mg/L, 0 62 mg/L, 18 mg/L, and 17 mg/L, respectively. These were better than the designed demand. In addition, the existence of biological degradation in this system was proven by several methods. The removal efficiencies of the chemical and biological flocculation process were 20% higher than that of the chemical flocculation process above at the same coagulant dosage. The treatment process under different situations was evaluated on a pilot scale experiment, and the results provided magnificent parameters and optimal condition for future operation of the plant.展开更多
基金Guojun Liu wishes to thank NSERC of Canada for a Tier 1 Canada Research Chair and for funding
文摘Block copolymers can yield a diverse array of nanostructures.Their assembly structures are influenced by their inherent structures,and the wide variety of structures that can be prepared especially becomes apparent when one considers the number of routes available to prepare block copolymer assemblies.Some examples include self-assembly,directed assembly,coupling,as well as hierarchical assembly,which can yield assemblies having even higher structural order.These assembly routes can also be complemented by processing techniques such as selective crosslinking and etching,the former technique leading to permanent structures,the latter towards sculpted and the combination of the two towards permanent sculpted structures.The combination of these pathways provides extremely versatile routes towards an exciting variety of architectures.This review will attempt to highlight destinations reached by LIU Guojun and coworkers following these pathways.
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Xing Yuan Program of China National Nuclear Corporation(CNPE-8208)National Natural Science Foundation of China(22478056)for the financialsupport.
文摘The rational utilization of nuclear energy is crucial in current global energy system.Using a flame denitrificationreactor,this study develops uranium trioxide(UO_(3)),a critical intermediate product in the nuclear fuel cycle,and systematically characterizes its physicochemical properties.The UO_(3) products are comprehensively examined to assess their suitability for downstream nuclear industry applications.Our results indicates that high-quality UO_(3) products can be obtained using flamedenitrificationreactor at temperatures between 440℃ and 480℃.This study reveals the considerable potential of UO_(3) production via flamedenitrification,marking a significantadvancement towards enhanced nuclear fuel cycle systems.
基金funded by Shenzhen Science and Technology Program,grant number No.ZDSYS20230626091400001No.KCXST20221021111609024No.KCXFZ20240903093459001.
文摘This study addresses the energy-intensive challenge of small-scale biogas upgrading by optimizing a chemical absorption process employing methyl diethanolamine(MDEA).Focusing on a typical distributed application of 300 Nm^(3)/d,we developed an integrated simulation-optimization framework using Aspen HYSYS 14.0 to systematically evaluate the effects of critical operating parameters—absorption pressure,MDEA concentration,flow rate,temperature,number of trays,and reboiler duty—on methane purity and energy consumption.The key finding is the identification of an optimal parameter set:absorption pressure of 1200 kPa,MDEA concentration of 20mol%,lean flow rate of 2.5 kmol/h,temperature of 298.15 K,20 absorber trays,10 regenerator trays,and a reboiler duty of 4 kW,which enabled the product gas to achieve a high CH4 concentration of 97mol%,compliant with pipeline standards.A detailed energy consumption analysis revealed that the reboiler is the most energy-intensive unit,accounting for 75.40%of the total 5.29 kW energy consumption,followed by the gas compressor(23.38%).The specific energy consumption for CH4 recovery and the Energy Consumption Index(ECI)were quantified at 0.8852 kWh/kg CH_(4)and 6.82,respectively.This work provides a validated optimization strategy and critical energy breakdown,offering practical guidance for enhancing the technical and economic viability of small-scale,centralized biogas purification systems.
基金the US National Science Foundation (CBET-1603851 and CHE-1710102) for support of this workthe National Science Center of Poland (DEC-2013/09/B/ST4/03711) for support
文摘The application of high pressure favors many chemical processes, providing higher yields or improved rates in chemical reactions and improved solvent power in separation processes, and allowing activation barriers to be overcome through the increase in molecular energy and molecular collision rates. High pressures-up to millions of bars using diamond anvil cells-can be achieved in the laboratory, and lead to many new routes for chemical synthesis and the synthesis of new materials with desirable thermody- namic, transport, and electronic properties. On the industrial scale, however, high-pressure processing is currently limited by the cost of compression and by materials limitations, so that few industrial processes are carried out at pressures above 25 MPa. An alternative approach to high-pressure processing is pro- posed here, in which very high local pressures are generated using the surface-driven interactions from a solid substrate. Recent experiments and molecular simulations show that such interactions can lead to local pressures as high as tens of thousands of bars （1 bar=1×10^5 Pa）, and even millions of bars in some cases. Since the active high-pressure processing zone is inhomogeneous, the pressure is different in dif- ferent directions. In many cases, it is the pressure in the direction parallel to the surface of the substrate （the tangential pressure） that is most greatly enhanced. This pressure is exerted on the molecules to be processed, but not on the solid substrate or the containing vessel. Current knowledge of such pressure enhancement is reviewed, and the possibility of an alternative route to high-pressure processing based on surface-driven forces is discussed. Such surface-driven high-pressure processing would have the advantage of achieving much higher pressures than are possible with traditional bulk-phase processing, since it eliminates the need for mechanical compression. Moreover, no increased pressure is exerted on the containing vessel for the process, thus eliminating concerns about materials failure.
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Supported by the National Natural Science Foundation of China （20536020, 20876056）.
文摘Chemical batch processes have become significant in chemical manufacturing. In these processes, large numbers of chemical products are produced to satisfy human demands in daily life. Recently, economy globalization has resulted, in growing worldwide competitions in tradi.tional chemical .process industry. In order to keep competitive in the global marketplace, each company must optimize its production management and set up a reactive system for market fluctuation. Scheduling is the core of production management in chemical processes. The goal of this paper is to review the recent developments in this challenging area. Classifications of batch scheduling problems and optimization methods are introduced. A comparison of six typical models is shown in a general benchmark example from the literature. Finally, challenges and applications in future research are discussed.
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the support of the National Key Research and Development Program of China(2021YFB4000505)Sichuan Science and Technology Program(2021YFS0301)。
文摘Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.
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 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.
文摘This perspectives article is intended highlight the growing importance and emergence of shale gas as an energy resource and as a source of chemicals. Over the next decades huge amounts of newly discovered deposits of trapped gas are expected to be produced not only in the USA but elsewhere providing a wealth of methane and ethane not only used for energy production, but also for conversion to lower hydrocarbon chemicals. This manuscript seeks to focus on the potential of trapped natural gas around the world. The potential new volumes of trapped gas within shale or other mineral strata coming to the marketplace offer a tremendous opportunity if scientists can invent new, cost effective ways to convert this methane to higher value chemicals. Understanding how to selectively break a single C-H bond in methane while minimizing methane conversion to C02 is critical.
文摘The pilot scale experimental apparatus and the procedure of the chemical and biological flocculation process to verify the feasibility in treating Shanghai municipal sewage were introduced in this paper. In addition, the biological function of the process was discussed. The results of optimal running showed that in the reaction tank, the concentration of mixed liquor suspended solid(MLSS) was 2 g/L, hydraulic retention time(HRT) was 35 min, dosage of liquid polyaluminium chloride(PAC) was 60 mg/L, and the concentration of polyacrylamide(PAM) was 0 5 mg/L. The effluent average concentrations of COD Cr , TP, SS and BOD 5 were 50 mg/L, 0 62 mg/L, 18 mg/L, and 17 mg/L, respectively. These were better than the designed demand. In addition, the existence of biological degradation in this system was proven by several methods. The removal efficiencies of the chemical and biological flocculation process were 20% higher than that of the chemical flocculation process above at the same coagulant dosage. The treatment process under different situations was evaluated on a pilot scale experiment, and the results provided magnificent parameters and optimal condition for future operation of the plant.
