A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-...A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.展开更多
A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeli...A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.展开更多
Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a se...Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.展开更多
In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped ki...In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.展开更多
Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on s...Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.展开更多
A first principles-based dynamic model for a continuous catalyst regeneration (CCR) platforming process, the UOP commercial naphtha catalytic reforming process, is developed in this paper. The lumping details of the n...A first principles-based dynamic model for a continuous catalyst regeneration (CCR) platforming process, the UOP commercial naphtha catalytic reforming process, is developed in this paper. The lumping details of the naphtha feed and reaction scheme of the reaction model are given. The process model is composed of the reforming reaction model with catalyst deactivation, the furnace model and the separator model, which is capable of capturing the major dynamics that occurs in this process system. Dynamic simulations are performed based on Gear numerical algorithm and method of lines (MOL), a numerical technique dealing with partial differential equations (PDEs). The results of simulation are also presented. Dynamic responses caused by disturbances in the process system can be correctly predicted through simulations.展开更多
This paper is concerned with the problem of robust stability analysis for networked control systems （NCSs）. A new NCS model is proposed under consideration of both the network-induced delay and parameter uncertainti...This paper is concerned with the problem of robust stability analysis for networked control systems （NCSs）. A new NCS model is proposed under consideration of both the network-induced delay and parameter uncertainties. The parameter uncertainties appearing in NCSs are norm-bounded, and possibly time-varying. The conventional method and the descriptor system method are used to obtain maximum allowable delay bound （MADB） guaranteeing robust stability and stability of the NCSs, respectively, where the stability criteria are formulated in terms of linear matrix inequalities （LMIs）. And the MADB can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to illustrate the effectiveness of the proposed method.展开更多
A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machi...A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.展开更多
The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs...The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs with parameter uncertainty, network-induced delay, nonlinearity and data packet dropout in the transmission, a strict linear matrix inequality （LMI） criterion is proposed for robust stabilization of the uncertain nonlinear NCSs based on the Lyapunov stability theory. The maximum allowable transfer interval （MATI） can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to demonstrate the applicability of the proposed algorithm.展开更多
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Ak...A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.展开更多
Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Althou...Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.展开更多
The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive obj...The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.展开更多
The problem of robust H-infinity control for a class of uncertain singular time-delay systems is studied in this paper. A new approach is proposed to describe the relationship between slow and fast subsystems of singu...The problem of robust H-infinity control for a class of uncertain singular time-delay systems is studied in this paper. A new approach is proposed to describe the relationship between slow and fast subsystems of singular time- delay systems, based on which, a sufficient condition is presented for a singular time-delay system to be regular, impulse free and stable with an H-infinity performance. The robust H-infinity control problem is solved and an explicit expression of the desired state-feedback control law is also given. The obtained results are formulated in terms of strict linear matrix inequalities （LMIs） involving no decomposition of system matrices. A numerical example is given to show the effectiveness of the proposed method.展开更多
An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is pre...An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the “worst-case” objective function is converted into the linear objective minimization problem in- volving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.展开更多
In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-s...In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.展开更多
In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to laten...In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.展开更多
A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining...A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.展开更多
基金Supported by the National Natural Science Foundation of China (No.60421002).
文摘A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.
基金Supported by the National Outstanding Youth Science Foundation of China (No. 60025308).
文摘A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First,a set of preliminary input variables is selected according to prior knowledge and experience. Secondly,a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables.The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.
基金National Key Technologies Research and Development Program in the 10th Five-year Phan(No.2001BA204B01)National Outstanding Youth Science Foundation of China(No.60025308)
文摘Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process modei that has been proved to describe industrial process quite well. The modei is a semi-empirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithra applied in this study is non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ). The performance of NSGA-Ⅱ is further illustrated by application to the title process.
基金Supported by the National Natural Science Foundation of China (No.60421002).
文摘In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set.
基金National Natural Foundation of China (No.60421002, No.70471052)
文摘Chemical process variables are always driven by random noise and disturbances. The closed-loop con-trol yields process measurements that are auto and cross correlated. The influence of auto and cross correlations on statistical process control (SPC) is investigated in detail by Monte Carlo experiments. It is revealed that in the sense of average performance, the false alarms rates (FAR) of principal component analysis (PCA), dynamic PCA are not affected by the time-series structures of process variables. Nevertheless, non-independent identical distribution will cause the actual FAR to deviate from its theoretic value apparently and result in unexpected consecutive false alarms for normal operating process. Dynamic PCA and ARMA-PCA are demonstrated to be inefficient to remove the influences of auto and cross correlations. Subspace identification-based PCA (SI-PCA) is proposed to improve the monitoring of dynamic processes. Through state space modeling, SI-PCA can remove the auto and cross corre-lations efficiently and avoid consecutive false alarms. Synthetic Monte Carlo experiments and the application in Tennessee Eastman challenge process illustrate the advantages of the proposed approach.
文摘A first principles-based dynamic model for a continuous catalyst regeneration (CCR) platforming process, the UOP commercial naphtha catalytic reforming process, is developed in this paper. The lumping details of the naphtha feed and reaction scheme of the reaction model are given. The process model is composed of the reforming reaction model with catalyst deactivation, the furnace model and the separator model, which is capable of capturing the major dynamics that occurs in this process system. Dynamic simulations are performed based on Gear numerical algorithm and method of lines (MOL), a numerical technique dealing with partial differential equations (PDEs). The results of simulation are also presented. Dynamic responses caused by disturbances in the process system can be correctly predicted through simulations.
基金supported by the National Natural Science Foundation of China (No.60721062)863 Program of China (No.2006AA04Z182)+1 种基金Department of Science and Technology Project of Zhejiang Province (No.2006C31016)Science Foundation of Zhejiang SciTech University(No.0803817-Y)
文摘This paper is concerned with the problem of robust stability analysis for networked control systems （NCSs）. A new NCS model is proposed under consideration of both the network-induced delay and parameter uncertainties. The parameter uncertainties appearing in NCSs are norm-bounded, and possibly time-varying. The conventional method and the descriptor system method are used to obtain maximum allowable delay bound （MADB） guaranteeing robust stability and stability of the NCSs, respectively, where the stability criteria are formulated in terms of linear matrix inequalities （LMIs）. And the MADB can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to illustrate the effectiveness of the proposed method.
基金Project (No. 60421002) supported by the National Natural ScienceFoundation of China
文摘A new multi-step adaptive predictive control algorithm for a class of bilinear systems is presented. The structure of the bilinear system is converted into a simple linear model by using nonlinear support vector machine (SVM) dynamic approximation with analytical control law derived. The method does not need on-line parameters estimation because the system’s internal model has been transformed into an off-line global model. Compared with other traditional methods, this control law reduces on-line parameter estimating burden. In addition, its overall linear behavior treating method allows an analytical control law available and avoids on-line nonlinear optimization. Simulation results are presented in the article to illustrate the efficiency of the method.
基金the National Natural Science Foundation of China (No.60421002)the National High Technology Research and Development Program of China under grant 863 Program (2006AA04 Z182).
文摘The problem of robust stabilization for a class of uncertain networked control systems （NCSs） with nonlinearities satisfying a given sector condition is investigated in this paper. By introducing a new model of NCSs with parameter uncertainty, network-induced delay, nonlinearity and data packet dropout in the transmission, a strict linear matrix inequality （LMI） criterion is proposed for robust stabilization of the uncertain nonlinear NCSs based on the Lyapunov stability theory. The maximum allowable transfer interval （MATI） can be derived by solving the feasibility problem of the corresponding LMI. Some numerical examples are provided to demonstrate the applicability of the proposed algorithm.
基金Project supported by the National Creative Research Groups Science Foundation of China (No. 60421002)the National "Tenth Five-Year" Science and Technology Research Program of China (No.2004BA204B08)
文摘A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.
基金Supported by the National Creative Research Groups Science Foundation of P.R. China （NCRGSFC： 60421002） and National High Technology Research and Development Program of China （863 Program） （2006AA04 Z182）
基金the National Natural Science Foundation of China （No.60421002）.
文摘Principal component analysis （PCA） is a useful tool in process fault detection, but offers little support on fault isolation. In this article, structured residual with strong isolation property is introduced. Although it is easy to get the residual by transformation matrix in static process, unfortunately, it becomes hard in dynamic process under control loop. Therefore, partial dynamic PCA（PDPCA） is proposed to obtain structured residual and enhance the isolation ability of dynamic process monitoring, and a compound statistic is introduced to resolve the problem resulting from independent variables in every variable subset. Simulations on continuous stirred tank reactor （CSTR） show the effectiveness of the proposed method.
基金Supported by the National Natural Science Foundation of China （No.60421002）
文摘The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.
基金This work was supported by the National Creative Research Groups Science Foundation of China (No. 60421002) and the New Century 151 Talent Projectof Zhejiang Province.
文摘The problem of robust H-infinity control for a class of uncertain singular time-delay systems is studied in this paper. A new approach is proposed to describe the relationship between slow and fast subsystems of singular time- delay systems, based on which, a sufficient condition is presented for a singular time-delay system to be regular, impulse free and stable with an H-infinity performance. The robust H-infinity control problem is solved and an explicit expression of the desired state-feedback control law is also given. The obtained results are formulated in terms of strict linear matrix inequalities （LMIs） involving no decomposition of system matrices. A numerical example is given to show the effectiveness of the proposed method.
基金Project (No. 60421002) supported by the National Natural ScienceFoundation of China
文摘An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the “worst-case” objective function is converted into the linear objective minimization problem in- volving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.
基金Supported by the National Natural Science Foundation of China （No.60421002） and the New Century 151 Talent Project of Zhejiang Province.
文摘In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.
基金Supported by National Natural Science Foundation of China （60721062） and National High Technology Research and Development Program of China （863 Program） （2006AA04Z182）
基金Supported by National Young Science Foundation of P.R.China(60604003)National Natural Science Key Foundation of P.R.China(60434020)National Key Technologies Research and Development Program in the 10th Five-year Plan(2001BA204B01)
文摘这份报纸处理与州的时间延期，参数无常和未知统计特征，但是与有限力量骚乱为 Lurie 单个系统的一个班过滤的柔韧的 H 的问题，试图设计一个要用体力地稳定的过滤器以便单个系统是的不明确的 Lurie 时间延期不仅常规，免费、稳定的推动，而且为所有可被考虑的无常为过滤错误动力学有 H 性能的规定水平。为如此的一个过滤器的存在的一个足够的条件以线性矩阵不平等(LMI ) 被建议。当 LMI 的这个集合的一个答案存在时，一个需要的过滤器的参量的矩阵能容易用 LMI 工具箱被获得。
文摘In order to reduce the variations of the product quality in batch processes, multivariate statistical process control methods according to multi-way principal component analysis (MPCA) or multi-way projection to latent structure (MPLS) were proposed for on-line batch process monitoring. However, they are based on the decomposition of relative covariance matrix and strongly affected by outlying observations. In this paper, in view of an efficient projection pursuit algorithm, a robust statistical batch process monitoring (RSBPM) framework,which is resistant to outliers, is proposed to reduce the high demand for modeling data. The construction of robust normal operating condition model and robust control limits are discussed in detail. It is evaluated on monitoring an industrial streptomycin fermentation process and compared with the conventional MPCA. The results show that the RSBPM framework is resistant to possible outliers and the robustness is confirmed.
基金This work is supported by the National Natural Science Foundation of China (No.60421002) Priority supported financially by the New Century 151 Talent Project of Zhejiang Province.
文摘A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.
