基于Aspen Batch Process Developer软件对原料药工程设计进行应用研究。在某原料药项目中,通过软件建立工艺流程模拟模型,分析批次操作时间、年生产批次、年生产规模、物料衡算、设备选型和利用率、公用工程消耗等,在满足生产需求的情...基于Aspen Batch Process Developer软件对原料药工程设计进行应用研究。在某原料药项目中,通过软件建立工艺流程模拟模型,分析批次操作时间、年生产批次、年生产规模、物料衡算、设备选型和利用率、公用工程消耗等,在满足生产需求的情况下,达到优化生产排班、设备选型和公用工程量的目的。展开更多
This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed...This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.展开更多
间歇生产过程具有弹性大、灵活等特点,其市场适应性较强。化工生产中间歇生产过程占相当比例,文章就间歇生产工艺的模拟与优化进行介绍,利用Aspen Batch Process Developer模拟间歇工艺过程,可以快速地得到工艺流程的物料衡算、热量衡...间歇生产过程具有弹性大、灵活等特点,其市场适应性较强。化工生产中间歇生产过程占相当比例,文章就间歇生产工艺的模拟与优化进行介绍,利用Aspen Batch Process Developer模拟间歇工艺过程,可以快速地得到工艺流程的物料衡算、热量衡算、操作时间、公用工程和成本估算等结果。同时,还可以对模拟结果进行分析,找出制约生产工艺的瓶颈,并对生产周期、生产规模、生产设备等进行优化,提高工艺设计效率,降低生产成本。展开更多
In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range pre...In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.展开更多
In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independen...In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.展开更多
The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the t...The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. The paper deals with two variants: the case only with two distinct arrival times and the general case. For the first case, an on-line algorithm with competitive ratio 119/44 is given. For the latter one, a simple algorithm with competitive ratio 3 is given. For both variants the better ratios can be obtained if the problem satisfies proportional assumption.展开更多
This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The ...This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.展开更多
A new method was developed for batch process monitoring in this paper.In the developed method,just-in-time learning(J1TL) and independent component analysis(ICA) were integrated to build JITL-ICA monitoring scheme.JIT...A new method was developed for batch process monitoring in this paper.In the developed method,just-in-time learning(J1TL) and independent component analysis(ICA) were integrated to build JITL-ICA monitoring scheme.JITL was employed to tackle with the characteristics of batch process such as inherent timevarying dynamics,multiple operating phases,and especially the case of uneven length stage.According to new coming test data,the most correlated segmentation was obtained from batch-wise unfolded training data by JITL.Then,ICA served as the principal components extraction approach.Therefore,the non-Gaussian distributed data can also be addressed under this modeling framework.The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.展开更多
In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome...In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.展开更多
Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is pr...Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.展开更多
This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network,...This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.展开更多
An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designe...An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.展开更多
Batch processes are important in chemical industry,in which operators usually play a major role and hazards may arise by their inadvertent acts.In this paper,based on hazard and operability study and concept of qualit...Batch processes are important in chemical industry,in which operators usually play a major role and hazards may arise by their inadvertent acts.In this paper,based on hazard and operability study and concept of qualitative simulation,an automatic method for adverse consequence identification for potential maloperation is proposed.The qualitative model for production process is expressed by a novel directed graph.Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords.The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations.The method is illustrated with a simple batch process and a batch reaction process.The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations.After analysis for possible plant maloperations,some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.展开更多
Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes w...Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.展开更多
In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component a...In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.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
文摘基于Aspen Batch Process Developer软件对原料药工程设计进行应用研究。在某原料药项目中,通过软件建立工艺流程模拟模型,分析批次操作时间、年生产批次、年生产规模、物料衡算、设备选型和利用率、公用工程消耗等,在满足生产需求的情况下,达到优化生产排班、设备选型和公用工程量的目的。
基金the National Natural Science Foundation of China(Grant Number 61573264).
文摘This study focuses on the scheduling problem of unrelated parallel batch processing machines(BPM)with release times,a scenario derived from the moulding process in a foundry.In this process,a batch is initially formed,placed in a sandbox,and then the sandbox is positioned on a BPM formoulding.The complexity of the scheduling problem increases due to the consideration of BPM capacity and sandbox volume.To minimize the makespan,a new cooperated imperialist competitive algorithm(CICA)is introduced.In CICA,the number of empires is not a parameter,and four empires aremaintained throughout the search process.Two types of assimilations are achieved:The strongest and weakest empires cooperate in their assimilation,while the remaining two empires,having a close normalization total cost,combine in their assimilation.A new form of imperialist competition is proposed to prevent insufficient competition,and the unique features of the problem are effectively utilized.Computational experiments are conducted across several instances,and a significant amount of experimental results show that the newstrategies of CICAare effective,indicating promising advantages for the considered BPMscheduling problems.
文摘间歇生产过程具有弹性大、灵活等特点,其市场适应性较强。化工生产中间歇生产过程占相当比例,文章就间歇生产工艺的模拟与优化进行介绍,利用Aspen Batch Process Developer模拟间歇工艺过程,可以快速地得到工艺流程的物料衡算、热量衡算、操作时间、公用工程和成本估算等结果。同时,还可以对模拟结果进行分析,找出制约生产工艺的瓶颈,并对生产周期、生产规模、生产设备等进行优化,提高工艺设计效率,降低生产成本。
基金This work was supported by the UK EPSRC (GR/N13319, GR/R10875).
文摘In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
基金Supported by the National Natural Science Foundation of China(No.61763029)the Natural Science Foundation of Gansu Province(1610RJZA016)
文摘In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.
文摘The on-line problem of scheduling on a batch processing machine with nonidentical job sizes to minimize makespan is considered. The batch processing machine can process a number of jobs simultaneously as long as the total size of these jobs being processed does not exceed the machine capacity. The processing time of a batch is given by the longest processing time of any job in the batch. Each job becomes available at its arrival time, which is unknown in advance, and its processing time becomes known upon its arrival. The paper deals with two variants: the case only with two distinct arrival times and the general case. For the first case, an on-line algorithm with competitive ratio 119/44 is given. For the latter one, a simple algorithm with competitive ratio 3 is given. For both variants the better ratios can be obtained if the problem satisfies proportional assumption.
基金Supported by the National Natural Science Foundation of China (60974040, 61120106009), the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China (BS2011DX010), and the High School Science & Technol- ogy Fund Planning Project of Shandong Province of China (J 10LG32).
文摘This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.
基金National Natural Science Foundations of China(Nos.61403256,61374132)Special Scientific Research of Selection and Cultivation of Excellent Young Teachers in Shanghai Universities,China(No.YYY11076)
文摘A new method was developed for batch process monitoring in this paper.In the developed method,just-in-time learning(J1TL) and independent component analysis(ICA) were integrated to build JITL-ICA monitoring scheme.JITL was employed to tackle with the characteristics of batch process such as inherent timevarying dynamics,multiple operating phases,and especially the case of uneven length stage.According to new coming test data,the most correlated segmentation was obtained from batch-wise unfolded training data by JITL.Then,ICA served as the principal components extraction approach.Therefore,the non-Gaussian distributed data can also be addressed under this modeling framework.The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.
文摘In the past decades, on-line monitoring of batch processes using multi-way independent component analysis (MICA) has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components, we introduce a novel concept of system deviation, which is able to evaluate the reconstructed observations with different independent components. The monitored statistics are transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-batch penicillin fermentation simulator, and the experimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.
基金Supported by the National Natural Science Foundation of China (60404012, 60674064), UK EPSRC (GR/N13319 and GR/R10875), the National High Technology Research and Development Program of China (2007AA04Z193), New Star of Science and Technology of Beijing City (2006A62), and IBM China Research Lab 2007 UR-Program.
基金Supported by the National Creative Research Groups Science Foundation of China (60721062) and the National High Technology Research and Development Program of China (2007AA04Z162).
基金Supported by the National Natural Science Foundation of China(61374044)Shanghai Science Technology Commission(15510722100,16111106300)Shanghai Municipal Education Commission(14ZZ088)
文摘Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.
基金Supported by UK EPSRC (grants GR/N13319 and GR/R 10875)
文摘This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
基金Supported by the National Natural Science Foundation of China (20676013)
文摘An iterative optimization strategy for fed-batch fermentation process is presented by combining a run-to-run optimization with swarm energy conservation particle swarm optimization (SEC-PSO). SEC-PSO, which is designed with the concept of energy conservation, can solve the problem of premature convergence frequently appeared in standard PSO algorithm by partitioning its population into several sub-swarms according to the energy of the swarm and is used in the optimization strategy for parameter identification and operation condition optimization. The run-to-run optimization exploits the repetitive nature of fed-batch processes in order to deal with the optimal problems of fed-batch fermentation process with inaccurate process model and unsteady process state. The kinetic model parameters, used in the operation condition optimization of the next run, are adjusted by calculating time-series data obtained from real fed-batch process in the run-to-run optimization. The simulation results show that the strategy can adjust its kinetic model dynamically and overcome the instability of fed-batch process effectively. Run-to-run strategy with SEC-PSO provides an effective method for optimization of fed-batch fermentation process.
文摘Batch processes are important in chemical industry,in which operators usually play a major role and hazards may arise by their inadvertent acts.In this paper,based on hazard and operability study and concept of qualitative simulation,an automatic method for adverse consequence identification for potential maloperation is proposed.The qualitative model for production process is expressed by a novel directed graph.Possible operation deviations from normal operating procedure are identified systematically by using a group of guidewords.The proposed algorithm is used for qualitative simulation of batch processes to identify the effects of maloperations.The method is illustrated with a simple batch process and a batch reaction process.The results show that batch processes can be simulated qualitatively and hazards can be identified for operating procedures including maloperations.After analysis for possible plant maloperations,some measures can be taken to avoid maloperations or reduce losses resulted from maloperations.
基金Supported in part by NSFC/RGC joint Research Scheme (N-HKUST639/09), the National Natural Science Foundation of China (61104058, 61273101), Guangzhou Scientific and Technological Project (2012J5100032), Nansha district independent innovation project (201103003), China Postdoctoral Science Foundation (2012M511367, 2012M511368), and Doctor Scientific Research Foundation of Liaoning Province (20121046).
文摘Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.
基金Project (No. 60774067) supported by the National Natural ScienceFoundation of China
文摘In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.
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.