The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and redu...The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and reduce forming loads.However,the absence of compatible forming equipment makes it difficult to control the constraint in the unloaded zones during the forming process.This difficulty complicates coordination and control of deformation,particularly for asymmetric rib-web components.Additionally,the current implementation involves multi-fire heating,a long process flow,and high energy consumption,which limits the popularization and application of the local loading process.In this study,a new multi-pass local loading hydraulic forming apparatus that can quickly and reliably switch between heavy-load deformation and low-load constraint for different local loading sub-dies was developed.A 10-tonne laboratory prototype was developed,and the forming characteristics during the forming process as well as the response characteristics of the hydraulic system during the multi-pass intermittent local loading of rib-web component were investigated using numerical simulations and physical experiments.Results indicated that,compared to a whole loading process with the same initial geometry of billet,the total forming load(i.e.,the sum of loaded and restrained loads)is reduced by more than 40%with the local loading process,and by nearly 50%with multi-pass local loading.The multi-pass local loading process allows for more effective control of material flow compared to single-pass local loading,leading to improved cavity filling and reduced flow line disturbance.For a large-scale,complex titanium alloy bulkhead,the cavity filling problem was addressed by optimizing the multi-pass local loading path with an unequal thickness billet.The dynamic performance of the multi-pass local loading hydraulic system was found to be robust,with stable pressure transitions during motion and load switching for the sub-die(s).The dynamic characteristic of the hydraulic cylinder when switching from non-moving/unloaded state to a moving/loading state are consistent whether a load is present or not.However,the dynamic characteristics differ when switching from a moving/loading state to non-moving/unloaded state,showing opposite behavior.The developed hydraulic drive mechanism provides a way for implementation of multi-pass local loading without auxiliary operation and extra heating.The results of the study provide a foundation for the industrial production of large-scale,complex components with reduced force requirement and low-energy consumption.展开更多
Qingke(highland hull-less barley)is a grain replete with substantial nutrients and bioactive ingredients.In this study,we evaluated the effects of boiling(BO),steaming(ST),microwave baking(MB),far-infrared baking(FB),...Qingke(highland hull-less barley)is a grain replete with substantial nutrients and bioactive ingredients.In this study,we evaluated the effects of boiling(BO),steaming(ST),microwave baking(MB),far-infrared baking(FB),steam explosion(SE),and deep frying(DF)on bioactive components,phenolic compounds,and antioxidant activities of Qingke compared with the effects of traditional roast(TR).Results showed that the soluble dietary fiber,beta-glucan and water-extractable pentosans of Qingke in dry heat processes of TR,SE,MB and FB had a higher content compared with other thermal methods and had a better antioxidant activity of hydroxyl radical scavenging and a better reduction capacity,while those in wet heat processes of BO and ST had a better antioxidant activity of ABTS radical scavenging and a better Fe^(2+) chelating ability.DF-and SE-Qingke had a higher content of tocopherol,phenolic,and flavonoid.Overall,6 free phenolic compounds and 12 bound phenolic compounds of Qingke were identified,and free phenolic compounds suffered more damage during thermal processing.Principal component analysis showed that SE had more advantages in retaining and improving the main biological active ingredients of Qingke,and it may be the best method for treating Qingke.展开更多
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th...With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.展开更多
The aim of this research was to explore the effect of fixation,rolling,and drying processing technology on the retention rate of green tea catechins components and tea polyphenol.Different fixation processes(rotary dr...The aim of this research was to explore the effect of fixation,rolling,and drying processing technology on the retention rate of green tea catechins components and tea polyphenol.Different fixation processes(rotary drumfiring,microwave,steam-blasting),rolling process(weight of rolling,gently press rolling and traditional rolling),drying process(stove drying,roasting dehydration,baked fried drying) were adopted.The effect of different tea processing technology on the retention rate of catechins component and tea polyphenol was analyzed.It showed that the microwave fixation process,gently press rolling process,baked fried dry process were beneficial to keep high levels of EGCG,C,EGC,EC,ECG.展开更多
Logistics network design influences the efficiency and cost of Logistics directly.Some manufacturing enterprises not only have warehouse hubs,but also build component processing workshops which are usually located in ...Logistics network design influences the efficiency and cost of Logistics directly.Some manufacturing enterprises not only have warehouse hubs,but also build component processing workshops which are usually located in those places where the costs of materials and workforce are lower.This paper establishes a logistics network design model for the manufacturing enterprises with component processing workshops based on 0-1 mixture integer programming.The model optimizes the logistics network in an integrated view,by which the selection of the nodes,the manufacturing plan,and transportation plan can be obtained.An example is given to verify its feasibility.The approach is helpful for designing of the logistics network in manufacturing enterprises.展开更多
[Objectives]To explore the correlation of processing technology,physical parameters and chemical components during plain stir-baking of Trichosanthis Radix.[Methods]Based on mixture uniform experiment design,the Trich...[Objectives]To explore the correlation of processing technology,physical parameters and chemical components during plain stir-baking of Trichosanthis Radix.[Methods]Based on mixture uniform experiment design,the Trichosanthis Radix was prepared by plain stir-bake method.Delphi method was used to evaluate and select the highest-scoring processed product for measuring physical parameters.UV spectrophotometry was used to determine the contents of starch and polysaccharide.The correlation and linear regression model of processing technology,physical parameters and chemical components were established with the aid of SPSS 26.0[Results]After processing by plain stir-bake method,the relative density and chromaticity showed a decreasing trend in the processed products of Trichosanthis Radix,the oxidation value,hydroscopic rate and swelling decreased firstly and then increased,and pH increased firstly and then decreased.The content of total starch decreased,the content of polysaccharide increased,and there was a negative correlation between them.There was a significant positive correlation between temperature and oxidation value,swelling and hydroscopic rate,hydroscopic rate and polysaccharide,and there was a significant negative correlation between relative density and hydroscopic rate or polysaccharide,total starch and hydroscopic rate or swelling.The linear relation model between processing technology and physical parameters and chemical components was r2>0.9.[Conclusions]After processing by plain stir-bake method,the physical parameters of Trichosanthis Radix changed,and there may be mutual conversion between total starch and polysaccharides.To a certain extent,physical parameters can be used to evaluate the quality of processed products of Trichosanthis Radix.This study is expected to provide a reference for research on quality evaluation of processed products of traditional Chinese medicine.展开更多
Objective:To establish a gas chromatography-mass spectrometry(GC-MS)method for the determination of volatile components in different prepared products of Alisma purpurea.Methods:The volatile components were determined...Objective:To establish a gas chromatography-mass spectrometry(GC-MS)method for the determination of volatile components in different prepared products of Alisma purpurea.Methods:The volatile components were determined by GC-MS,and the types and relative contents of volatile components were compared in 4 kinds of processed products.Result:The main volatile components of 30 kinds of different products from Zelica were analyzed and identified,which were mainly sesquiterpenes,ketones and alcohols,mainly Pogostol,olive,guaiacene,caryophyllene and so on.Conclusion:There are differences in the types and relative contents of volatile components in different products of Alisma platyphylla,in order to provide a basis for improving the quality standards of different products of Alisma platyphylla.展开更多
The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.展开更多
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), usi...Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.展开更多
In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to thei...In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.展开更多
For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component b...For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.展开更多
Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable me...Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.展开更多
This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzh...This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.展开更多
Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. Ho...Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.展开更多
The current study was to understand how process variables of high shear wet granulations affect physical properties of granules and tablets. The knowledge gained was intended to be used for Quality-by-Design based pro...The current study was to understand how process variables of high shear wet granulations affect physical properties of granules and tablets. The knowledge gained was intended to be used for Quality-by-Design based process design and optimization. The variables were selected based on the risk assessment as impeller speed, liquid addition rate, and wet massing time. Formulation compositions were kept constant to minimize their influence on granules properties. Multiple linear regression models were built providing understanding of the impact of each variable on granule hardness, Carr’s index, tablet tensile strength, surface mean diameter of granules, and compression behavior. The experimental results showed that the impact of impeller speed was more dominant compared to wet massing time and water addition rate. The results also revealed that quality of granules and tablets could be optimized by adjusting specific process variables(impeller speed 1193 rpm, water spray rate 3.7 ml/min, and wet massing time 2.84 min). Overall desirability was 0.84 suggesting that the response values were closer to the target one. The SEM image of granules showed that spherical and smooth granules produced at higher impeller speed, whereas rough and irregular shape granules at lower speed. Moreover, multivariate data analysis demonstrated that impeller speed and massing time had strong correlation with the granule and tablet properties. In overall, the combined experimental design and principal component analysis approach allowed to better understand the correlation between process variables and granules and tablet attributes.展开更多
In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different ...In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.展开更多
In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from ...In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.展开更多
Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the advers...Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.展开更多
基金the supports of the National Natural Science Foundation of China(Grant No.52375378)。
文摘The multi-pass intermittent local loading process,which features a more flexible processing path,can further enhance the second material distribution during local loading,improve the formability of components,and reduce forming loads.However,the absence of compatible forming equipment makes it difficult to control the constraint in the unloaded zones during the forming process.This difficulty complicates coordination and control of deformation,particularly for asymmetric rib-web components.Additionally,the current implementation involves multi-fire heating,a long process flow,and high energy consumption,which limits the popularization and application of the local loading process.In this study,a new multi-pass local loading hydraulic forming apparatus that can quickly and reliably switch between heavy-load deformation and low-load constraint for different local loading sub-dies was developed.A 10-tonne laboratory prototype was developed,and the forming characteristics during the forming process as well as the response characteristics of the hydraulic system during the multi-pass intermittent local loading of rib-web component were investigated using numerical simulations and physical experiments.Results indicated that,compared to a whole loading process with the same initial geometry of billet,the total forming load(i.e.,the sum of loaded and restrained loads)is reduced by more than 40%with the local loading process,and by nearly 50%with multi-pass local loading.The multi-pass local loading process allows for more effective control of material flow compared to single-pass local loading,leading to improved cavity filling and reduced flow line disturbance.For a large-scale,complex titanium alloy bulkhead,the cavity filling problem was addressed by optimizing the multi-pass local loading path with an unequal thickness billet.The dynamic performance of the multi-pass local loading hydraulic system was found to be robust,with stable pressure transitions during motion and load switching for the sub-die(s).The dynamic characteristic of the hydraulic cylinder when switching from non-moving/unloaded state to a moving/loading state are consistent whether a load is present or not.However,the dynamic characteristics differ when switching from a moving/loading state to non-moving/unloaded state,showing opposite behavior.The developed hydraulic drive mechanism provides a way for implementation of multi-pass local loading without auxiliary operation and extra heating.The results of the study provide a foundation for the industrial production of large-scale,complex components with reduced force requirement and low-energy consumption.
基金financially supported by the 2018 annual three gorges follow-up research project of the three gorges office of the State Council (YYNY-2017-01)
文摘Qingke(highland hull-less barley)is a grain replete with substantial nutrients and bioactive ingredients.In this study,we evaluated the effects of boiling(BO),steaming(ST),microwave baking(MB),far-infrared baking(FB),steam explosion(SE),and deep frying(DF)on bioactive components,phenolic compounds,and antioxidant activities of Qingke compared with the effects of traditional roast(TR).Results showed that the soluble dietary fiber,beta-glucan and water-extractable pentosans of Qingke in dry heat processes of TR,SE,MB and FB had a higher content compared with other thermal methods and had a better antioxidant activity of hydroxyl radical scavenging and a better reduction capacity,while those in wet heat processes of BO and ST had a better antioxidant activity of ABTS radical scavenging and a better Fe^(2+) chelating ability.DF-and SE-Qingke had a higher content of tocopherol,phenolic,and flavonoid.Overall,6 free phenolic compounds and 12 bound phenolic compounds of Qingke were identified,and free phenolic compounds suffered more damage during thermal processing.Principal component analysis showed that SE had more advantages in retaining and improving the main biological active ingredients of Qingke,and it may be the best method for treating Qingke.
文摘With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.
文摘The aim of this research was to explore the effect of fixation,rolling,and drying processing technology on the retention rate of green tea catechins components and tea polyphenol.Different fixation processes(rotary drumfiring,microwave,steam-blasting),rolling process(weight of rolling,gently press rolling and traditional rolling),drying process(stove drying,roasting dehydration,baked fried drying) were adopted.The effect of different tea processing technology on the retention rate of catechins component and tea polyphenol was analyzed.It showed that the microwave fixation process,gently press rolling process,baked fried dry process were beneficial to keep high levels of EGCG,C,EGC,EC,ECG.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2007AA04Z105)the Innovation Action Project from Science and Technology Commission of Shanghai Municipality(08170511300)
文摘Logistics network design influences the efficiency and cost of Logistics directly.Some manufacturing enterprises not only have warehouse hubs,but also build component processing workshops which are usually located in those places where the costs of materials and workforce are lower.This paper establishes a logistics network design model for the manufacturing enterprises with component processing workshops based on 0-1 mixture integer programming.The model optimizes the logistics network in an integrated view,by which the selection of the nodes,the manufacturing plan,and transportation plan can be obtained.An example is given to verify its feasibility.The approach is helpful for designing of the logistics network in manufacturing enterprises.
基金Science and Technology Research and Development Project of Chengde City,Hebei Province(201706A043)Young Scholar Program of Hebei Pharmaceutical Association Hospital Pharmaceutical Research Project(2020—Hbsyxhqn0029)Public Health Service Subsidy Fund Project of Chinese Medicine Department,State Administration of Traditional Chinese Medicine(Guo Zhong Yi Gui Cai Fa[2015]No.21).
文摘[Objectives]To explore the correlation of processing technology,physical parameters and chemical components during plain stir-baking of Trichosanthis Radix.[Methods]Based on mixture uniform experiment design,the Trichosanthis Radix was prepared by plain stir-bake method.Delphi method was used to evaluate and select the highest-scoring processed product for measuring physical parameters.UV spectrophotometry was used to determine the contents of starch and polysaccharide.The correlation and linear regression model of processing technology,physical parameters and chemical components were established with the aid of SPSS 26.0[Results]After processing by plain stir-bake method,the relative density and chromaticity showed a decreasing trend in the processed products of Trichosanthis Radix,the oxidation value,hydroscopic rate and swelling decreased firstly and then increased,and pH increased firstly and then decreased.The content of total starch decreased,the content of polysaccharide increased,and there was a negative correlation between them.There was a significant positive correlation between temperature and oxidation value,swelling and hydroscopic rate,hydroscopic rate and polysaccharide,and there was a significant negative correlation between relative density and hydroscopic rate or polysaccharide,total starch and hydroscopic rate or swelling.The linear relation model between processing technology and physical parameters and chemical components was r2>0.9.[Conclusions]After processing by plain stir-bake method,the physical parameters of Trichosanthis Radix changed,and there may be mutual conversion between total starch and polysaccharides.To a certain extent,physical parameters can be used to evaluate the quality of processed products of Trichosanthis Radix.This study is expected to provide a reference for research on quality evaluation of processed products of traditional Chinese medicine.
文摘Objective:To establish a gas chromatography-mass spectrometry(GC-MS)method for the determination of volatile components in different prepared products of Alisma purpurea.Methods:The volatile components were determined by GC-MS,and the types and relative contents of volatile components were compared in 4 kinds of processed products.Result:The main volatile components of 30 kinds of different products from Zelica were analyzed and identified,which were mainly sesquiterpenes,ketones and alcohols,mainly Pogostol,olive,guaiacene,caryophyllene and so on.Conclusion:There are differences in the types and relative contents of volatile components in different products of Alisma platyphylla,in order to provide a basis for improving the quality standards of different products of Alisma platyphylla.
基金Supported by the 973 project of China （2013CB733600）, the National Natural Science Foundation （21176073）, the Doctoral Fund of Ministry of Education （20090074110005）, the New Century Excellent Talents in University （NCET-09-0346）, ＂Shu Guang＂ project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金Supported by the National High-tech Program of China (No. 2001 AA413110).
文摘Multi-way principal component analysis (MPCA) had been successfully applied to monitoring the batch and semi-batch process in most chemical industry. An improved MPCA approach, step-by-step adaptive MPCA (SAMPCA), using the process variable trajectories to monitoring the batch process is presented in this paper. It does not need to estimate or fill in the unknown part of the process variable trajectory deviation from the current time until the end. The approach is based on a MPCA method that processes the data in a sequential and adaptive manner. The adaptive rate is easily controlled through a forgetting factor that controls the weight of past data in a summation. This algorithm is used to evaluate the industrial streptomycin fermentation process data and is compared with the traditional MPCA. The results show that the method is more advantageous than MPCA, especially when monitoring multi-stage batch process where the latent vector structure can change at several points during the batch.
基金supported by the National Basic Research Program of China (973 Program) (No. 2013CB733600)the National Natural Science Foundation of China (No. 21176073)+1 种基金the Program for New Century Excellent Talents in University (No. NCET-09-0346)the Fundamental Research Funds for the Central Universities, China
文摘In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.
基金National Natural Science Foundation of China(61673279)。
文摘For plant-wide processes with multiple operating conditions,the multimode feature imposes some challenges to conventional monitoring techniques.Hence,to solve this problem,this paper provides a novel local component based principal component analysis(LCPCA)approach for monitoring the status of a multimode process.In LCPCA,the process prior knowledge of mode division is not required and it purely based on the process data.Firstly,LCPCA divides the processes data into multiple local components using finite Gaussian mixture model mixture(FGMM).Then,calculating the posterior probability is applied to determine each sample belonging to which local component.After that,the local component information(such as mean and standard deviation)is used to standardize each sample of local component.Finally,the standardized samples of each local component are combined to train PCA monitoring model.Based on the PCA monitoring model,two monitoring statistics T^(2) and SPE are used for monitoring multimode processes.Through a numerical example and the Tennessee Eastman(TE)process,the monitoring result demonstrates that LCPCA outperformed conventional PCA and LNS-PCA in the fault detection rate.
基金Supported by the Guangzhou Scientific and Technological Project (2012J5100032)Nansha District Independent Innovation Project (201103003)
文摘Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.
文摘This study examined public attitudes concerning the value of outdoor spaces which people use daily. Two successive analyses were performed based on data from common residents and college students in the city of Hangzhou, China. First, citizens registered various items constituting desirable values of residential outdoor spaces through a preliminary questionnaire. The result proposed three general attributes (functional, aesthetic and ecological) and ten specific qualities of residential outdoor spaces. An analytic hierarchy process (AHP) was applied to an interview survey in order to clarify the weights among these attributes and qualities. Second, principal factors were extracted from the ten specific qualities with principal component analysis (PCA) for both the common case and the campus case. In addition, the variations of respondents’ groups were classified with cluster analysis (CA) using the results of the PCA. The results of the AHP application found that the public prefers the functional attribute, rather than the aesthetic attribute. The latter is always viewed as the core value of open spaces in the eyes of architects and designers. Fur-thermore, comparisons of ten specific qualities showed that the public prefers the open spaces that can be utilized conveniently and easily for group activities, because such spaces keep an active lifestyle of neighborhood communication, which is also seen to protect human-regarding residential environments. Moreover, different groups of respondents diverge largely in terms of gender, age, behavior and preference.
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province of China(ZR2011FM014)+1 种基金the Doctoral Fund of Shandong Province(BS2012ZZ011)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.
基金supported by the National Research Foun-dation of Korea(NRF)grant,funded by the Korean govern-ment(MSIT)(2015R1A1A1A05000942)the National Re-search Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2018R1A5A2023127)
文摘The current study was to understand how process variables of high shear wet granulations affect physical properties of granules and tablets. The knowledge gained was intended to be used for Quality-by-Design based process design and optimization. The variables were selected based on the risk assessment as impeller speed, liquid addition rate, and wet massing time. Formulation compositions were kept constant to minimize their influence on granules properties. Multiple linear regression models were built providing understanding of the impact of each variable on granule hardness, Carr’s index, tablet tensile strength, surface mean diameter of granules, and compression behavior. The experimental results showed that the impact of impeller speed was more dominant compared to wet massing time and water addition rate. The results also revealed that quality of granules and tablets could be optimized by adjusting specific process variables(impeller speed 1193 rpm, water spray rate 3.7 ml/min, and wet massing time 2.84 min). Overall desirability was 0.84 suggesting that the response values were closer to the target one. The SEM image of granules showed that spherical and smooth granules produced at higher impeller speed, whereas rough and irregular shape granules at lower speed. Moreover, multivariate data analysis demonstrated that impeller speed and massing time had strong correlation with the granule and tablet properties. In overall, the combined experimental design and principal component analysis approach allowed to better understand the correlation between process variables and granules and tablet attributes.
基金supported by Zhejiang Provincial Natural Science Foundation of China(LY19F030003)Key Research and Development Project of Zhejiang Province(2021C04030)+1 种基金the National Natural Science Foundation of China(62003306)Educational Commission Research Program of Zhejiang Province(Y202044842)。
文摘In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.
基金Shanghai Leading Academic Discipline Project,China(No.B504) Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,China
文摘In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(12CX06071A)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Conventional process monitoring method based on fast independent component analysis(Fast ICA) cannot take the ubiquitous measurement noises into account and may exhibit degraded monitoring performance under the adverse effects of the measurement noises. In this paper, a new process monitoring approach based on noisy time structure ICA(Noisy TSICA) is proposed to solve such problem. A Noisy TSICA algorithm which can consider the measurement noises explicitly is firstly developed to estimate the mixing matrix and extract the independent components(ICs). Subsequently, a monitoring statistic is built to detect process faults on the basis of the recursive kurtosis estimations of the dominant ICs. Lastly, a contribution plot for the monitoring statistic is constructed to identify the fault variables based on the sensitivity analysis. Simulation studies on the continuous stirred tank reactor system demonstrate that the proposed Noisy TSICA-based monitoring method outperforms the conventional Fast ICA-based monitoring method.
