The evaluation of input factors of complex system is a hot and difficult point in the sensitivity analysis. In this paper, the Garson algorithm based on artificial intelligence is studied and the original Garson algor...
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ISBN:
(纸本)9781509046584
The evaluation of input factors of complex system is a hot and difficult point in the sensitivity analysis. In this paper, the Garson algorithm based on artificial intelligence is studied and the original Garson algorithm accuracy is not high. Therefore, an improved Garson algorithm is proposed and the input factors are introduced into the Garson algorithm. At the same time, the original local sensitivity analysis algorithm is improved as the global sensitivity analysis algorithm and it increases the accuracy and stability of the Garson algorithm. Through the typical benchmark test function simulation, the experimental results show that the improved Garson algorithm has higher accuracy and stability in the evaluation of sensitivity coefficient. Finally, the improved Garson algorithm is applied to evaluate the input factors of the plate-fin heat exchangers. It shows that the IGarson algorithm is more feasibility and effectiveness.
Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to infor...
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Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be *** the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.
Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM...
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Complex industrial process often contains multiple operating modes, and the challenge of multimode process monitoring has recently gained much attention. However, most multivariate statistical process monitoring (MSPM) methods are based on the assumption that the process has only one nominal mode. When the process data contain different distributions, they may not function as well as in single mode processes. To address this issue, an improved partial least squares (IPLS) method was proposed for multimode process monitoring. By utilizing a novel local standardization strategy, the normal data in multiple modes could be centralized after being standardized and the fundamental assumption of partial least squares (PLS) could be valid again in multimode process. In this way, PLS method was extended to be suitable for not only single mode processes but also multimode processes. The efficiency of the proposed method was illustrated by comparing the monitoring results of PLS and IPLS in Tennessee Eastman(TE) process.
Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficie...
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Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.
In this paper, a distributed model predictive control (DMPC) scheme is presented to optimize the power flow management of microgrids in smart grid environment. For a multi-microgrids system in which local microgrid li...
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In order to improve the ozonation efficiency for the remediation of PAHs contaminated soil,the performance experiments were carried out with quartz sand artificially contaminated with *** byproducts of phenanthrene we...
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In order to improve the ozonation efficiency for the remediation of PAHs contaminated soil,the performance experiments were carried out with quartz sand artificially contaminated with *** byproducts of phenanthrene were detected by GC-MS and the toxicity was evaluated by seed germination *** influence of the particle size and moisture content of quartz sand on the ozonation efficiency was *** addition,two kinds of real soil was used to compare with the quartz *** was revealed that the phenanthrene removal rate reached 96%after 600 minutes by using the ozonation *** byproducts of phenanthrene,including 9,10-phenanthrenedione,(1,1’-biphenyl)-2,2’-dicarboxaldehyde,and(1,1’-biphenyl)-2,2’-dicarboxylic acid,were *** proven by seed germination tests,the toxicity of the byproducts was lower than *** phenanthrene was removed more effectively by ozonation in the quartz sand with finer particle *** ozonation efficiency was significantly improved by increasing the moisture content,which is assumed to be related to the alkalinity of quartz sand.
This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect a...
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This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and there input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed to solve these problems. More specifically, it is shown that robust semi-global leaderless consensus can be achieved over a connected undirected graph and robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.
Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyet...
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Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.
The simultaneous achievement of chiral multiple-resonance thermally activated delayed fluorescence(MR-TADF)emitters with narrowband and circularly polarized electroluminescence(CPEL)poses a ***,a MR-TADF emitter,Spiro...
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The simultaneous achievement of chiral multiple-resonance thermally activated delayed fluorescence(MR-TADF)emitters with narrowband and circularly polarized electroluminescence(CPEL)poses a ***,a MR-TADF emitter,Spiro-BNCz,embedding spirofluorene structure was developed and chiral separated,whose emission peaks at 528 nm in toluene with Commission Internationale de L’Eclairage coordinates of(0.26,0.69).The conjugation extension caused by embedding sulfur substituted spirofluorene on B/N framework shortens the singlettriplet energy gap and increases spin-orbital coupling matrix ***,a fast reverse intersystem crossing rate constant of 2.8×10^(6)s^(−1)and a high photoluminescence efficiency of 92%in film were *** organic light-emitting diode(OLED)exhibits a maximum external quantum efficiency of 32.3%.Particularly,the circularly polarized OLEDs based on Spiro-BNCz enantiomers show symmetric CPEL with dissymmetry factors(|gEL|)≈10^(−3).
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