A novel mathematical model for single particle slurry propylene polymerization rising heterogeneous Ziegler-Natta catalysts has been developed to describe the kinetic behavior, the molecular weight-distribution, the m...
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A novel mathematical model for single particle slurry propylene polymerization rising heterogeneous Ziegler-Natta catalysts has been developed to describe the kinetic behavior, the molecular weight-distribution, the monomer concentration, the degree of polymerization, the polydispersity index （PDI）, etc. This model provides a more valid mathematical description by accounting for the monomer diffusion phenomena at two levels as multigrai model counts, and obtains results that are more applicable to the conditions existing in most polymerizations of industrial interest. Considering that some models on the mesoscale phenomena are so complex that some existingmodeling aspects have to be simplified or even neglected to make the model convenient for use in interesting engineering studies, it is very important to put some effort into determining what sort of numerical analysis works bestfor these problems. For this reason, special *** to these studies to explorean efficient algorithm usingadaptive grid-point spacing in a tlnlte-ditterence technique to tlgure out more practical mass transport models andconvection-diffusion models efficiently. The reasonable outcomes, as well as the significant computation time saving, have been achieved, thereby displaying the advantage of this calculation method.
Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a...
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Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.
It is difficult to directly co‐register the 3D FMT (Fluorescence Molecular Tomography) image of a small tumor in a mouse whose maximal diameter is only a few mm with a larger CT image of the entire animal that spans ...
It is difficult to directly co‐register the 3D FMT (Fluorescence Molecular Tomography) image of a small tumor in a mouse whose maximal diameter is only a few mm with a larger CT image of the entire animal that spans about ten cm. This paper proposes a new method to register 2D flat and 3D CT image first to facilitate the registration between small 3D FMT images and large CT images. A novel algorithm based on SMC (Sequential Monte Carlo) incorporated with least square operation for the registration between the 2D flat and 3D CT images is introduced and validated with simulated images and real images of mice. The visualization of the preliminary alignment of the 3D FMT and CT image through 2D registration shows promising results.
FTU (Feeder Terminal Unit) is an important device of distribution automation system, always working in bad environment. Its power dissipation and related thermal issues affect the lifetime of circuit component. When b...
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Automatic differentiation is the approach of differentiation without truncation error introduced. In this paper, two ways of automatic differentiation based on source transformation and operator overloading are invest...
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Automatic differentiation is the approach of differentiation without truncation error introduced. In this paper, two ways of automatic differentiation based on source transformation and operator overloading are investigated and applied to the optimization of distillation column. After comparing with the traditional finite difference method, the result proves the automatic differentiation approach based on source transformation the highest efficient in the optimization
In process systems there are many large-scale problems with large numbers of equality constraints, but their degrees of freedom are not small enough. To solve this kind of problems, an extended RSQP (reduced space seq...
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In process systems there are many large-scale problems with large numbers of equality constraints, but their degrees of freedom are not small enough. To solve this kind of problems, an extended RSQP (reduced space sequential quadratic programming) algorithm was presented. In the extended RSQP, reduced space Hessian and the cross item were expressed and computed by limited memory method, so the memory needed to store these matrixes were reduced largely, Moreover, to avoid the storage of matrix C - 1 N, zero space coordinate was expressed implicitly, and rules for basis selection were replaced by a heuristic basis selection strategy. Performance of the new algorithm was test by several variable large-scale problems and two real cases. Computational results demonstrate that the new algorithm can largely reduce computing time and storage required
In order to overcome the high dimension and collinearity problem of spectrum data in spectroscopy quantitative analysis, we introduce a partial least squares support vector machines (PLS-SVM) method, which integrates ...
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In order to overcome the high dimension and collinearity problem of spectrum data in spectroscopy quantitative analysis, we introduce a partial least squares support vector machines (PLS-SVM) method, which integrates partial least squares (PLS) and support vector machines (SVM). The method uses PLS to extract the feature of spectrum. Then the feature serves as the input of SVM calibration model instead of the whole spectrum. Experimental results show that PLS-SVM is superior to PLS in terms of prediction precision, while the modeling time is greatly reduced compared with SVM without feature extraction
This paper proposes a development method to deal with the stability problems for a kind of complex large-scale systems with hybrid models. These hybrid large-scale systems are composed of considerable interconnected n...
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This paper proposes a development method to deal with the stability problems for a kind of complex large-scale systems with hybrid models. These hybrid large-scale systems are composed of considerable interconnected nonlinear subsystems, some of which are described by differential equations and others by Takagi-Sugeno fuzzy models. Novel techniques to cope with the nonlinear interconnection between subsystems are developed. A set of LMI-based conditions are derived to judge the stability of the whole system by checking the stability of the subsystems in parallel, which greatly speeds up the stability analysis process. And the computational complexity is greatly reduced. A numerical example is given to illustrate its effectiveness
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