In the traditional performance assessment method, different modes of data are classified mainly by expert knowledge. Thus, human interference is highly probable. The traditional method is also incapable of distinguishing transition data from steady-state data, which reduces the accuracy of the monitor model. To solve these problems, this paper proposes a method of multimode operating performance visualization and nonoptimal cause identification. First, multimode data identification is realized by subtractive clustering algorithm (SCA), which can reduce human influence and eliminate transition data. Then, the multi-space principal component analysis (MsPCA) is used to characterize the independent characteristics of different datasets, which enhances the robustness of the model with respect to the performance of independent variables. Furthermore, a self-organizing map (SOM) is used to train these characteristics and map them into a two-dimensional plane, by which the visualization of the process monitor is realized. For the online assessment, the operating performance of the current process is evaluated according to the projection position of the data on the visual model. Then, the cause of the nonoptimal performance is identified. Finally, the Tennessee Eastman (TE) process is used to verify the effectiveness of the proposed method.
The
high-fidelity model is not easy to analyze and optimize because
of the high computational cost. When the classical design of experiments
is applied to construct the metamodel to replace such a computationally
intensive model for analysis or optimization, it usually needs more
samples compared with hybrid adaptive sampling to ensure the reliability
of the metamodel due to ignoring the system information. In this study,
considering the general feature of the chemical model, a new method
of hybrid adaptive sampling named hybrid adaptive sampling algorithm
based on scores (HASAS) is proposed on the basis of k-nearest neighbor
for exploration and nearest neighbor expected improvement for exploitation
to enhance the global quality of the metamodel. Furthermore, a weight
coefficient is introduced to balance exploration and exploitation
during sample placement. Sixteen benchmark cases are utilized to evaluate
the performance of the HASAS and four other hybrid adaptive methods.
Results show that with the same number of samples, HASAS can perform
well in terms of global accuracy on most of them. The effect of the
number of initial sample points on HASAS is also discussed. Finally,
it is applied for the construction of the Kriging for the hydropurification
process of a typical chemical (terephthalic acid) and sensitivity
of each variable is done with the metamodel.
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