A Modeling and Prediction Method for 4-NP Reduction Catalysts Based on ECSA Gaussian Process Regression
A Gaussian process regression and prediction method technology, applied in the field of 4-NP reduction catalyst modeling and prediction, can solve the problems of cumbersome and inefficient experimental process, and achieve the effect of high convergence accuracy and efficient optimization performance.
Active Publication Date: 2020-07-21
SHANXI JIETAIDA COAL CHEM ENG CO LTD
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[0003] At this stage, the screening of catalysts is only obtained through human experience and blind experiments, and the experimental process is cumbersome and inefficient
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[0027] The object of the present invention is to provide a kind of 4-NP reduction catalyst modeling prediction method based on the Gaussian process regression of ECSA, this method can not only provide a kind of method of optimizing Gaussian process regression prediction model, and can utilize this prediction model to find out The functional relationship between elements and catalytic activity, and use this prediction model to accurately predict the element with the best catalytic performance among the elements.
[0028] A kind of 4-NP reduction catalyst modeling prediction method based on the Gaussian process regression of ECSA, comprises the following steps:
[0029] Step 1: Obtain the original data and form the data set of the p-nitrophenol reduction catalyst prediction model based on the enhanced crow search algorithm to optimize the Gaussian process regression. At present, there are 112 elements in the periodic table of elements, except for gaseous, radioactive, and toxic ...
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The invention discloses a p-nitrophenol reduction catalyst modeling prediction method based on ECSA Gaussian process regression, and the method comprises the following steps: adding a contraction surrounding mechanism and simulated annealing to a graffiti search algorithm for improvement, and obtaining an enhanced graffiti search algorithm; preprocessing the original input data through normalization, kernel PCA dimension reduction and mean shift clustering; establishing a p-nitrophenol reduction catalyst prediction model based on a Gaussian process regression model by utilizing the processed data; optimizing hyper-parameters of the prediction model by using an enhanced graffiti search algorithm; and screening out the optimal catalyst according to the optimized prediction model. The invention provides a novel enhanced graffiti search algorithm, and the algorithm is used for optimizing modeling prediction of the p-nitrophenol reduction catalyst of Gaussian process regression, so that themodel prediction precision and the catalyst screening efficiency are improved.
Description
technical field [0001] The invention relates to a Gaussian process regression-based 4-NP reduction catalyst modeling and prediction method based on ECSA, which belongs to the field of modeling and optimization of complex industrial processes. Background technique [0002] p-Nitrophenol (4-NP) is generally used as an intermediate of fine chemicals such as pesticides, medicines, and dyes, and is widely used in chemical production. However, 4-NP is also a highly toxic and difficult to be biodegradable organic matter, so once 4-NP enters the environment, it will cause great harm. One of the 129 key environmental pollutants. Therefore, it is of great significance to develop an efficient catalyst for the reduction of p-nitrophenol. [0003] At present, the screening of catalysts is only obtained through human experience and blind experiments, and the experimental process is cumbersome and inefficient. Contents of the invention [0004] The object of the present invention is t...
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IPC IPC(8): G06F30/20
Inventor 韩晓霞许全喜岳林董颖超
Owner SHANXI JIETAIDA COAL CHEM ENG CO LTD