YU Yu, WANG Yulian, ZHU Yibin, ZHANG Yifan, LI Keqing, GUAN Rui,SUN Haoran, HAN Huili, YUAN Zhigang

(College of Materials Science and Engineering, Shenyang Ligong University, Shenyang 110159, China)

Abstract

Objective In order to increase the aspect ratio of nesquehonite, a single factor test or orthogonal test is usually used to determine the optimal preparation process of nesquehonite. However, these two methods suffer from the problems of errors due to random effects, complicated experimental data, and large workloads. Optimize the preparation process of nesquehonite using BP neural network can effectively improve aspect ratio of nesquehonite and avoid the above problems.

Methods Nesquehonite were prepared by a simple process using magnesite as raw material and MgCl₂ as an additive. A combination of orthogonal test and BP neural network was used to optimize five conditions in the process, including reaction time, reaction temperature, stirring rate, pH, and MgCl₂ dosage. Firstly, an orthogonal test of L16(45) was designed to find out the optimal process conditions, while the influence of each condition on the aspect ratio of nesquehonite was judged by the range analysis.Then, a three-layer BP neural network model was designed with the number of nodes in the hidden layer determined by Eq. (1) and the loss function as MSE. Based on the optimal process conditions of orthogonal test, the model was used to predict the relationship between each condition and the variation of the aspect ratio of nesquehonite. Moreover, the optimal preparation process optimized by BP neural network model was obtained.

Results and Discussion The results of XRD and SEM images show that nesquehonite whisker with an aspect ratio of 20 could be obtained through orthogonal test under the optimized conditions of 80 min for reaction time, 40℃ for reaction temperature, 500 r/ min for stirring rate, 7±0. 1 for pH, and 0. 5 g / L for MgCl₂ dosage. The influence of each condition on the aspect ratio of nesquehonite can be obtained according to the range analysis , and the order is MgCl₂ dosage, reaction temperature, pH, reaction time, and stirring rate in descending. From the results of orthogonal tests, it can be seen that the aspect ratio of nesquehonite is significantly increased by the addition of MgCl₂ because Mg²⁺ accelerates the nucleation rate and the growth rate of nesquehonite. Samples with high pH have a small aspect ratio and a rough surface, which may be due to the fact that it will accelerate the dissolution of nesquehonite and the transformation to hydromagnesite with a large amount of OH^- entering the solution. When the temperature is too high, nesquehonite is easily converted to hydromagnesite, resulting in a decrease in the aspect ratio. Nesquehonite whisker with an aspect ratio of 25 could be obtained by BP neural network with the optimized conditions of 86 min for reaction time, 44 ℃ for reaction temperature, 760 r/ min for stirring rate, 7 for pH, and 1. 6 g / L for MgCl₂ dosage. The crystal aspect ratio shows a tendency to increase and then decrease with the extension of reaction time. With the rise of reaction temperature, the whisker aspect ratio shows a tendency to increase and then decrease. It can be seen the stirring rate increases, that the aspect ratio of nesquehonite shows an increasing trend, and it is close to 800 r/ min, that the aspect ratio shows a weak decreasing trend. With the increase of pH, the aspect ratio of nesquehonite decreased instead. The aspect ratio of nesquehonite whisker shows a trend of enlargement and then decreased with the increase of MgCl₂ dosage.

Conclusion In this work, a BP neural network model for the relationship between the preparation process and whisker aspect ratio of nesquehonite based on orthogonal test data is established, and the error between the experimental value and the predicted value is less than 7. 2%, which indicates that the neural network is relatively accurate. On the basis of orthogonal test, the preparation process of nesquehonite is further optimized by using BP neural network, that the aspect ratio is increased by 25% and the workload is reduced. It indicates that it is feasible to optimize the preparation process of nesquehonite by using BP neural network.

Keywords: magnesite; back-propagation neural network; nesquehonite whisker

Get Citation:YU Y, WANG Y L, ZHU Y B, et al. Optimization of nesquehonite whisker preparation process based on BP neural network[J].China Powder Science and Technology,2024, 30(1): 103-113