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| 改进的灰色模型在光催化数据预测中的应用 | |
| 引用本文： | 王路平,卢占会,魏鑫,方明,王祥科.改进的灰色模型在光催化数据预测中的应用[J].无机材料学报,2021,36(8):871-876. |
| 作者姓名： | 王路平 卢占会 魏鑫 方明 王祥科 |
| 作者单位： | 1. 数理学院, 华北电力大学, 资源环境系统优化教育部重点实验室, 北京 102206 2.控制与计算机工程学院, 华北电力大学, 资源环境系统优化教育部重点实验室, 北京 102206 3.环境科学与工程学院, 华北电力大学, 资源环境系统优化教育部重点实验室, 北京 102206 |
| 基金项目： | 科技部重大研发计划(2018YFC1900105,);科技部重大研发计划(2017YFA0207002);中央高校基本科研业务费专项资金(2019MS040) |
| 摘 要： | 光催化去除水中污染物的研究通常得到的是小样本的离散数据,利用拟一级动力学模型对实验结果进行模拟和分析,有时拟合效果较差,且无法用于数据预测.本研究在离散灰色预测模型(DGM(1,1))的基础上,考虑数据的非线性特征并结合等维信息替代思想建立了非线性动态离散灰色模型(EDGM(1,1,α)),利用该模型对三元复合材料Bi... |
| 关 键 词： | 光催化 预测 灰色模型 等维信息替代 |
| 收稿时间： | 2020-11-19 |
| 修稿时间： | 2021-02-04 |
Application of Improved Grey Model in Photocatalytic Data Prediction |
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| WANG Luping,LU Zhanhui,WEI Xin,FANG Ming,WANG Xiangke.Application of Improved Grey Model in Photocatalytic Data Prediction[J].Journal of Inorganic Materials,2021,36(8):871-876. | |
| Authors: | WANG Luping LU Zhanhui WEI Xin FANG Ming WANG Xiangke |
| Affiliation: | 1. School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China 2. College of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China 3. MOE Key Laboratory of Resources and Environmental System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China |
| Abstract: | Discrete and small samples data are usually obtained from the studies on photocatalytic removal of pollutants in water. The first-order kinetic model used to simulate and analyze the experimental data sometimes has poor fitting effect and cannot be used for data prediction. In this study, based on the discrete grey model (DGM(1, 1)), the nonlinear dynamic discrete gray model (EDGM(1, 1, α)) are established by considering the nonlinear characteristics of the data and equal dimensional information substitution method. The model is used to predict the experimental data of photocatalytic degradation of tetracycline by Bi/BiOCl/Au. As compared with DGM(1, 1) and other three models, the EDGM(1, 1, α) model has a better prediction level for the experimental data of photocatalysis. The results are in good agreement with the experimental results. This model can be used to guide the next step of experiments, which is expected to reduce the number of experiments and realize the rapid development of experimental research with low cost and energy consumption. |
| Keywords: | photocatalysis predict grey model equivalent substitution method |
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