由于探寻改性麦糟吸附三价砷离子As(Ⅲ)最适条件过程的复杂性,本文引入支持向量回归模型对该过程进行建模,以发现实验过程中各因素对去除率的影响,揭示该生物吸附剂对As(Ⅲ)吸附的最适条件和最佳效果。为了解决建模过程中数据采集不完整和可能存在的模型稳定性波动问题,引入了矩阵补全算法对受损数据进行补全。矩阵补全后的模型验证表明,该模型的预测结果与实际吸附率相比,具有偏差小、稳定性高、有效性和实用性,为改性麦糟吸附三价砷离子的进一步研究提供指导。 Due to the complexity of the process of determining the optimum condition for the adsorption of As (Ⅲ) on modified arable grains, a support vector regression model was introduced to model the process to find out the influence of various factors on the removal rate. Adsorption of As (Ⅲ) by Biosorbent and Its Optimal Conditions. In order to solve the problem of incomplete data collection and possible model stability fluctuation in the modeling process, a matrix complementation algorithm is introduced to complement the damaged data. The model validation after the matrix complementation shows that compared with the actual adsorption rate, the prediction results of the model have small deviations, high stability, validity and practicability, and provide guidance for the further study on the adsorption of trivalent arsenic ions in the modified grains. .