基于自适应采样算法的芳烃异构化代理模型

异构化是芳烃生产中的重要环节，提高异构化环节的建模和优化效率对工业生产有着重要意义。但是，直接使用机理模型的优化过程耗时较长，优化效率低。代理模型可以有效地对机理模型进行近似，而代理模型采样方法对模型精度有很大影响。提出了一种新的基于稀疏度和最邻近期望的自适应采样算法，该方法可以平衡全局搜索和局部搜索，通过求解优化问题找到反映函数关键信息的新采样点，再加入原始样本集中，使得代理模型精度不断提高。多个测试函数结果表明，相比于其他自适应采样算法，该算法能有效提升代理模型精度和建模效率。该算法在芳烃异构化环节代理模型中也得到了有效验证，与本文中其他算法对比，该算法模型误差减少5%以上，建模时间缩短30%以上。

Surrogate model of aromatic isomerization process based on adaptive sampling algorithm

Isomerization is an important part in the production of aromatics. Improving the modeling and optimization efficiency of isomerization is of great significance to industrial production. However, the optimization process using the mechanism model directly takes a long time and the optimization efficiency is low. The surrogate model can effectively approximate the mechanism model and greatly reduce the computation time. The surrogate model sampling method has a great influence on the accuracy of the model. A new adaptive sampling algorithm based on sparsity and nearest neighbor expectation is proposed in this paper, which can balance global search and local search. By solving the optimization problem, we can find new sampling points that reflect the key information of the object function, and then add the new point to the sample set, so that the accuracy of the surrogate model is continuously improved. The results of multiple test functions show that compared with other adaptive sampling algorithms, the algorithm can effectively improve the accuracy and modeling efficiency of the surrogate model. The algorithm is also validated in the aromatic isomerization surrogate model. Compared with other algorithms, our algorithm model error is reduced by more than 5%, and the modeling time is reduced by more than 30%.