基于集成学习的多产品化工过程软测量建模方法

化工过程通常具有非线性、时变以及多产品等特性。针对上述特点，在集成学习框架下建立自适应软测量模型。首先，面向具有多个产品的化工对象，借助k近邻法，以统计假设检验理论为依据，提出一种自适应局部化方法，获得多样性程度高的局部模型集合。然后，根据未知样本量化局部模型的泛化能力，通过选择性集成方法获得主导变量的估计值。此外，为了对主导变量估计值的精度进行评估，基于局部模型泛化误差，给出一种通用性高的模型性能评价方法。在仿真的盘尼西林生产过程上的运行结果验证了所提方法的有效性。

Ensemble learning-based soft sensor method for multi-product chemical processes

To handle characteristics of nonlinearity, time-variation and multi-product of chemical processes, a self-adaptive soft sensing method was developed under ensemble learning framework. Initially, a self-adaptive localization technique was proposed to construct ensemble of high diversified local models by statistical hypothesis testing theory and k-nearest neighbor method. Subsequently, based on generalization capabilities of quantified local models with online query sample, primary process variables were estimated through selective ensemble learning. Furthermore, in order to measure estimation accuracy of primary process variables, a highly universal method of model performance assessment was presented by using local model's generalization error. Simulation study on a penicillin fermentation process demonstrated effectiveness of the proposed method.