School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, PR China.
Prep Biochem Biotechnol. 2021;51(5):430-439. doi: 10.1080/10826068.2020.1827428. Epub 2020 Oct 5.
The vital state variables in marine alkaline protease (MP) fermentation are difficult to measure in real-time online, hardly is the optimal control either. In this article, a dynamic soft sensor modeling method which combined just-in-time learning (JITL) technique and ensemble learning is proposed. First, the local weighted partial least squares algorithm (LWPLS) with JITL strategy is used as the basic modeling method. For further improving the prediction accuracy, the moving window (MW) is used to divide sub-dataset. Then the MW-LWPLS sub-model is built by selecting the diverse sub-datasets according to the cumulative similarity. Finally, stacking ensemble-learning method is utilized to fuse each MW-LWPLS sub-models. The proposed method is applied to predict the vital state variables in the MP fermentation process. The experiments and simulations results show that the prediction accuracy is better compared to other methods.
在海洋碱性蛋白酶（MP）发酵中，关键状态变量难以实时在线测量，也很难进行优化控制。本文提出了一种结合即时学习（JITL）技术和集成学习的动态软测量建模方法。首先，采用具有 JITL 策略的局部加权偏最小二乘法（LWPLS）作为基本建模方法。为了进一步提高预测精度，使用移动窗口（MW）将子数据集划分。然后，根据累积相似度选择不同的子数据集来构建 MW-LWPLS 子模型。最后，利用堆叠集成学习方法融合每个 MW-LWPLS 子模型。将所提出的方法应用于预测 MP 发酵过程中的关键状态变量。实验和模拟结果表明，与其他方法相比，该方法的预测精度更好。
