Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2023 Jun 29;23(13):6014. doi: 10.3390/s23136014.
This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of . First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of . Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of under different operating conditions.
本文提出了一种基于 BDA-IPSO-LSSVM 的软测量建模新方法，旨在解决由于不同批次发酵过程中不同操作条件导致发酵数据分布变化而引起的模型失效问题。首先，通过采用迁移学习中的平衡分布适应（BDA）方法来解决发酵过程中不同批次数据分布差异较大的问题。该方法减少了发酵过程中不同批次的数据分布差异，同时利用模糊集概念，将分类问题转化为发酵过程的回归预测问题，改进了 BDA 方法。其次，采用最小二乘支持向量机（LSSVM）构建发酵过程的软测量模型。采用基于个体差异的改进粒子群优化（IPSO）算法对模型参数进行优化。最后，利用发酵实验获得的数据进行仿真，将开发的软测量模型应用于预测发酵过程中的细胞浓度和产物浓度。仿真结果表明，与其他算法相比，IPSO 算法具有良好的收敛性能和优化性能。改进的 BDA 算法可以使软测量模型适应不同的操作条件，所提出的软测量方法优于现有方法，具有更高的预测精度，能够准确预测不同操作条件下的发酵过程。
