School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2020 Mar 23;20(6):1771. doi: 10.3390/s20061771.
For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.
为了对发酵过程进行有效的监控和控制，需要对重要变量进行准确的实时测量。由于发酵过程的时变、非线性、强耦合和复杂机制等限制，这些变量很难实时测量。构建具有出色性能和鲁棒性的软传感器已成为工业过程中的核心问题。本文对现有的数据预处理方法、变量选择方法、数据驱动（黑盒）软测量建模方法和优化技术进行了全面综述。详细回顾了用于软测量建模的数据驱动方法，如支持向量机、多最小二乘支持向量机、神经网络、深度学习、模糊逻辑、概率潜在变量模型等。还讨论了用于模型参数估计的优化技术，如粒子群优化算法、蚁群优化算法、人工蜂群算法、布谷鸟搜索算法和遗传算法等。以表格形式对各种软测量模型进行了全面分析，突出了发酵领域中使用的重要方法。对 70 多篇关于变量估计的软测量建模方法的研究出版物进行了检查和列出，以供快速参考。本文综述可以作为一个有用的资源，为研究人员探索软测量建模领域进一步改进的机会提供参考点。
