Process monitoring helps to estimate the quality of the end products, equipment health parameters, and operational reliability of chemical processes. This is an area in which data-driven approaches are widely used by academic and industrial practitioners. With the ever-increasing complexities in process industries, there is an increased thrust in developing the process monitoring methods of generic nature which are capable of handling the inherent nonlinear characteristics of the chemical process. This demanded the employment of complex data-driven model paradigms in the process monitoring framework. To circumvent the issues related to high-dimensional process data, a large body of these process monitoring algorithms extract only relevant features during the training. Further, model complexity is another important issue that needs to be accounted while employing these methods. In this work, an optimization-based features selection method for process monitoring is proposed, that simultaneously trades-off between the optimal feature selection and the resulting model complexity, by means of solving a multi-objective optimization problem. Particularly, this paper focuses on combining neural network architecture with recursive feature elimination and genetic algorithm to obtain an improved identification accuracy while reducing the number of variables to be measured continuously in the process plant. The efficacy of the proposed approach was validated using a basic numerical case and tested upon the operational data collected from the benchmark Tennessee Eastman plant data, and steel plates manufacturing case studies.

过程监控有助于估计最终产品的质量，设备健康参数以及化学过程的操作可靠性。在这一领域，数据驱动方法被学术和工业从业人员广泛使用。随着过程工业中日益复杂的问题，人们越来越需要开发通用的过程监控方法，该方法能够处理化学过程固有的非线性特征。这就要求在过程监控框架中采用复杂的数据驱动模型范式。为了规避与高维过程数据有关的问题，这些过程监视算法中的很大一部分在训练期间仅提取相关特征。进一步，模型复杂度是使用这些方法时需要考虑的另一个重要问题。在这项工作中，提出了一种用于过程监控的基于优化的特征选择方法，该方法通过解决多目标优化问题，在最佳特征选择和模型复杂度之间进行了权衡。特别是，本文着重于将神经网络架构与递归特征消除和遗传算法相结合，以提高识别精度，同时减少在过程工厂中要连续测量的变量数量。该方法的有效性通过一个基本的数值案例进行了验证，并根据从基准田纳西州伊士曼工厂数据和钢板制造案例研究中收集到的运营数据进行了测试。