Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network

Yufeng Dong,Yingping Zhuang,Xuefeng Yan,Paolo Spagnolo

doi:10.1155/2021/6842835

Abstract

For batch processes that are extensively applied in modern industry and characterized by nonlinearity and dynamics, quality prediction is significant to obtain high-quality products and maintain production safety. However, some quality variables and key performance indicators are difficult to measure online. In addition, the mechanism-based model for batch processes is usually tough to acquire due to the strong nonlinearity and dynamics, which makes quality prediction a challenge. With the accumulation of historical process data, data-driven methods for quality prediction gain increasing attention, among which convolutional neural network (CNN) is quite successful for its automatic feature extraction of nonlinear features from raw data. Considering that most CNN-based methods mainly take the variety of extracted features into account and ignore the redundancy between them, this paper introduces the minimal-redundancy-maximal-relevance algorithm to select features obtained by original CNN and further improves it with a feature selection layer to form the proposed method referred as mRMR-CNN. Then, a quality prediction model is established based on mRMR-CNN and the effectiveness of it is verified on the penicillin fermentation process, where the proposed method shows remarkable performance.

Highlights

In modern industry, batch processes are extensively applied to the production of high-value products in many areas such as pharmaceutical, biotechnology, and polymer and semiconductor manufacturing industries [1,2,3,4]
A generic scheme is used to establish a prediction model to estimate the value of quality variables using easy-to-measure variables in the process, which facilitates the existence of numerous quality prediction methods
Structure Determination of mRMR-convolutional neural network (CNN). is subsection attempts to determine the structure of mRMR-CNN with the best performance and the well-known dropout technique is adopted in the fully connected layer to ensure the prediction capability, which is as well applied to other neural network-based methods used in this paper, if not specified. 960 features are extracted by pretrained CNN, and the prominent grid search approach is executed using feature preservation proportion grid [0.20, 0.25, 0.30, 0.35, 0.40, 0.50, 0.55, 0.60, 0.65, 0.70]

Summary

Introduction

Batch processes are extensively applied to the production of high-value products in many areas such as pharmaceutical, biotechnology, and polymer and semiconductor manufacturing industries [1,2,3,4]. Yuan et al [30] proposed a dynamic CNN to learn hierarchical dynamic nonlinear feature, based on which they developed a soft sensor model exploring both the spatial and temporal correlations from industrial data. All these methods try to extract abundant discriminative features, while the redundancy between extracted features is neglected, which may degrade the performance of CNN-based models. CNN is a successful deep learning technique proposed by Lecun et al [20], which simulates the mechanism of cat’s visual cortex [37] It is well known for its excellent automatic feature extraction ability and extensively applied to image classification, computer vision, and natural language processing fields. MRMR is originally developed for classification tasks, it works for regression tasks as well. e difference lies in calculating the mutual information between dependent variable x and independent variable y, instead of variables x and related class c, to quantify corresponding relevance D. is makes the relevance D slightly different in formula as the following expression: xi; y􏼁

Proposed Method

Methods

Findings

Conclusion