Batch Process Quality Prediction Research Articles

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

Novel Multi-flow Multi-scale Convolutional Neural Network Developed for Quality Prediction of Batch Processes to Fuse Data With Different Sampling Frequencies

Collaborative Multiple Players to Address Label Sparsity in Quality Prediction of Batch Processes.

For decades, soft sensors have been extensively renowned for their efficiency in real-time tracking of expensive variables for advanced process control. However, despite the diverse efforts lavished on enhancing their models, the issue of label sparsity when modeling the soft sensors has always posed challenges across various processes. In this paper, a fledgling technique, called co-training, is studied for leveraging only a small ratio of labeled data, to hone and formulate a more advantageous framework in soft sensor modeling. Dissimilar to the conventional routine where only two players are employed, we investigate the efficient number of players in batch processes, making a multiple-player learning scheme to assuage the sparsity issue. Meanwhile, a sliding window spanning across both time and batch direction is used to aggregate the samples for prediction, and account for the unique 2D correlations among the general batch process data. Altogether, the forged framework can outperform the other prevalent methods, especially when the ratio of unlabeled data is climbing up, and two case studies are showcased to demonstrate its effectiveness.

Soft Sensor Development Based on Unsupervised Dynamic Weighted Domain Adaptation for Quality Prediction of Batch Processes

Soft Sensor Development Based on Unsupervised Dynamic Weighted Domain Adaptation for Quality Prediction of Batch Processes

Domain adaptation graph convolution network for quality inferring of batch processes

Developing a reliable quality inference model in batch processes remains a challenge. The performance of the model tends to decline due to batch-to-batch variations. Additionally, the spatial coupling relationships among multiple variables need to be mined to explain the model behavior. To this end, a domain adaptation graph convolution network is developed for quality prediction of batch processes. Under the constraint of the structural loss function, the model is firstly encoded to capture the topological correlations between variables. Moreover, the production time information is added into input variables to track the process temporal characteristics. Subsequently, the cross-batch characteristics are explored by the finetune technology to transfer the source knowledge and enlarge the prediction domain. Compared with several popular data-driven candidates, the proposed model shows its superiority in two batch processes. Remarkably, the variable relationships self-captured by the source model are visualized, which are consistent with the process prior knowledge to verify the model interpretability.

Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals.

The quality prediction of batch processes is an important task in the field of biological fermentation. However, dynamic nonlinearity, unequal sampling intervals, uneven duration, and multiple features of a batch process make this task challenging. Thus, the multiple-feature fusion transformer (MFFT) model is proposed for the time series quality prediction of a batch process. First, the application of sequence-to-sequence architecture enables MFFT to perform a wide range of sequence prediction tasks. Second, the transformer parallel operation model imposes no rigid requirement for the order of sequence input, allowing the model to deal with problems of unequal interval sampling and utilize the sequence information. Third, MFFT integrates a pretrained ResNet50 as a mycelium status classifier for fusing image information into the features. Moreover, a multiple-feature encoding structure is proposed to integrate sampling time and mycelium status. Finally, multiple tasks in penicillin fermentation have shown that MFFT significantly outperforms existing methods for time series prediction.

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

The major promise of the 4th industrial revolution is encompassed by the utilization of real-time process data to inform operational decision-making. Research focus relating to the monitoring of batch process end product qualities has typically been dominated by the use of latent variable models. In this work, we combined latent variable modeling with probabilistic machine learning methods to construct novel soft-sensors, which are able to synchronously capture nonlinearities expressed within process data, as well as identify accurate uncertainty estimates for predictions. Specifically, we explored the combination of multiway projection to latent structures (MPLS) with Gaussian processes (GPs), Bayesian neural networks (BNNs) and heteroscedastic noise neural networks (HNNs). We demonstrated performance of the soft-sensors for industrial batch process quality control, which involves estimating the end viscosity of different variants of a non-newtonian liquid product manufactured over different periods. Through experimental validation, it is concluded that the use of the MPLS-HNN soft-sensor provides particular promise for industrial batch process monitoring given its high accuracy and reliability, as well as ease for practical implementation.

Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis.

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction

Batch process quality prediction is an important application in manufacturing and chemical industries. The complexity of batch processes is characterized by multiphase, nonlinearity, dynamics, and uneven durations so that modeling of these batch processes is rather difficult. Moreover, there are other challenges in the face of quality prediction. Specifically, the process trajectories over the whole running duration potentially make specific contributions to the final targets so that the prediction issue embraces tremendously high-dimensional inputs but very low-dimensional outputs. This means that the prediction suffers from a severe dimensional imbalance between inputs and outputs. Motivated by these difficulties, this paper proposes a new deep learning-based framework for complex feature representative and quality prediction. Long short-term memory (LSTM) is used to extract comprehensive quality-relevant hidden features from a long-time sequence in each phase, significantly reducing the predictor dimensions. And these features from different phases are further integrated and compressed by a stacked auto-encoder (SAE). A practical industrial example testifies to the efficacy of the proposed framework.

Transfer learning for end-product quality prediction of batch processes using domain-adaption joint-Y PLS

In this work, a domain-adaption joint-Y partial least squares (JYPLS) is proposed to solve the problem of transfer learning for end-product quality prediction of batch processes. The difference from the variances in source and target domains is included as a regular term in the objective function of the traditional JYPLS model to realize the trade-off between minimizing the difference of empirical distribution in source and target domains and maximizing the covariance between latent variables and output variables. Since the issue of domain-adaption is considered in the proposed method, the prediction performances can be further improved. And the merits of JYPLS method can also be retained at the same time. The proposed method is tested on simulated data sets to verify the efficiency of the additional index in the objective function. It is also applied to predict the final mean particle size of a new cobalt oxalate synthesis process, and the data sets used to build the data-driven model is obtained from two synthesis processes with different model parameters and control policies. Compared with traditional JYPLS method, the prediction accuracy of the proposed method in the target domain has been greatly improved.

Soft sensor design based on phase partition ensemble of LSSVR models for nonlinear batch processes.

Traditional single model based soft sensors may have poor performance on quality prediction for batch processes because of the strong nonlinearity, multiple-phase, and time-varying characteristics. Therefore, a phase partition based ensemble learning framework upon least squares support vector regression (LSSVR) is proposed for soft sensor modeling. Firstly, multiway principal component analysis (MPCA) is employed to handle high-dimensional datasets and extract essential correlation information. Then, different operation phases of the process can be identified by the phase partition strategy based on Gaussian mixture model (GMM) method. Meanwhile, the optimal Gaussian component number is determined by Bayesian information criterion (BIC) technique. Further, multiple localized LSSVR models are constructed to characterize the various dynamic relationships between quality and process variables for local regions, while the grid search (GS) and ten-fold cross-validation methods are introduced to parameter optimization for each local model. Finally, the posterior probability for each test sample with respect to different phases can be estimated by Bayesian inference strategy, and local outputs are integrated to produce the final quality prediction results. Feasibility and superiority of the proposed soft sensor are validated through a case study for penicillin fermentation process. It can achieve satisfactory prediction accuracy and effectively tackle nonlinear and multi-phase modeling problems in chemical and biological processes.

Phase division and transition modeling based on the dominant phase identification for multiphase batch process quality prediction

Batch processes are carried out from one steady phase to another one, which may have multiphase and transitions. Modeling in transitions besides in the steady phases should also be taken into consideration for quality prediction. In this paper, a quality prediction strategy is proposed for multiphase batch processes. First, a new repeatability factor is introduced to divide batch process into different steady phases and transitions. Then, the different local cumulative models that considered the cumulative effect of process variables on quality are established for steady phases and transitions. Compared with the reported modeling methods in transitions, a novel just-in-time model can be established based on the dominant phase identification. The proposed method can not only consider the dynamic characteristic in the transition but also improve the accuracy and the efficiency of transitional models. Finally, online quality prediction is performed by accumulating the prediction results from different phases and transitions. The effectiveness of the proposed method is demonstrated by penicillin fermentation process.

On-line quality prediction of batch processes using a new kernel multiway partial least squares method

The conventional data-driven soft sensor methods such as multiway partial least squares have been encountering nonlinear problems in predictions of batch processes, and kernel methods have been used to deal with these problems. In this work, a new data-driven soft sensor method is proposed by developing a Reduced Dual Kernel multiway partial least squares algorithm. First, the number of kernel vectors is reduced by the feature vector selection method. Then, by projecting both input data and the output data into two reduced kernel spaces, dual kernel matrices are established. These two matrices can be used to build PLS models. Finally, the predicted data in the kernel space can be reversely projected onto its original space during online prediction. Comparisons were made among the proposed method and some pervious algorithms through a numerical example and an Escherichia coli fermentation batch process.

Phase adaptive RVM model for quality prediction of multiphase batch processes with limited modeling batches

For quality prediction of batch processes under limited modeling batches, the relevance vector machine (RVM) has recently been introduced. By unfolding the three-way dataset through the variable direction, significant nonlinearities are remained in the process data, which in turn explored the nonlinear modeling ability of RVM. For multiphase batch processes, however, different phases may have simultaneous impacts on the final product quality, which should be connected together in the modeling stage. In this paper, a new phase adaptive RVM model is proposed for quality prediction in multiphase batch processes. Based on the information transfer of relevance vectors in each RVM model, different phases are connected one after another, providing simultaneous information for prediction of the final product quality. A detailed industrial case study is given to show the efficiency of the new developed method.

Self-Training Statistical Quality Prediction of Batch Processes with Limited Quality Data

Because of expensive cost or large time delay, quality data are difficult to obtain in many batch processes, while the ordinary process variables are measured online and recorded frequently. This paper intends to build a statistical quality prediction model for batch processes under limited quality data. Particularly, the self-training strategy is introduced and combined with the partial least-squares regression model. For multiphase batch processes, a phase-based self-training PLS model is developed for quality prediction in each phase of the process. The feasibility and effectiveness of the developed method is evaluated by an industrial injection molding process.

Phase-based Statistical Modeling, Online Monitoring and Quality Prediction for Batch Processes

The paper first presents a comprehensive description of some hot problems in statistical modeling,online monitoring,and quality prediction for batch process based on multivariate statistical techniques,including the development of various solutions and their advantages and disadvantages.Then,phase-based statistical analysis strategies are addressed with a focus on the multiplicity of operation phase and phase transition behaviors.This part analyzes the phase-based process characteristics and their effects on product quality,discusses the inherent basis,and reveals their significance.Finally,from the viewpoint of solving practical problems,the existing problems are explored and their prospective development is discussed.Phase-based statistical analysis for batch processes is important in both theory meaning and application,which will benefit further process monitoring,fault diagnosis and quality prediction.

Improved Knowledge Extraction and Phase-Based Quality Prediction for Batch Processes

This paper develops a process analysis and quality prediction scheme for the improvement of quality estimation performance in batch processes. Combined with prior phase division algorithm, correlation measure criteria are employed to identify critical phases and key variables with respect to quality prediction. As an effective process understanding and knowledge extraction tool, correlation analyses focusing on each phase help one reveal the phase-specific effect of process operation on product quality prediction without any requirement of prior expertise. The spoiling influences on quality inferential models caused by inclusion of variable redundancy and autocorrelation is well alleviated by the phase-based variablewise unfolding technique and key variable selection procedure. Meanwhile, the proposed method does not demand estimating the unavailable future process observations when used for online quality predicting. On the basis of specific phase analyses and variable selections, the applications of the...

Stage-Based Process Analysis and Quality Prediction for Batch Processes

A process analysis and quality prediction scheme is proposed based on a stage-based PLS modeling for batch processes. Without any requirement of prior process knowledge, the scheme first divides a batch process into stages of different process characteristics. Subsequently, a strategy is developed to identify stages that have critical influences on concerned qualities, defined as critical-to-quality stages. Within these critical-to-quality stages, an algorithm is then further developed to identify the variables that have significant contributions to the quality variations. Finally, based on the identified nature of quality and stage relationships, a set of algorithms is developed for online quality prediction. The applications of the proposed scheme to injection molding show that the proposed analysis and quality prediction are not only effective but are also able to enhance process understanding and identify specific variables and periods for quality improvement.

Online Performance Monitoring and Quality Prediction for Batch Processes

Abstract Two different quality prediction techniques are incorporated with online MSPM through PLS modeling in this study. The first technique is based on unfolding a hatch data array by preserving variable direction. An MPLS model between this matrix and vector of elapsed local batch times is developed to reflect the batch progress. More data partitions hecome available as the batch progresses and these partitions are rearranged into a matrix to develop local MPLS models predicting quality online. The second technique uses hierarchical PLS rnodeling in an adaptive manner resulting in a model that can be used to predict end-of-batch quality online. Neither technique requires estimation of future portions of variable trajectories and both are suitable for online multivariate statistical process monitoring and fault diagnosis. Case studies from a simulated fed-batch penicillin fermentation illustrate the implementation of the methodology.