Soft Sensor Model Research Articles

A Soft Sensor Model for Predicting the Flow of a Hydraulic Pump Based on Graph Convolutional Network–Long Short-Term Memory

The hydraulic pump plays a pivotal role in engineering machinery, and it is essential to continuously monitor its operating status. However, many vital signals for monitoring cannot be directly obtained in practical applications. To address this, we propose a soft sensor approach for predicting the flow signal of the hydraulic pump based on a graph convolutional network (GCN) and long short-term memory (LSTM). Our innovative GCN-LSTM model is intricately designed to capture both spatial and temporal interdependencies inherent in complex machinery, such as hydraulic pumps. We used the GCN to extract spatial features and LSTM to extract temporal features of the process variables. To evaluate the performance of GCN-LSTM in predicting the flow of a hydraulic pump, we construct a real-world experimental dataset with an actual hydraulic shovel. We further evaluated GCN-LSTM on two public datasets, showing the effectiveness of GCN-LSTM for predicting the flow of hydraulic pumps and other complex engineering operations.

A review of just‐in‐time learning‐based soft sensor in industrial process

AbstractData‐driven soft sensing approaches have been a hot research field for decades and are increasingly used in industrial processes due to their advantages of easy implementation and high efficiency. However, nonlinear and time‐varying problems widely exist in practical industrial processes. Just‐in‐time learning (JITL) was proposed to solve these problems and has attracted great attention in practical applications. To present a comprehensive review of JITL‐based soft sensor studies and provide detailed technical guidance for new researchers, this paper introduces the recent research on JITL‐based soft sensor modelling methods in the industrial process from three aspects: similarity criterion, sample subset, and local model, which include the whole process of establishing a JITL‐based soft sensor. Moreover, the future research and innovation directions of JITL‐based soft sensors in industrial processes are also prospected.

A hybrid soft sensor for key product yield of FCC unit based on deep learning framework driven by data and process mechanism

The model for online prediction of product yield embedded in the optimization and control framework has the potential to improve the profits of fluid catalytic cracking (FCC) units. In this work, a hybrid soft sensor model is developed to predict key product yield in commercial FCC units. The deep learning framework based on semi-supervised learning is driven by data and process mechanism to construct the hybrid model with a double-layer structure. Moreover, process simulation strategy is utilized for key input variable selection of the hybrid model to ensure model reliability. The hybrid model is compared to a pure data-driven model in terms of accuracy and trend consistency of model predictions. The results demonstrate that the hybrid model exhibits a superior prediction performance. This work suggests that the hybrid model can drive innovation and industrial practice in the optimization and control of FCC units.

Instance transfer partial least squares for semi-supervised adaptive soft sensor

In order to deal with the unknown data drift problem caused by the change of working conditions in the process of industrial soft sensor modeling, this paper proposes a semi-supervised soft sensor modeling algorithm based on instance transfer learning, named instance transfer partial least squares. Under the framework of nonlinear partial least squares, the algorithm introduces the instance transfer inner model, and realizes the distribution adaptation of the modeling data and the data to be predicted through sample importance weighting. First, a nonlinear partial least squares model is constructed by using labeled samples in the source domain, which consists of a linear outer model and a nonlinear inner model. The linear outer model maps the original data to the hidden variable space, and the nonlinear inner model realizes the nonlinear modeling of hidden variables through kernel ridge regression. Second, a small number of labeled samples from the target domain are used to update the model weights of the nonlinear inner model, enabling adaptation of the latent variable distribution. The effectiveness of the algorithm is verified on a toy dataset, a simulation dataset and a real pulverized coal power plant dataset.

A Soft Sensor for Multirate Quality Variables Based on MC-CNN.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling

Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling

Information Complementary Fusion Stacked Autoencoders for Soft Sensor Applications in Multimode Industrial Processes

Multimode characteristics within process data are common due to switching operating conditions or load changes, which pose challenges for soft sensor modeling. The key challenges include multiple data patterns and complex nonlinear relationships between process variables and quality variables. To address these issues, an information complementary fusion stacked autoencoders (ICF-SAE) model is proposed in this article. In ICF-SAE, a gating module is designed. Since the gating module can regulate the information flowing path, the proposed model can represent multiple patterns in the data. Moreover, the bottom hidden representations of SAE contain a lot of noise. Using them directly to predict quality variables may deteriorate the prediction accuracy of the model. Taking this issue into account, a layerwise fusion module including bottom-top and top-bottom fusion channels is designed. This module can filter noise progressively by sequential layer fusions. Detailed evaluations of the proposed ICF-SAE model are conducted using numerical data and real-world industrial data. The results show that the proposed ICF-SAE model achieves better prediction accuracy than the existing methods.

BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection

BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection

Semi-Supervised Soft Sensor Modeling Based on Ensemble Learning With Pseudolabel Optimization

Semi-Supervised Soft Sensor Modeling Based on Ensemble Learning With Pseudolabel Optimization

Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network

Real-time prediction of key quality variables based on data-driven soft sensor modeling is an important way to monitor flotation status and product quality in the froth flotation process. However, the existing data-driven methods have limitations in terms of nonlinear feature extraction and interpretability. In addition, the prevalent correlations between process variables can help improve the model interpretability and feature extraction of the model, but there are still challenges in exploring the potential correlations between process variables due to the complexity of the industrial process mechanism and the presence of high noise in process data. These relationships can be abstracted as edges between nodes in a graph representation. To fully explore the deep process-related structural features of data, a novel stacked graph convolutional network (S-GCN) is proposed. First, S-GCN aggregates the features of neighboring nodes by an adaptive adjacency matrix to achieve the extraction of structural features among process variables. Then, the labeled quality variable data detected by laboratory assay analysis are utilized to pre-train the network layer by layer to obtain better initial parameters and adjacency matrices for quality prediction. Finally, the prediction model is constructed based on the learned parameters to explore the interpretability of model and perform prediction tasks. Experimental results on real industrial potassium chloride froth flotation process data show a significant improvement in the prediction accuracy and interpretability of the proposed method.

Transfer Learning Soft Sensor Modeling based on Two-dimensional Domain-adaption Stochastic Configuration Network

Transfer Learning Soft Sensor Modeling based on Two-dimensional Domain-adaption Stochastic Configuration Network

BTPNet: A Probabilistic Spatial-Temporal Aware Network for Burn-Through Point Multistep Prediction in Sintering Process.

Burn-through point (BTP) is a very key factor in maintaining the normal operation of the sintering process, which guarantees the yield and quality of sinter ore. Due to the characteristics of time-varying and multivariable coupling in the actual sintering process, it is difficult for traditional soft-sensor models to extract spatial-temporal features and reduce multistep prediction error accumulation. To address these issues, in this study, we propose a probabilistic spatial-temporal aware network, called BTPNet, which is used to extract spatial-temporal feature for accurate BTP multistep prediction. The BTPNet model consists of two parts: an encoder network and a decoder network. In the encoder network, the multichannel temporal convolutional network (MTCN) is employed to extract the temporal features. Meanwhile, we also propose a novel architectural unit called variables interaction-aware module (VIAM) to extract the spatial features. In the decoder network, to reduce the accumulated errors of the last step prediction, a probabilistic estimation (PE) method is proposed to improve the performance of multistep prediction. Finally, the experimental results on a real sintering process demonstrate the proposed BTPNet model outperforms state-of-the-art multistep prediction models.

BOF steelmaking endpoint carbon content and temperature soft sensor based on supervised dual-branch DBN

Accurate prediction of endpoint carbon content and temperature is critical in the basic oxygen furnace (BOF) steelmaking process. Although deep learning soft sensor approaches have the capacity to extract abstract features from high-dimensional nonlinear steelmaking data, they confront the challenge of a low correlation between acquired features and labels. This work presents a BOF steelmaking soft sensor model based on supervised dual-branch deep belief network (SD-DBN) to address this issue. The SD-DBN model incorporates label information into the feature extraction process and fuses crucial feature information to complete the feature extraction in order to extract features that are closely connected to the target variables. First, the supervised Restricted Boltzmann Machine (RBM) is improved by using a pruning strategy to extract features that are highly correlated with quality information, and then the autocorrelation key feature extraction module is spliced and fused to form a dual-branch feature extraction module to improve key information extraction. Second, stacking the supervised dual-branch RBM modules to build a deep feature extraction network enhances the deep extraction capabilities of data features. This deep network stacking not only increases the impact of essential target data in hierarchical training, but it also acquires characteristics associated with the target variables.

A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models

The proposed methodology presents a comprehensive analysis of soft sensor modeling techniques for air ozone prediction. We compare the performance of three different modeling techniques: LR (linear regression), NN (neural networks), and RFR (random forest regression). Additionally, we evaluate the impact of different variable sets on prediction performance. Our findings indicate that neural network models, particularly the RNN (recurrent neural networks), outperform the other modeling techniques in terms of prediction accuracy. The proposed methodology evaluates the impact of different variable sets on prediction performance, finding that variable set E demonstrates exceptional performance and achieves the highest average prediction accuracy among various software sensor models. In comparing variable set E and A, B, C, D, it is observed that the inclusion of an additional input feature, PM10, in the latter sets does not improve overall performance, potentially due to multicollinearity between PM10 and PM2.5 variables. The proposed methodology provides valuable insights into soft sensor modeling for air ozone prediction.Among the 72 sensors, sensor NNR[Y]C outperforms all other evaluated sensors, demonstrating exceptional predictive performance with an impressive R2 of 0.8902, low RMSE of 24.91, and remarkable MAE of 19.16. With a prediction accuracy of 81.44%, sensor NNR[Y]C is reliable and suitable for various technological applications.

Dynamic nonlinear soft sensor modelling method using linear slow feature analysis and least squares support vector regression for batch processes

AbstractData‐driven soft sensor models have been extensively utilized in industrial processes. Batch processes are usually employed to manufacture low‐volume and high value‐added products in chemical, materials, and pharmaceutical industries. The most distinctive features of batch process lie in nonlinear, repetition, and slow time varying characteristics. In this paper, a data‐driven soft sensor modelling method based on linear slow feature analysis (LSFA) and least squares support vector regression (LSSVR) is proposed. In this method, LSFA was used to effectively capture the driving force behind the data features that change as slowly as possible. Then, a LSSVR model was constructed between the extracted slow feature variables and quality variables. Finally, a numerical example, industrial penicillin fermentation processes, and cobalt oxalate synthesis process were utilized to confirm the prediction accuracy and model reliability of the proposed approach.

Quality-driven Gaussian mixture variational probabilistic network for soft sensor application in PET/PA6 polymerization process

Variational autoencoder has been widely used to build soft sensor models as a deep unsupervised feature extractor in recent years. However, the vanilla VAE employs a single diagonal Gaussian distribution as the approximate posterior in its latent feature space, which may lead to inferior performance when solving complex non-Gaussian multimode modeling problem. To address the issue, this paper proposes a quality-driven Gaussian mixture variational probabilistic network-based (QGMVPN) soft sensor model. First, Gaussian mixture density with diagonal covariance matrices and Householder transformations are implemented to characterize the distributions of non-Gaussian multimode feature. Then, a quality-driven strategy is designed to extract quality-related features and a novel gating mechanism is employed to enhance the nonlinear representation ability. Last, the trained decoder, coupled with the sampling space, is directly applied to soft sensor modeling owing to the special extra input of the decoder. The effectiveness of QGMVPN-based model is demonstrated on two polymerization datasets.

Multisource Information Fusion for Autoformer: Soft Sensor Modeling of FeO Content in Iron Ore Sintering Process

As a key thermal-state indicator of the iron ore sintering process, the content of ferrous oxide (FeO) in the finished sinter is directly related to product quality. Based on the massive data of sintering process, the data-driven soft sensor model provides a good choice for real-time FeO content detection. However, the complex characteristics of the data, including dynamics, nonlinearity, and multi-source heterogeneity, are still the main obstacles to improving the modeling accuracy. To solve this problem, a multi-source information fusion Autoformer (MIF-Autoformer) model is introduced in this paper. Firstly, feature-level information fusion and data-level information fusion are implemented based on the MIF strategy. Then, the comprehensive information of the sintering process is fed to the downstream Autoformer model in a serial manner, which not only improves the information capacity, but also provides additional prior information about the FeO content grade. This is helpful for Autoformer to capture the complex temporal distributions in the sintering process. Finally, the proposed model is applied to a real sintering plant. Experimental results show that the hybrid image features provided by the MIF strategy have a general optimization effect on different competitive models, and MIF-Autoformer exhibits the lowest prediction error on the test set.

A modeling method of wide random forest multi-output soft sensor with attention mechanism for quality prediction of complex industrial processes

Complex industrial production processes often involve multiple product quality indicators that are interrelated. There exists a complex nonlinear mapping relationship between the operational input feature variables and multiple output target quality variables, making it difficult to accurately model through first-principle models. In order to fully capture the complex relationship between measurable variables and difficult-to-measure quality variables, and achieve accurate prediction of multiple output variables to meet the needs of practical industrial sites, this paper proposes a broad random forest-based multi-output soft sensor modeling method based on the idea of attention mechanism derived from the concept of broad learning systems. This method comprehensively considers the dynamic impact of different feature variables on the target quality indicators in actual production processes. The attention mechanism assists the soft sensor model in capturing contextual information better when dealing with long sequences, with a focus on the relevant parts related to the current task. Additionally, the interpretable random forest algorithm is employed as the weight estimator for the basic feature learning unit of Broad-based learning, enabling regression modeling of multiple target quality variables. The use of Broad-based random forest improves the model’s learning ability, interpretability, and generalization capability. To validate the reliability of the proposed method, it was applied to real industrial cases. The results demonstrated that the multi-output quality variable prediction performance of the proposed soft sensor outperforms existing soft sensors in terms of prediction accuracy. This indicates promising industrial application prospects for the proposed method.

Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants.

Assessment of wastewater effluent quality in terms of physicochemical and microbial parameters is a difficult task; therefore, an online method which combines the variables and represents a final value as the quality index could be used as a useful management tool for decision makers. However, conventional measurement methods often have limitations, such as time-consuming processes and high associated costs, which hinder efficient and practical monitoring. Therefore, this study presents an approach that underscores the importance of using both short- and long-term memory networks (LSTM) to enhance monitoring capabilities within wastewater treatment plants (WWTPs). The use of LSTM networks for soft sensor design is presented as a promising solution for accurate variable estimation to quantify effluent quality using the total chemical oxygen demand (TCOD) quality index. For the realization of this work, we first generated a dataset that describes the behavior of the activated sludge system in discrete time. Then, we developed a deep LSTM network structure as a basis for formulating the LSTM-based soft sensor model. The results demonstrate that this structure produces high-precision predictions for the concentrations of soluble X1 and solid X2 substrates in the wastewater treatment system. After hyperparameter optimization, the predictive capacity of the proposed model is optimized, with average values of performance metrics, mean square error (MSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), of 23.38, 0.97, and 1.31 for X1, and 9.74, 0.93, and 1.89 for X2, respectively. According to the results, the proposed LSTM-based soft sensor can be a valuable tool for determining effluent quality index in wastewater treatment systems.

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning.

Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets. Second, an improved seagull optimization algorithm was used to optimize and transform the Gaussian process regression model, and a sub-prediction model was established. Finally, the fusion strategy was determined according to the connectivity between the test samples and local sample subsets. The proposed soft sensor model was applied to the prediction of key biochemical parameters of the marine lysozyme fermentation process. The simulation results show that the proposed soft sensor model can effectively predict the key biochemical parameters with relatively small prediction errors in the case of limited training data. According to the results, this model can be expanded to the soft sensor prediction applications in general nonlinear systems.