As an effective means of sensing difficult-to-measure process variables in real time, soft sensors are widely used but have a few significant limitations. Modeling errors between the mechanism model and real system can occur, which affect the accuracy of the mechanism-driven sensing result. Furthermore, deep learning-based data-driven soft sensors are complex black boxes, resulting in a lack of interpretability in terms of the established model and a lack of reliability regarding the sensing results. To solve these problems, this paper introduces the generative adversarial network (GAN) for soft sensor modeling and establishes an innovative GAN-based hybrid mechanism- and data-driven soft sensor framework. Meanwhile, considering the dynamic characteristics of the industrial process, a deep gated recurrent unit (DGRU) was adopted to compensate for the modeling errors in the mechanism model. This deep learning-based data-driven model not only captures the timing relationship between sensor data but also uses numerous unlabeled data. The generator to be identified in the GAN consists of the DGRU and mechanism model. In an industrial case for predicting the rotor thermal deformation of an air preheater in a power station boiler, the effectiveness and superiority of the hybrid-driven dynamic soft sensor model were verified, and the post hoc interpretability of the model was explained by manipulating the latent variables.

A Hybrid Mechanism- and Data-Driven Soft Sensor Based on the Generative Adversarial Network and Gated Recurrent Unit

Accurate and reliable measurement of key biological parameters during penicillin fermentation is of great significance for improving penicillin production. In this research context, a new hybrid soft sensor model method based on RF-IHHO-LSTM (random forest-improved​ Harris hawks optimization-long short-term memory) is proposed for penicillin fermentation processes. Firstly, random forest (RF) is used for feature selection of the auxiliary variables for penicillin. Next, improvements are made for the Harris hawks optimization (HHO) algorithm, including using elite opposition-based learning strategy (EOBL) in initialization to enhance the population diversity, and using golden sine algorithm (Gold-SA) in the search strategy to make the algorithm accelerate convergence. Then the long short-term memory (LSTM) network is constructed to build a soft sensor model of penicillin fermentation processes. Finally, the hybrid soft sensor model is used to the Pensim platform in simulation experimental research. The simulation test results show that the established soft sensor model, with high accuracy of measurement and good effect, can meet the actual requirements of engineering. 

An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process.

VMD-SEAE-TL-Based Data-Driven Soft Sensor Modeling for a Complex Industrial Batch Processes

A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process

Developing Semi-Supervised Latent Dynamic Variational Autoencoders to Enhance Prediction Performance of Product Quality

Semisupervised dynamic soft sensor based on complementary ensemble empirical mode decomposition and deep learning

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Semisupervised dynamic soft sensor based on complementary ensemble empirical mode decomposition and deep learning