Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) network is very suitable for soft sensor model development. However, the original LSTM does not consider variable and sample relevance for quality prediction. In order to overcome this problem, a spatiotemporal attention-based LSTM network is proposed for soft sensor modeling, which can, not only identify important input variables that are related to the quality variable at each time step, but also adaptively discover quality-related hidden states across all time steps. By taking the spatiotemporal quality-relevant interactions into consideration, the prediction performance can be improved for the soft sensor model. The effectiveness and flexibility of the proposed model is demonstrated on an industrial hydrocracking process to predict the initial boiling points of heavy naphtha and aviation kerosene.

The growth of data collection in industrial processes has led to a renewed emphasis on the development of data-driven soft sensors. A key step in building an accurate, reliable soft sensor is feature representation. Deep networks have shown great ability to learn hierarchical data features using unsupervised pretraining and supervised fine-tuning. For typical deep networks like stacked auto-encoder (SAE), the pretraining stage is unsupervised, in which some important information related to quality variables may be discarded. In this article, a new quality-driven regularization (QR) is proposed for deep networks to learn quality-related features from industrial process data. Specifically, a QR-based SAE (QR-SAE) is developed, which changes the loss function to control the weights of the different input variables. By choosing an appropriate inductive bias for the weight matrix, the model provides quality-relevant information for predictive modeling. Finally, the proposed QR-SAE is used to predict the quality of a real industrial hydrocracking process. Comparative experiments show that QR-SAE can extract quality-related features and achieve accurate prediction performance.

The development of advanced techniques for process monitoring and fault diagnosis using both model-based and data-driven approaches has led to many practical applications. One issue that has not been considered in such applications is the ability to deal with key performance indicators (KPIs) that are only sporadically measured and with significant time delay. Therefore, in this paper, the data-driven design of diagnostic-observer-based process monitoring schemes is extended to include the ability to detect changes given infrequently measured KPIs. The extended diagnostic observer is shown to be stable and hence able to converge to the true value. The proposed method is tested using both Monte Carlo simulations and the Tennessee-Eastman problem. It is shown that although time delay and sampling time increase the detection delay, the overall effect can be mitigated by using a soft sensor. Furthermore, it is shown that the results are not strongly dependent on the sampling time, but do depend on the time delay. Therefore, the proposed soft-sensor-based monitoring scheme can efficiently detect faults even in the absence of direct process information.