Abnormal Condition Identification via OVR-IRBF-NN for the Process Industry with Imprecise Data and Semantic Information

Abstract

Data-driven methods are commonly used to identify abnormal operating conditions to maintain the health and safety of processes, which assume that the process data are precisely known and single-valued. However, in practice, process data are from multiple sources and are in various formats with uncertainties or measurement errors, which may lead to a high false-alarm rate and imprecise decisions. In addition, various key variables are difficult or impossible to measure online, and they are always estimated and described in terms of semantic information by operators or experts. In this work, a one-versus-rest interval radial basis function neural network (OVR-IRBF-NN)-based abnormality identification method is proposed for imprecise data and semantic information in the process industry. First, three types of process data, namely, precise single-valued variables (PSVVs), imprecise single-valued variables (ISVVs), and two-dimensional-interval-valued variables (2DIVVs), which are transformed from semantic information, are analyzed to investigate more comprehensive process information. Then, transformation approaches are presented for transforming these three types of data into one-dimensional intervals. Gaussian mixture model (GMM)-based measurement error estimation is developed for ISVVs, and an uncertain-unchanged interval adjustment strategy is proposed for 2DIVVs. Moreover, the one-versus-rest interval radial basis function neural network (OVR-IRBF-NN) is put forth for the identification of abnormal operating conditions via the analysis of one-dimensional-interval-valued data, providing more reliable and robust identification results due to the employment of interval-valued data. The proposed strategy is evaluated on both a numerical example and a real hydrometallurgical metallurgy leaching process, and the results demonstrate the feasibility and effectiveness of the strategy.

Application of local and global interval embedding algorithms in uncertain system processes

“…If the ideal data of the ith sensor cannot be measured, the lower and upper limits can be determined through expert experience or measurement error specifications provided by the sensor manufacturers [24]. The conversion process of interval value data is given by the following equation…”

Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

“…The results of Table 2 s comparison of the results using misclassification rates and contour coefficients show that the suggested fusion clustering approach performs better on this dataset than the single clustering method and can successfully distinguish between normal and abnormal data. The model was evaluated with 11 distinct configurations applied to the activation functions of Bi-LSTM + MLP (ReLU, ELU, Tanh, SiLU, LeakyReLU), which differ in the number of various Bi-LSTM (1,2,3) and MLP layers (1,2,3). A feature introduced to artificial neural networks is the activation function, which aids in the network's ability to recognize intricate patterns in data.…”

“…The Bi-LSTM + MLP model performs best with 1-layer Bi-LSTM and 1-layer MLP when employing the LeakyReLU activation function for this collection of data, as can be seen by the bolding of each best result. number of various Bi-LSTM (1,2,3) and MLP layers (1,2,3). A feature introduced to artificial neural networks is the activation function, which aids in the network s ability to recognize intricate patterns in data.…”

“…Chemical processes can pose a significant risk to safety and efficiency due to their potential for flammability, explosiveness, toxicity, and hazardous reactions. Abnormal conditions generated during chemical processes can cause disturbances in multiple variables that transmit across the entire process through logistics and equipment connections [1], leading to numerous alarms and abnormal conditions. Therefore, monitoring and warning of abnormal conditions are essential in the chemical industry to ensure plant safety and efficiency [2].…”