Real-time monitoring of reaction mechanisms from spectroscopic data using hidden semi-Markov models for mode identification. (September 2022)

• Record Type: Journal Article
• Authors: Puliyanda, Anjana; Li, Zukui; Prasad, Vinay
• Is Part Of: Journal of process control. Volume 117(2022)
• Page Start: 188
• Page End: 205
• Publication Date: 2022-09
• Subjects: Real-time reaction mechanisms -- Data-driven reaction monitoring -- Explicit duration modeling -- Dynamic mode identification -- Bayesian networks -- Process monitoring; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.07.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this work, we present a framework for process monitoring focusing on the dynamics of reaction mechanisms based purely on online spectroscopic data. This is accomplished by developing an explicit duration hidden semi-Markov model (HSMM) that is used to monitor changes in reaction mechanisms with changing temperatures in a complex reacting system by dynamically identifying groups of spectroscopic samples that belong to a mode, and the mode duration of the reaction mechanism associated with the samples. An expectation maximization algorithm is used for parameter re-estimation, and Viterbi state decoding is used to identify the most likely sequence of hidden states/modes that may have generated the observed sequence of spectra. The reaction mechanism associated with samples of a mode is then deduced by extracting latent features among spectra of the mode and learning a probabilistic graphical structure among the features using Bayesian networks, which represent a network or mechanism of hypothesized reactions. The technique is demonstrated on case studies related to the partial upgrading of bitumen using thermochemical conversion based on the acquisition of Fourier transform infrared spectroscopic data. This system is complex enough that prior information regarding both species and reactions is unavailable. Both offline and online monitoring are implemented for mode identification, and the technique provides monitoring of the multi-modal process and, at the same time, provides insight into the chemistry specific to each mode, which makes it useful both for process control and fundamental studies into process chemistry. Synthetic dynamic spectral data, that is derived through interpolation from real spectral data obtained at various static conditions of temperature and residence time, is used in the study. Highlights: Data-driven approach for inferring reaction mechanisms from online spectroscopic data Hidden semi-Markov model segments data into modes and their duration distributions Latent factor decomposition of spectra from a mode are mapped to reactive species Modes physically interpreted as reaction mechanisms by constructing Bayesian networks Reactive species, reaction mechanisms, time scales deciphered for process monitoring