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Srinivasan K, Puliyanda A, Prasad V. Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention. J Phys Chem A 2024; 128:4714-4729. [PMID: 38836378 DOI: 10.1021/acs.jpca.4c01592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
In this work, we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-D convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch hydrothermal liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities. Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data, and we are also able to recover candidate molecules and reaction networks close to the ones reported in the previous literature studies for biomass pyrolysis.
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Affiliation(s)
- Karthik Srinivasan
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada
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Sattari F, Srinivasan K, Puliyanda A, Prasad V. Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass. Ind Eng Chem Res 2023. [DOI: 10.1021/acs.iecr.2c04309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Affiliation(s)
- Fereshteh Sattari
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Karthik Srinivasan
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Anjana Puliyanda
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada
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Sattari F, Prasad V. Data‐driven hypotheses of reaction networks for thermochemical conversion of a physical mixture of levoglucosan and 2‐phenoxyethyl benzene. CAN J CHEM ENG 2022. [DOI: 10.1002/cjce.24071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Fereshteh Sattari
- Department of Chemical and Materials Engineering University of Alberta Edmonton Alberta Canada
| | - Vinay Prasad
- Department of Chemical and Materials Engineering University of Alberta Edmonton Alberta Canada
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Sattari F, Lefsrud L, Kurian D, Macciotta R. A theoretical framework for data-driven artificial intelligence decision making for enhancing the asset integrity management system in the oil & gas sector. J Loss Prev Process Ind 2022. [DOI: 10.1016/j.jlp.2021.104648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Wang Y, Hu J, Zhang X, Yusuf A, Qi B, Jin H, Liu Y, He J, Wang Y, Yang G, Sun Y. Kinetic Study of Product Distribution Using Various Data-Driven and Statistical Models for Fischer-Tropsch Synthesis. ACS OMEGA 2021; 6:27183-27199. [PMID: 34693138 PMCID: PMC8529696 DOI: 10.1021/acsomega.1c03851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/24/2021] [Indexed: 05/14/2023]
Abstract
Three modeling techniques, namely, a radial basis function neural network (RBFNN), a comprehensive kinetic with genetic algorithm (CKGA), and a response surface methodology (RSM), were used to study the kinetics of Fischer-Tropsch (FT) synthesis. Using a 29 × 37 (4 independent process parameters as inputs and corresponding 36 responses as outputs) matrix with total 1073 data sets for data training through RBFNN, the established model is capable of predicting hydrocarbon product distribution i.e., the paraffin formation rate (C2-C15) and the olefin to paraffin ratio (OPR) within acceptable uncertainties. With additional validation data sets (15 × 36 matrix with total 540 data sets), the uncertainties of using three different models were compared and the outcomes were: RBFNN (±5% uncertainties), RSM (±10% uncertainties), and CKGA (±30% uncertainties), respectively. A new effective strategy for kinetic study of the complex FT synthesis is proposed: RBFNN is used for data matrix generation with a limited number of experimental data sets (due to its fast converge and less computation time), CKGA is used for mechanism selections by the Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach using a genetic algorithm to find out potential reaction pathways, and RSM is used for statistical analysis of the investigated data matrix (generated from RBFNN through central composite design) upon responses and subsequent singular/multiple optimizations. The proposed strategy is a very useful and practical tool in process engineering design and practice for the product distribution during FT synthesis.
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Affiliation(s)
- Yixiao Wang
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Jing Hu
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Xiyue Zhang
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Abubakar Yusuf
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Binbin Qi
- Department
of Petroleum Engineering, China University
of Petroleum—Beijing, Beijing 102249, China
| | - Huan Jin
- School
of Computer Science, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Yiyang Liu
- Department
of Chemistry, University College London
(UCL), 20 Gordon Street, London WC1H 0AJ, U.K.
| | - Jun He
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
| | - Yunshan Wang
- National
Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy
of Sciences, Beijing 100190, China
| | - Gang Yang
- National
Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy
of Sciences, Beijing 100190, China
| | - Yong Sun
- Key
Laboratory of Carbonaceous Wastes Processing and Process Intensification
of Zhejiang Province, University of Nottingham
Ningbo, Ningbo 315100, China
- Edith Cowan
University School of Engineering, 270 Joondalup Drive, Joondalup, WA 6027, Australia
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Zhang W, Zhou N, Zhang Y, Huang Z, Hu H, Liang J, Qin Y. Construction of thermoplastic cellulose esters matrix composites with enhanced flame retardancy and mechanical properties by embedding hydrophobic magnesium hydroxide. J Appl Polym Sci 2021. [DOI: 10.1002/app.50677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Wuxiang Zhang
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Nan Zhou
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Yanjuan Zhang
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Zuqiang Huang
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Huayu Hu
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Jing Liang
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
| | - Yuben Qin
- School of Chemistry and Chemical Engineering Guangxi University Nanning China
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