A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization. Hsiao, Y. & Yen, H. Fuel, 380:133144, January, 2025. ![A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Pyrolysis fuel oil from an industrial naphtha cracker was used to prepare isotropic pitch precursors (IPPs) in a kilogram-scale air-blowing process. Due to its intrinsic nature of batch operations, the available data for model development is extremely limited, making data-driven techniques nearly inapplicable. Therefore, in this work, a novel semi-empirical model based on the first order rate law was proposed and developed to inference the softening points (SPs) of IPPs. The model was proven to be able to accurately predict the SPs by the given operating procedures in a prior to any new batch. Although the number of modeling samples were limited, with the physically interpretable model structure, the proposed model possesses good prediction and extrapolating performances. The model also showed its effectiveness to guide the constrained makespan minimization, and it was validated by real operations that over 25 % of time and utilities were saved for preparing IPPs with same SP level.
@article{hsiao_novel_2025,
title = {A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization},
volume = {380},
issn = {0016-2361},
url = {https://www.sciencedirect.com/science/article/pii/S0016236124022932},
doi = {10.1016/j.fuel.2024.133144},
abstract = {Pyrolysis fuel oil from an industrial naphtha cracker was used to prepare isotropic pitch precursors (IPPs) in a kilogram-scale air-blowing process. Due to its intrinsic nature of batch operations, the available data for model development is extremely limited, making data-driven techniques nearly inapplicable. Therefore, in this work, a novel semi-empirical model based on the first order rate law was proposed and developed to inference the softening points (SPs) of IPPs. The model was proven to be able to accurately predict the SPs by the given operating procedures in a prior to any new batch. Although the number of modeling samples were limited, with the physically interpretable model structure, the proposed model possesses good prediction and extrapolating performances. The model also showed its effectiveness to guide the constrained makespan minimization, and it was validated by real operations that over 25 \% of time and utilities were saved for preparing IPPs with same SP level.},
urldate = {2024-10-13},
journal = {Fuel},
author = {Hsiao, Yu-Da and Yen, Hung-Yu},
month = jan,
year = {2025},
keywords = {Arrhenius equation, first order rate law, mentions sympy, semi-empirical model, softening point},
pages = {133144},
}
Downloads: 0
{"_id":"Dtyw4tZbPSzxjPdZR","bibbaseid":"hsiao-yen-anovelsemiempiricalkineticmodelforpredictingsofteningpointsofpitchprecursorsanditsapplicationtoprocessoptimization-2025","author_short":["Hsiao, Y.","Yen, H."],"bibdata":{"bibtype":"article","type":"article","title":"A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization","volume":"380","issn":"0016-2361","url":"https://www.sciencedirect.com/science/article/pii/S0016236124022932","doi":"10.1016/j.fuel.2024.133144","abstract":"Pyrolysis fuel oil from an industrial naphtha cracker was used to prepare isotropic pitch precursors (IPPs) in a kilogram-scale air-blowing process. Due to its intrinsic nature of batch operations, the available data for model development is extremely limited, making data-driven techniques nearly inapplicable. Therefore, in this work, a novel semi-empirical model based on the first order rate law was proposed and developed to inference the softening points (SPs) of IPPs. The model was proven to be able to accurately predict the SPs by the given operating procedures in a prior to any new batch. Although the number of modeling samples were limited, with the physically interpretable model structure, the proposed model possesses good prediction and extrapolating performances. The model also showed its effectiveness to guide the constrained makespan minimization, and it was validated by real operations that over 25 % of time and utilities were saved for preparing IPPs with same SP level.","urldate":"2024-10-13","journal":"Fuel","author":[{"propositions":[],"lastnames":["Hsiao"],"firstnames":["Yu-Da"],"suffixes":[]},{"propositions":[],"lastnames":["Yen"],"firstnames":["Hung-Yu"],"suffixes":[]}],"month":"January","year":"2025","keywords":"Arrhenius equation, first order rate law, mentions sympy, semi-empirical model, softening point","pages":"133144","bibtex":"@article{hsiao_novel_2025,\n\ttitle = {A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization},\n\tvolume = {380},\n\tissn = {0016-2361},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0016236124022932},\n\tdoi = {10.1016/j.fuel.2024.133144},\n\tabstract = {Pyrolysis fuel oil from an industrial naphtha cracker was used to prepare isotropic pitch precursors (IPPs) in a kilogram-scale air-blowing process. Due to its intrinsic nature of batch operations, the available data for model development is extremely limited, making data-driven techniques nearly inapplicable. Therefore, in this work, a novel semi-empirical model based on the first order rate law was proposed and developed to inference the softening points (SPs) of IPPs. The model was proven to be able to accurately predict the SPs by the given operating procedures in a prior to any new batch. Although the number of modeling samples were limited, with the physically interpretable model structure, the proposed model possesses good prediction and extrapolating performances. The model also showed its effectiveness to guide the constrained makespan minimization, and it was validated by real operations that over 25 \\% of time and utilities were saved for preparing IPPs with same SP level.},\n\turldate = {2024-10-13},\n\tjournal = {Fuel},\n\tauthor = {Hsiao, Yu-Da and Yen, Hung-Yu},\n\tmonth = jan,\n\tyear = {2025},\n\tkeywords = {Arrhenius equation, first order rate law, mentions sympy, semi-empirical model, softening point},\n\tpages = {133144},\n}\n\n\n\n\n\n\n\n","author_short":["Hsiao, Y.","Yen, H."],"key":"hsiao_novel_2025","id":"hsiao_novel_2025","bibbaseid":"hsiao-yen-anovelsemiempiricalkineticmodelforpredictingsofteningpointsofpitchprecursorsanditsapplicationtoprocessoptimization-2025","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0016236124022932"},"keyword":["Arrhenius equation","first order rate law","mentions sympy","semi-empirical model","softening point"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero-group/nicoguaro/525293","dataSources":["YtBDXPDiQEyhyEDZC"],"keywords":["arrhenius equation","first order rate law","mentions sympy","semi-empirical model","softening point"],"search_terms":["novel","semi","empirical","kinetic","model","predicting","softening","points","pitch","precursors","application","process","optimization","hsiao","yen"],"title":"A novel semi-empirical kinetic model for predicting softening points of pitch precursors and its application to process optimization","year":2025}