Thermodynamic Study of Improvements of Various Methods of Predicting Vapor Pressure of Different Materials
- سال انتشار: 1405
- محل انتشار: فصلنامه پیشرفت در تحقیقات بیوشیمی و شیمی، دوره: 9، شماره: 1
- کد COI اختصاصی: JR_PCBR-9-1_004
- زبان مقاله: انگلیسی
- تعداد مشاهده: 26
نویسندگان
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
Department of Chemical Engineering, Arv.C., Islamic Azad University, Abadan, Iran
چکیده
The accurate prediction of vapor pressure is a cornerstone in thermodynamic mod-eling, with wide-ranging applications across chemical engineering, environmental science, and materials development. This study presents a comprehensive thermo-dynamic investigation into the enhancement of existing methods for predicting va-por pressure across a broad spectrum of chemical substances, including conven-tional organics, polymers, and emerging advanced materials such as ionic liquids and metal-organic frameworks. Current predictive models—ranging from empirical equations and group contribution methods to corresponding states theory and mo-lecular simulations—exhibit varying degrees of accuracy, generalizability, and computational demand. However, each approach is constrained by limitations such as narrow applicability, reliance on extensive experimental data, or high computa-tional costs. This research aims to bridge these gaps by integrating refined thermo-dynamic models, advanced statistical correlations, and data-driven machine learn-ing techniques to improve both the accuracy and flexibility of vapor pressure pre-dictions. The study evaluates the performance of enhanced models using extensive benchmark datasets, incorporating compounds with diverse molecular structures and thermodynamic behaviors. It explores improvements in hybrid models that couple group contribution and equations of state frameworks, and introduces un-certainty quantification through Bayesian analysis to assess predictive reliability. Results demonstrate that thermodynamically-informed, machine-learning-augmented models outperform traditional methods across a wide range of sub-stances and temperature conditions. These advancements have significant implica-tions for the design of safer, more efficient, and environmentally sustainable chemi-cal processes. The findings contribute to the evolving field of predictive thermody-namics by offering scalable, accurate, and computationally feasible tools for vapor pressure estimation in both academic and industrial contexts.کلیدواژه ها
vapor pressure, Fluids, Oil, Mathematical Relationships, Thermodynamic Behaviorsاطلاعات بیشتر در مورد COI
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