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property-predictions
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Learning Molecular Properties Using Deep Learning
, M.Sc. Thesis Sharif University of Technology ; Hossein Khalaj, Babak (Supervisor)
Abstract
Design and production of a drug is a very time and money consuming process. It takes more than a decade and about 2.5 million dollars on various stages to design a drug. Attempts to reduce this cost and time to market will make drugs available to customers at a more reasonable time. Some stages such as animal testing phase and clinical trials, can not be replaced and must take place in practice. Fortunately, some laboratory steps are interchangeable with software algorithms. These algorithms can significantly reduce the cost and time to market of the drug if they are accurate enough. On the other hand, the remarkable results of machine learning, in particular, Deep Neural Networks, in areas...
Non-Destructive estimation of physicochemical properties and detection of ripeness level of apples using machine vision
, Article International Journal of Fruit Science ; Volume 22, Issue 1 , 2022 , Pages 628-645 ; 15538362 (ISSN) ; Nadimi, M ; Abbaspour Gilandeh, Y ; Paliwal, J ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
Nondestructive estimation of physicochemical properties, post-harvest physiology, and level of ripeness of fruits is essential to their automated harvesting, sorting, and handling. Recent research efforts have identified machine vision systems as a promising noninvasive nondestructive tool for exploring the relationship between physicochemical and appearance characteristics of fruits at various ripening levels. In this regard, the purpose of the current study is to provide an intelligent algorithm for estimating two physical properties including firmness, and soluble solid content (SSC), three chemical properties viz. starch, acidity, and titratable acidity (TA), as well as detection of the...
Insights on the speed of sound in ionic liquid binary mixtures: Investigation of influential parameters and construction of predictive models
, Article Journal of Molecular Liquids ; Volume 326 , 2021 ; 01677322 (ISSN) ; Salimi, M ; Iranshahi, D ; Sharif University of Technology
Elsevier B.V
2021
Abstract
Relative novelty and wide functionality of ionic liquids (ILs) have led to a surge in the studies devoted to experimental determination of their physicochemical properties. Systematic collection and analysis of the available data and development of predictive models to address the extreme diversity of IL systems are of great value in this regard. In the present work, the significance of speed of sound in ILs and their mixtures was outlined and related theoretical concepts were surveyed. A comprehensive database was utilized for the construction of predictive models based on least square support vector machine. By constructing four different models, the influence of temperature, molecular...
A new multi-sample EOS model for the gas condensate phase behavior analysis
, Article Oil and Gas Science and Technology ; Volume 66, Issue 6 , September , 2011 , Pages 1025-1033 ; 12944475 (ISSN) ; Crespo, F ; Sharif University of Technology
2011
Abstract
Equations of State EOS are vastly being used to predict the phase behavior of reservoir fluids. The accuracy of EOS modeling technique over conventional correlation models would benefit an improved property prediction of these fluids. Once the crude oil or gas condensate fluid system has been probably characterized using limited laboratory tests, its PVT behavior under a variety of conditions can be easily studied. In this paper, the PVT behavior of gas condensate from a reservoir in South Pars retrograde gas field in Iran was modeled using the three-parameter Patel and Teja Equation of State. The multi-sample characterization method is used to arrive at one consistent model for retrograde...