Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

M Abdipour, SHR Ramazani… - Journal of the …, 2018 - Wiley Online Library
Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content
(OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability …

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear …

M Sabzi-Nojadeh, G Niedbała… - Agriculture, 2021 - mdpi.com
Foeniculum vulgare Mill.(commonly known as fennel) is used in the pharmaceutical,
cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue …

Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening

J Fang, S Yang, M Xie, J Hu, H Sun, G Liu, S Zhao… - Applied Materials …, 2024 - Elsevier
A machine learning strategy is proposed based on the demand for prediction of solid-liquid
phase transition temperature properties of multi-component precious metal alloys. Firstly, the …

Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies

SB Ghugare, SS Tambe - Journal of the Energy Institute, 2017 - Elsevier
The higher heating value (HHV) is the most important indicator of a coal's potential energy
yield. It is commonly used in the efficiency and optimal design calculations pertaining to the …

Quantitative structure property relationship schemes for estimation of autoignition temperatures of organic compounds

A Dashti, M Jokar, F Amirkhani… - Journal of Molecular …, 2020 - Elsevier
We have extended a quantitative structure–property relationship (QSPR) scheme to estimate
the auto-ignition temperatures (AIT) of organic compounds by employing GA-ANFIS, PSO …

Prediction of coal ash fusion temperatures using computational intelligence based models

SS Tambe, M Naniwadekar, S Tiwary… - International Journal of …, 2018 - Springer
In the coal-based combustion and gasification processes, the mineral matter contained in
the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly …

Prediction of API values of crude oils by use of saturates/aromatics/resins/asphaltenes analysis: computational-intelligence-based models

P Goel, K Saurabh, V Patil-Shinde, SS Tambe - SPE Journal, 2017 - onepetro.org
The° API value is an important physicochemical characteristic of crude oils often used in
determining their properties and quality. There exist models—predominantly linear ones …

AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence

LY Geer, SE Stein, WG Mallard… - Journal of Chemical …, 2024 - ACS Publications
The Kováts retention index (RI) is a quantity measured using gas chromatography and is
commonly used in the identification of chemical structures. Creating libraries of observed RI …

Co-gasification of high ash coal–biomass blends in a fluidized bed Gasifier: Experimental study and computational intelligence-based modeling

S Tiwary, SB Ghugare, PD Chavan, S Saha… - Waste and Biomass …, 2020 - Springer
Co-gasification (COG) is a clean-coal technology that uses a binary blend of coal and
biomass for generating the product gas; it is environment-friendly since it emits lesser …

Genetic programming based models for prediction of vapor-liquid equilibrium

V Patil-Shinde, SS Tambe - Calphad, 2018 - Elsevier
The design, operation, and control of chemical separation processes heavily rely on the
knowledge of the vapor-liquid equilibrium (VLE). Often, conducting experiments to gain an …