Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Keyword
-
- API solid state (1)
- AlphaFold (1)
- Artificial Neural Network; CCRRP; Modeling; Reformate Yield; Research Octane Number; Simulation (1)
- Cocrystal (1)
- Crystallization (1)
-
- Eterification (1)
- Ethyl acetate (1)
- Heаt integrаtion (1)
- Machine learning (1)
- Modeling (1)
- Molecular dynamics simulation (1)
- Non-ionic deep eutectic solvents (1)
- Polymorphism (1)
- Protein language models (1)
- Protein structures (1)
- Reаctive Distillаtion (1)
- Solubility (1)
- Spherical crystallization (1)
- UNIFАC (1)
- Аspen Plus (1)
- Publication
- Publication Type
Articles 1 - 4 of 4
Full-Text Articles in Other Chemical Engineering
Study Of Reactive Distillation Equipment For Eterification Process, Abbos Elmanov, Abdulaziz Bakhtiyorov, Adham Norkobilov, Olimjon Maksudov, Zarif Oripov
Study Of Reactive Distillation Equipment For Eterification Process, Abbos Elmanov, Abdulaziz Bakhtiyorov, Adham Norkobilov, Olimjon Maksudov, Zarif Oripov
CHEMISTRY AND CHEMICAL ENGINEERING
The purpose of this work is to study the chemical technological processes for eterification processes, and the reactive distillation unit, which carries out the reaction and separation process in one device, was chosen as the object of research. А reаctive distillаtion system is exаmined to produce for the production of Ethyl Acetate (EtAc) viа eterificаtion of acetic acid (HAc) with ethаnol. Optimizаtion methods using sensitivity analysis аre аlso conducted. Cаlculаtion of vаpor liquid equilibrium of the mixture system is done using UNIFАC model. Heat integration from the distillate stream of the column to the acid feed stream and from bottom …
Artificial Neural Network Modeling Applied For Predicting Reformate Yield And Research Octane Number In The Reforming Process, Badiea S. Babaqi, Abdelrigeeb Ali Al-Gathe, Mohd S. Takriff, Hassimi Abu Hasan, Mohammed H. Al-Douh
Artificial Neural Network Modeling Applied For Predicting Reformate Yield And Research Octane Number In The Reforming Process, Badiea S. Babaqi, Abdelrigeeb Ali Al-Gathe, Mohd S. Takriff, Hassimi Abu Hasan, Mohammed H. Al-Douh
Hadhramout University Journal of Natural & Applied Sciences
The prediction model of the continuous catalytic regeneration reforming process was developed for expecting the reformate yield and research octane number using an Artificial Neural Network technique (ANN) to improve the process performance. The proposed model includes temperatures, pressures, and hydrogen to hydrocarbon molar ratio as input parameters while the output of the process represents reformate yield and research octane number. The ANN model was carried out to estimate the process behavior based on the Levenberg-Marquardt Algorithm, which included the nine input parameters, two hidden layers (10-5 neurons), and two parameters as network outputs. The results obtained were that the …
Design Of Active Pharmaceutical Ingredients Solid States In Crystallization Processes, Weizhong Gong
Design Of Active Pharmaceutical Ingredients Solid States In Crystallization Processes, Weizhong Gong
Electronic Thesis and Dissertation Repository
Crystallization is an important technique to obtain solid-state drugs from solutions. Physicochemical properties of the active pharmaceutical ingredients (APIs) are determined by crystallization. More than half of the active pharmaceutical ingredients exhibit polymorphism, the phenomenon of chemical species showing more than one unit-cell structure in the solid state. Controlling polymorphism is one of the most important goals during pharmaceutical manufacturing. Nevertheless, the control of polymorphism is sometimes not enough to realize the targeted physicochemical properties. Suitable additives (coformers/salt formers) are explored to generate new multi-component solid phases of poorly soluble/bioavailable active pharmaceutical ingredients (APIs). The design of pharmaceutical cocrystals and …
Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas
Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas
Theses and Dissertations--Chemical and Materials Engineering
Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …