Dehydration Kinetics of Ulexite by Thermogravimetric Data Using the Coats-Redfern and Genetic Algorithm Method

2003 ◽  
Vol 42 (15) ◽  
pp. 3642-3646 ◽  
Author(s):  
Hüseyin Okur ◽  
Çiǧdem Eymir
Keyword(s):  
Genetic Algorithm ◽  
Dehydration Kinetics ◽  
Thermogravimetric Data ◽  
Genetic Algorithm Method ◽  
Kinetics Of

Dehydration kinetics of ulexite from thermogravimetric data

1997 ◽  
Vol 48 (2) ◽  
pp. 403-411 ◽  
Author(s):  
M. TunÇ ◽  
H. ErŞahan ◽  
S. Yapici ◽  
S. Şolak
Keyword(s):  
Dehydration Kinetics ◽  
Thermogravimetric Data ◽  
Kinetics Of

scholarly journals Kinetic study of the regeneration of spent caustic via the genetic algorithm method

2018 ◽  
Vol 5 (4) ◽  
pp. 231-229 ◽  
Author(s):  
Asadollah Karimi ◽  
Esmaeil Fatehifar ◽  
Reza Alizadeh ◽  
Hadi Soltani
Keyword(s):  
Genetic Algorithm ◽  
Catalytic Oxidation ◽  
Bubble Column ◽  
Oxygen Demand ◽  
Bubble Column Reactor ◽  
Elementary Model ◽  
Genetic Algorithm Method ◽  
Spent Caustic ◽  
High Chemical Oxygen Demand ◽  
Kinetics Of

Background: Spent caustic contains noxious components such as sulfide species and also high chemical oxygen demand content (COD). Oxidation of these materials to caustic and sulfate species is mostly the rate-controlling step within catalytic oxidation of spent caustic. Methods: In this study, the kinetics of catalytic oxidation of spent caustic and the regeneration methodology of the sulfidic spent caustic were investigated. The kinetics of catalytic oxidation of spent caustic was studied in the presence of a heterogeneous catalyst. The developed mathematical model was verified via the batch bubble column reactor. The elementary and non-elementary models based on the genetic algorithm were used to obtain the rate coefficient and kinetic order. Results: The experiments were carried out at various conditions. The results indicated that the error of objective function of the non-elementary and elementary models was 3.01% and 134.96%, respectively. Conclusion: According to the results, the non-elementary model had rational outcome compared to the elementary one. Also, non-elemental model is more concordance with experimental results.

Dehydration kinetics of howlite, ulexite, and tunellite using thermogravimetric data

1999 ◽  
Vol 326 (1-2) ◽  
pp. 99-103 ◽  
Author(s):  
Yunus Erdoğan ◽  
Ayşe Zeybek ◽  
Ayşe Şahin ◽  
Ayhan Demirbaş
Keyword(s):  
Dehydration Kinetics ◽  
Thermogravimetric Data ◽  
Kinetics Of

scholarly journals Application of the genetic algorithm method in the problem of chemical kinetics of the oxidative regeneration process

2021 ◽  
Vol 2131 (2) ◽  
pp. 022007
Author(s):  
O V Dubinets ◽  
I M Gubaidullin ◽  
R M Uzyanbaev ◽  
M K Vovdenko ◽  
I G Lapshin
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Chemical Kinetics ◽  
Laboratory Installation ◽  
Complex Chemical ◽  
Oxidative Regeneration ◽  
Genetic Algorithm Method ◽  
Complex Chemical Reactions ◽  
Kinetics Of

Abstract Annotation. One of the main problems in chemical kinetics is the establishment of the mechanisms of complex chemical reactions. The inverse problem of chemical kinetics is understood as the determination of the dependence of the concentration of the participating components on the basis of experimental data obtained from a laboratory installation for the oxidative regeneration of coked catalysts. One of the main methods used in inverse problems the genetic algorithm. The algorithms considered in the article make it possible to determine the values of the rate constants of the considered chemical stages.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

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