Kinetic parameters estimation and reaction model modification for thermal degradation of Beizao oil shale based on thermogravimetric analysis coupled with deconvolution procedure

Energy ◽  
2021 ◽  
pp. 120791
Author(s):  
Juan Zhang ◽  
Lulu Sun ◽  
Jiaqing Zhang ◽  
Yanming Ding ◽  
Wenlu Chen ◽  
...  
Keyword(s):  
Thermogravimetric Analysis ◽  
Thermal Degradation ◽  
Kinetic Parameters ◽  
Oil Shale ◽  
Reaction Model ◽  
Parameters Estimation ◽  
Model Modification ◽  
Kinetic Parameters Estimation

scholarly journals Pyrolysis Kinetic Parameters of Omari Oil Shale Using Thermogravimetric Analysis

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4060
Author(s):  
Ziad Abu El-Rub ◽  
Joanna Kujawa ◽  
Samer Al-Gharabli
Keyword(s):  
Activation Energy ◽  
Thermogravimetric Analysis ◽  
Kinetic Parameters ◽  
Oil Shale ◽  
Arrhenius Plot ◽  
Frequency Factor ◽  
Coefficient Of Determination ◽  
Assay Method ◽  
Temperature Integral ◽  
Integral Approximation

Oil shale is one of the alternative energies and fuel solutions in Jordan because of the scarcity of conventional sources, such as petroleum, coal, and gas. Oil from oil shale reservoirs can be produced commercially by pyrolysis technology. To optimize the process, mechanisms and rates of reactions need to be investigated. Omari oil shale formation in Jordan was selected as a case study, for which no kinetic models are available in the literature. Oil shale was analyzed using the Fischer assay method, proximate analysis (moisture, volatile, and ash), gross calorific value, elemental analysis (CHNS), and X-ray fluorescence (XRF) measurements. Non-isothermal thermogravimetric analysis was applied to study the kinetic parameters (activation energy and frequency factor) at four selected heating rates (5, 10, 15, and 20 °C/min). When oil shale was heated from room temperature to 1100 °C, the weight loss profile exhibited three different zones: drying (devolatilization), pyrolysis, and mineral decomposition. For each zone, the kinetic parameters were calculated using three selected methods: integral, temperature integral approximation, and direct Arrhenius plot. Furthermore, the activation energy in the pyrolysis zone was 112–116 kJ/mol, while the frequency factor was 2.0 × 107 − 1.5 × 109 min−1. Moreover, the heating rate has a directly proportional relationship with the rate constant at each zone. The three different methods gave comparable results for the kinetic parameters with a higher coefficient of determination (R2) for the integral and temperature integral approximation compared with the direct Arrhenius plot. The determined kinetic parameters for Omari formation can be employed in developing pyrolysis reactor models.

scholarly journals An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 963
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Muhammad Akmal Remli ◽  
Noorlin Mohd Ali ◽  
Kohbalan Moorthy ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Metabolic Model ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Distance Minimization ◽  
Kinetic Parameters Estimation

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

A knowledge based approach for elastomer cure kinetic parameters estimation

2013 ◽  
Vol 32 (1) ◽  
pp. 9-14 ◽  
Author(s):  
Eric Leroy ◽  
Anouar Souid ◽  
Alain Sarda ◽  
Rémi Deterre
Keyword(s):  
Kinetic Parameters ◽  
Parameters Estimation ◽  
Knowledge Based ◽  
Cure Kinetic ◽  
Kinetic Parameters Estimation
Sign in / Sign up

Export Citation Format

Share Document