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|Title: ||Towards more accurate and reliable mathematical model for steam gasification [Abstract]|
|Authors: ||Chiarasumran, Nutchapon|
Blanchard, Richard E.
|Issue Date: ||2017|
|Citation: ||CHIARASUMRAN, N., BLANCHARD, R.E. and BENYAHIA, B., 2017. Towards more accurate and reliable mathematical model for steam gasification. Presented at the 2nd International Conference on Advanced Energy Materials (AEM 2017), University of Surrey, Guildford, September 11-13th.|
|Abstract: ||Over the last decade, renewable energy has undergone unprecedented growth and development because of the increasing need for cleaner and more environmental friendly technologies. Hydrogen is one of the most important renewable energy that can be employed in many applications e.g. fuel cell technologies. Biomass, including municipal solid waste, is also a potential option for producing hydrogen and they can be converted through the thermochemical process particularly gasification, which is a technology that can produce H2 on both small and large scales using just air or steam as the oxidizing agents. Developing accurate and highly predictable model for the gasification is essential for process design and optimization. In order to predict the product gas composition and the amount of hydrogen produced from the gasification process precisely, the simulation of the mathematical models to present the behaviors and properties of gases is needed to be developed. Different types of gasification models are used to predict the maximum yield of the product gases based on thermodynamic equilibrium1. In addition, different simulators have been developed, such as Aspen Plus model type, but still not predictable enough compared to the experimental data2. The objective of this work is to develop more precise equilibrium model for the biomass steam gasification using Gibb’s energy minimization and Lagrange multiplier method. A rigorous and systematic procedure was developed to improve parameter estimation, using global optimization approach, which enhances the prediction capabilities of the mathematical model.|
|Description: ||This is an abstract which accompanies a poster presented at AEM 2017.|
|Version: ||Accepted for publication|
|Publisher Link: ||http://www.aem2017.com/|
|Appears in Collections:||Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)|
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