Open AccessSimulation and optimization integrated gasification combined cycle by used aspen hysys and aspen plus
Author(s) -
Mohsen Darabi,
Mohammad Mohammadiun,
Hamid Mohammadiun,
Saeed Mortazavi,
Mostafa Montazeri
Publication year - 2015
Publication title -
international journal of scientific world
Language(s) - English
Resource type - Journals
ISSN - 2307-9037
DOI - 10.14419/ijsw.v3i1.4583
Subject(s) - integrated gasification combined cycle , process engineering , coal , combined cycle , power station , environmental science , syngas , process simulation , fossil fuel , steam turbine , electricity generation , benchmark (surveying) , work (physics) , waste management , turbine , engineering , process (computing) , computer science , power (physics) , mechanical engineering , hydrogen , chemistry , physics , electrical engineering , organic chemistry , quantum mechanics , geodesy , operating system , geography
Electricity is an indispensable amenity in present society. Among all those energy resources, coal is readily available all over the world and has risen only moderately in price compared with other fuel sources. As a result, coal-fired power plant remains to be a fundamental element of the world's energy supply. IGCC, abbreviation of Integrated Gasification Combined Cycle, is one of the primary designs for the power-generation market from coal-gasification. This work presents a in the proposed process, diluted hydrogen is combusted in a gas turbine. Heat integration is central to the design. Thus far, the SGR process and the HGD unit are not commercially available. To establish a benchmark. Some thermodynamic inefficiencies were found to shift from the gas turbine to the steam cycle and redox system, while the net efficiency remained almost the same. A process simulation was undertaken, using Aspen Plus and the engineering equation solver (EES).The The model has been developed using Aspen Hysys® and Aspen Plus®. Parts of it have been developed in Matlab, which is mainly used for artificial neural network (ANN) training and parameters estimation. Predicted results of clean gas composition and generated power present a good agreement with industrial data. This study is aimed at obtaining a support tool for optimal solutions assessment of different gasification plant configurations, under different input data sets.