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首页> 外文期刊>International Journal of Scientific World >Simulation and optimization integrated gasification combined cycle by used aspen hysys and aspen plus
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Simulation and optimization integrated gasification combined cycle by used aspen hysys and aspen plus

机译：使用Aspen Hysys和Aspen Plus进行模拟和优化集成气化联合循环

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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.
机译：电力是当今社会必不可少的设施。在所有这些能源中，煤炭在世界各地都很容易获得，与其他燃料相比，价格仅适度上涨。结果，燃煤电厂仍然是世界能源供应的基本要素。 IGCC是“整体气化联合循环”的缩写，是煤气化发电市场的主要设计之一。这项工作提出了一种建议的方法，将稀释的氢气在燃气轮机中燃烧。热集成对于设计至关重要。到目前为止，SGR工艺和HGD装置尚无法商购。建立基准。发现一些热力学效率低下从燃气轮机转向蒸汽循环和氧化还原系统，而净效率几乎保持不变。使用Aspen Plus和工程方程求解器（EES）进行了过程仿真。该模型是使用Aspen Hysys？开发的。和Aspen Plus？它的一部分已在Matlab中开发，主要用于人工神经网络（ANN）训练和参数估计。清洁气体成分和发电量的预测结果与工业数据吻合良好。这项研究旨在获得一种支持工具，用于根据不同的输入数据集评估不同气化厂配置的最佳解决方案。

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