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Improvement of a cement rotary kiln performance using artificial neural network

机译:利用人工神经网络改进水泥旋转窑性能

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In order to investigate the effect of parameters and system optimization, the processes must be modeled first. Cement rotary kiln systems are complex because of non-linear, time invariant and full of behavioral uncertainty where the mathematical modeling of the plant is impossible. Artificial neural network (ANN) is one of the best tools for improving the performance of such processes. In this study, the operational data from a cement factory are gathered and the relationships between variables analyzed via using ANN via MATLAB toolbox. ANN proposed 2.7 and 865 rpm for kiln and fan motor speed respectively and 4599.7 Ncm/h for total grate flowrate as optimum values. This research shows that using ANN for improving the performance of rotary kiln is effective and by optimization of operational parameters through ANN and applying them in the rotary kiln, higher production in the cement industry is accessible.
机译:为了调查参数和系统优化的影响,必须首先建模过程。 由于非线性,时间不变,并且充满了工厂的数学建模的行为不确定性,水泥旋转窑系统很复杂。 人工神经网络(ANN)是提高这些过程性能的最佳工具之一。 在本研究中,聚集来自水泥厂的操作数据以及通过MATLAB工具箱通过ANN分析的变量之间的关系。 ANN提出2.7和865 rpm,用于窑和风扇电机速度,总炉速率为4599.7 ncm / h作为最佳值。 本研究表明,使用ANN用于提高旋转窑的性能是有效的,通过ANN的操作参数优化并在旋转窑中应用,水泥工业的更高产量可获得。

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