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首页> 外文会议>2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications >Multiple Input-Single Output (MISO) Feedforward Artificial Neural Network (FANN) Models for Pilot Plant Binary Distillation Column
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Multiple Input-Single Output (MISO) Feedforward Artificial Neural Network (FANN) Models for Pilot Plant Binary Distillation Column

机译：中试二元蒸馏塔的多输入单输出（MISO）前馈人工神经网络（FANN）模型

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摘要

Distillations column control becomes the main subject of control research due to the intensive energy usage in the industry and the nonlinearity behavior in control variables. The growing importance of "green technology" and sustainability has triggered researchers to focus on this matter. Therefore, a method of modeling and controlling of the column is certainly indispensible in this matter. Neural networks are a powerful tool especially in modeling nonlinear and intricate process. Hence, in this paper Feed forward Artificial Neural network (FANN) have been chosen to model the multiple input-single output (MISO) for the distillation column predicting top and bottom composition. The performance and the accuracy of the models have been presented in term of correlation coefficient (R value) and the smallest sum squared error (SSE). It has been found that FANN can model MISO in representing the process. The results obtained also show that the MISO model is suitable to be used to represent the distillation process accurately.
机译：由于工业中大量的能源消耗和控制变量的非线性行为，蒸馏塔的控制成为控制研究的主要课题。 “绿色技术”和可持续性的重要性日益提高，促使研究人员将重点放在这一问题上。因此，在此问题上，建模和控制色谱柱的方法无疑是必不可少的。神经网络是一个强大的工具，尤其是在对非线性和复杂过程进行建模时。因此，在本文中，已选择前馈人工神经网络（FANN）对蒸馏塔的多输入单输出（MISO）进行建模，以预测顶部和底部组成。已经用相关系数（R值）和最小平方和误差（SSE）表示了模型的性能和准确性。已经发现，FANN可以在表示过程的过程中对MISO进行建模。获得的结果还表明，MISO模型适用于准确表示蒸馏过程。

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