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首页> 外文期刊>Energy sources >Lean Amine Concentration Prediction Based on Computational Intelligences as Artificial Neural Networks (ANNs) in Gas Sweetening Processing Units
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Lean Amine Concentration Prediction Based on Computational Intelligences as Artificial Neural Networks (ANNs) in Gas Sweetening Processing Units

机译：气体甜味处理装置中基于人工神经网络（ANN）的计算智能的精胺浓度预测

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摘要

Gas sweetening is a fundamental step in gas treatment processes. In gas sweetening units, acid gases (H_2S and CO_2) are chemically absorbed from a gas using aqueous alkanolamine solutions, to produce a "sweet gas." The solvent is regenerated in a desorption column and the purified (or "lean") solvent is recycled to the absorption column. Gas sweetening units can be controlled if all of the operation data, for example, sweet gas, lean amine, and rich amine flow rates, concentration, and temperatures existed. In this article, a new method based on artificial neural network for prediction of lean amine concentration is presented. H_2S, H_2O, CO_2, and diethanolamine mole fractions in sour gas and sweet gas have been input as variables of the network and have been set as network output. Among the 130 data set, 92 data have been implemented to find the best artificial neural network structure as train data. Moreover, 19 data have been used to check the generalization capability of the trained artificial neural network named validation data and 19 data have been used to test an optimized network as test data. These predictions can prevent operation problems. The results, according to R value and mean squared error, show good accuracy of this type of modeling.
机译：气体脱硫是气体处理过程中的基本步骤。在气体脱硫装置中，使用链烷醇胺水溶液将酸性气体（H_2S和CO_2）从气体中化学吸收，从而产生“甜气体”。溶剂在解吸塔中再生，纯化的（或“贫”）溶剂循环到吸收塔。如果存在所有操作数据（例如，甜味气，稀胺和浓胺的流速，浓度和温度），则可以控制气体脱硫装置。本文提出了一种基于人工神经网络的稀胺浓度预测新方法。已将酸气和甜气中的H_2S，H_2O，CO_2和二乙醇胺摩尔分数作为网络变量输入，并设置为网络输出。在130个数据集中，已实施92个数据以找到最佳的人工神经网络结构作为火车数据。此外，已经使用19个数据来检查训练后的人工神经网络（称为验证数据）的泛化能力，并且使用19个数据来测试优化网络作为测试数据。这些预测可以防止操作问题。根据R值和均方误差得出的结果表明，此类建模具有良好的准确性。

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