Mathematical and artificial neural network models for corrosion of high temperature — High pressure boiler pipes in presence of oxygen scavengers

机译：高温腐蚀的数学和人工神经网络模型—存在除氧剂的高压锅炉管道

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This research reports the consequences of mass loss estimations by utilizing of oxygen scavengers in boilers to limit the consumption rate of carbon steel tubes. The consumption rate information was chosen from the literature as function of temperature, pressure, and inhibitors level. Consumption rate increased with increasing in temperature and pressure, and diminished with inhibitor level. Hydroquinone, ascorbic acid, and ethanolamine were utilized as consumption inhibitors. The present work is focused on deciding the artificial neural network (ANN) and mathematical formulas so as to increase great expectation properties. Five scientific models and five ANN display structures were recommended. Computer aided program was utilized for building up these models. The outcomes demonstrate that the polynomial mathematical model and multi-layer discernment can precisely anticipate the deliberate information with high relationship coefficients. Multi-Layer Perceptions (MLP) 3:3-4-1:1 was the best model over than the others with higher correlation coefficient.

机译：这项研究报告了通过利用锅炉中的除氧剂来限制碳钢管的消耗率来估计质量损失的后果。消耗率信息是根据温度，压力和抑制剂含量的函数从文献中选择的。消耗速率随温度和压力的升高而增加，而随抑制剂水平的降低而降低。对苯二酚，抗坏血酸和乙醇胺被用作消耗抑制剂。目前的工作集中在确定人工神经网络（ANN）和数学公式，以增加很大的期望值。建议使用五个科学模型和五个ANN显示结构。利用计算机辅助程序来建立这些模型。结果表明，多项式数学模型和多层识别可以准确预测具有高关系系数的故意信息。多层感知（MLP）3：3-4-1：1是比其他具有更高相关系数的模型更好的模型。

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来源
《2018 1st International Scientific Conference of Engineering Sciences - 3rd Scientific Conference of Engineering Science》|2018年|140-145|共6页
会议地点 Diyala(IQ)
作者
Anees A. Khadom; Abdul Munem A. Karim; Salah N. Farhan;
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作者单位

Department of Chemical Engineering, College of Engineering, University of Diyala, Diyala, Iraq;

Department of Chemical Engineering, College of Engineering, University of Diyala, Diyala, Iraq;

Department of Chemical Engineering, College of Engineering, University of Diyala, Diyala, Iraq;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Mathematical model; Corrosion; Artificial neural networks; Neurons; Standards; Inhibitors; Indexes;

机译：数学模型;腐蚀;人工神经网络;神经元;标准;抑制剂;指标;;

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4. Mathematical and artificial neural network models for corrosion of high temperature — High pressure boiler pipes in presence of oxygen scavengers [C] . Anees A. Khadom, Abdul Munem A. Karim, Salah N. Farhan International Scientific Conference of Engineering Sciences . 2018

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Mathematical and artificial neural network models for corrosion of high temperature — High pressure boiler pipes in presence of oxygen scavengers

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