Analysis of heat transfer fouling data using artificial neural networks

使用人工神经网络分析传热污垢数据

• 批准号: 5454817
• 负责人: Professor Dr.-Ing. Hans Müller-Steinhagen
• 金额: --
• 依托单位: DIU - Dresden International University
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2005
• 资助国家: 德国
• 起止时间: 2004-12-31 至 2007-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/5454817?language=en
• 关键词: Analysis; heat; transfer; fouling; data

The process-related formation of deposits on heat transfer surfaces is the least understood phenomenon in design and operation of heat transfer equipment. Part of this unsatisfactory situation must be blamed on the inadequacy of conventional regression methods to correlate experimental data with an ill-distributed parameter variation; e.g. the selection of independent variables based on limited experimental observations or solely because of their availability, and the lack of knowledge to justify whether the chosen regression equation is the best. The proposed research project endeavours i) to analyse several comprehensive data banks with fouling data from numerous sources, notably the unique, proprietary and hitherto unavailable HTRI (Heat Transfer Research Inc.) database and ii) to implement artificial neural networks trained on these data bases to correlate/predict the fouling behaviour. The present investigation will focus on the ubiquitous problem of scale formation from cooling water. If this work is successful, other generic fouling processes, such as crude oil fouling or milk fouling, may be investigated in subsequent projects. The original experimental fouling data and the related operating conditions will be converted into a working database consisting of dimensionless groups. It is important to select the fittest combination of dimensionless groups to be used as Artificial Neural Network inputs to predict fouling resistances. In addition to the identification of key parameters and their effects on scale formation, the trained neural network will be used to generate a well-structured and well-distributed data base, which can then be used to obtain correlations suitable for implementation into computer codes.

在传热设备的设计和操作中，与传热表面沉积的过程相关的形成是最不为人所知的现象。这种令人不满意的情况的部分原因必须归咎于传统回归方法在将实验数据与不均匀的参数变化联系起来方面的不足；例如，根据有限的实验观察或仅仅因为它们的可用性而选择自变量，以及缺乏证明所选回归方程是否最佳的知识。拟议的研究项目努力i)分析几个综合数据库，其中包含来自众多来源的污垢数据，特别是独特的，专有的和迄今为止不可用的HTRI（传热研究公司）数据库；ii)实现在这些数据库上训练的人工神经网络，以关联/预测污垢行为。目前的研究将集中在普遍存在的冷却水结垢问题上。如果这项工作是成功的，其他通用的污染工艺，如原油污染或牛奶污染，可能会在后续项目中进行研究。原始的实验污垢数据和相关的操作条件将被转换成一个由无因次分组组成的工作数据库。选择最合适的无量纲群组合作为人工神经网络输入来预测污垢阻力是很重要的。除了识别关键参数及其对尺度形成的影响外，训练后的神经网络还将用于生成结构良好、分布良好的数据库，然后可用于获得适合在计算机代码中实现的相关性。