Analysis of heat transfer fouling data using artificial neural networks

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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.