Fouling modeling and prediction approach for heat exchangers using deep learning

Sreenath Sundar, Manjunath C. Rajagopal, Hanyang Zhao, Gowtham Kuntumalla, Yuquan Meng, Ho Chan Chang, Chenhui Shao, Placid Ferreira, Nenad Miljkovic, Sanjiv Sinha, Srinivasa Salapaka

Research output: Contribution to journalArticle

Abstract

In this article, we develop a generalized and scalable statistical model for accurate prediction of fouling resistance using commonly measured parameters of industrial heat exchangers. This prediction model is based on deep learning where a scalable algorithmic architecture learns non-linear functional relationships between a set of target and predictor variables from large number of training samples. The efficacy of this modeling approach is demonstrated for predicting fouling in an analytically modeled cross-flow heat exchanger, designed for waste heat recovery from flue-gas using room temperature water. The performance results of the trained models demonstrate that the mean absolute prediction errors are under 10−4KW−1 for flue-gas side, water side and overall fouling resistances. The coefficients of determination (R2), which characterize the goodness of fit between the predictions and observed data, are over 99%. Even under varying levels of measurement noise in the inputs, we demonstrate that predictions over an ensemble of multiple neural networks achieves better accuracy and robustness to noise. We find that the proposed deep-learning fouling prediction framework learns to follow heat exchanger flow and heat transfer physics, which we confirm using locally interpretable model agnostic explanations around randomly selected operating points. Overall, we provide a robust algorithmic framework for fouling prediction that can be generalized and scaled to various types of industrial heat exchangers.

Original languageEnglish (US)
Article number120112
JournalInternational Journal of Heat and Mass Transfer
Volume159
DOIs
StatePublished - Oct 2020

Keywords

  • Bagging
  • Cross-flow heat exchanger
  • Deep learning
  • Ensemble model
  • Fouling modeling and prediction
  • Heat exchanger fouling
  • Neural networks
  • Waste heat recovery

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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