Energy Science & Engineering (May 2024)

AutoML‐based predictive framework for predictive analysis in adsorption cooling and desalination systems

  • Jaroslaw Krzywanski,
  • Karol Sztekler,
  • Dorian Skrobek,
  • Karolina Grabowska,
  • Waqar Muhammad Ashraf,
  • Marcin Sosnowski,
  • Kashif Ishfaq,
  • Wojciech Nowak,
  • Lukasz Mika

DOI
https://doi.org/10.1002/ese3.1725
Journal volume & issue
Vol. 12, no. 5
pp. 1969 – 1986

Abstract

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Abstract Adsorption cooling and desalination systems have a distinct advantage over other systems that use low‐grade waste heat near ambient temperature. Since improving their performance, including reliability and failure prediction, is challenging, developing an efficient diagnostic system is of great practical significance. The paper introduces artificial intelligence (AI) and an automated machine learning approach (AutoML) in a real‐life application for a computational diagnostic system of existing adsorption cooling and desalination facilities. A total of 1769 simulated data points containing data indicating a failure status are applied to develop a comprehensive AI‐based Diagnostic (AID) system covering a wide range of 42 input parameters. The paper introduces a conditional monitoring system for adsorption cooling and desalination systems. The novelty of the presented study mainly consists of two aspects. First, the intelligent system predicts the health or failure states of various components in a complex three‐bed adsorption chiller installation using the extensive input data sets of 42 different operating parameters. The developed AID expert tool, based on selecting the best from 42 models generated by the DataRobot platform, was validated on the complex, existing three‐bed adsorption chiller. The AID system correctly identified healthy and failure states in various installation components. The developed expert system is very efficient (AUC = 0.988, RMSE = 0.20, LogLoss = 0.14) in predicting emergency states. The proposed method constitutes a quick and easy technique for failure prediction and represents a complementary tool compared to the other condition monitoring methods.

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