Krzywanski, Jaroslaw;
Sztekler, Karol;
Skrobek, Dorian;
Grabowska, Karolina;
Ashraf, Waqar Muhammad;
Sosnowski, Marcin;
Ishfaq, Kashif;
... Mika, Lukasz; + view all
(2024)
AutoML-based predictive framework for predictive analysis in adsorption cooling and desalination systems.
Energy Science & Engineering
10.1002/ese3.1725.
Preview |
PDF
Energy Science Engineering - 2024 - Krzywanski - AutoML based predictive framework for predictive analysis in adsorption.pdf - Published Version Download (3MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | AutoML-based predictive framework for predictive analysis in adsorption cooling and desalination systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/ese3.1725 |
| Publisher version: | https://doi.org/10.1002/ese3.1725 |
| Language: | English |
| Additional information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | automated machine learning, condition monitoring, DataRobot, net-zero emissions, predictive maintenance, waste heat utilization. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189590 |
Archive Staff Only
 |
View Item |
