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• W4221099740 abstract "Recently, the scientific community has become more interested in solar-driven steam materials and systems for desalination and disinfection. Solar thermal energy for membrane distillation desalination provides a green and sustainable option for building settings where there is a strong connection between water constraint and high solar irradiation. Artificial intelligence (AI) is rapidly being used to optimize water treatments and saltwater desalination because of its high precision and accuracy. Explainable AI (XAI) enables people to better understand and trust a model's predictions and to detect and rectify inaccurate AI predictions. This study analyses recent advances in solar-driven steam materials engineering and the significant technological constraints that impede its wide-scale deployment. Using local interpretable model-agnostic explanations (LIME), our study provides an interpretable solution (in addition to the binary classification result of the developed black-box deep learning (DL) model) so that experts can understand why the machine thinks this way, providing critical insights for the decision-making process. The proposed XAI-DL model is based on a DL network consisting of three cascaded convolutional blocks for processing tabular data. Therefore, the XAI-DL classification model achieves a cooling quality accuracy of 82.64% during the validation stage, supporting the explaining capability. During the testing, the [inlet-cooling-water-temperature] pushes the model lower, whereas the [ambient-temperature], [feed-water-flow-rate], and the [inlet-feed-water-temperature] pushes the model higher. The LIME explanation result is consistent with the statistical analysis of the data. Consequently, the proposed explainer assists non-experts in comparing and improving the untrustworthy model through and clarifies the importance of each feature and its relationship to other features and its relationship to the class. Finally, the XAI-DL fits and supports the different manufacturers of membrane desalination system(s) to inspect cooling quality in their designed system and consistency of interpretability and trust." @default.
• W4221099740 created "2022-04-03" @default.
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• W4221099740 date "2022-12-01" @default.
• W4221099740 modified "2023-10-17" @default.
• W4221099740 title "Deep Learning model and Classification Explainability of Renewable energy-driven Membrane Desalination System using Evaporative Cooler" @default.
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• W4221099740 doi "https://doi.org/10.1016/j.aej.2022.03.050" @default.
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