Predicting process and regeneration air conditions in LT3 molecular sieve desiccant wheels using machine learning and regression methods
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The performance of desiccant wheels is influenced by various design and operational variables. Existing models in the literature for LT3 molecular sieve desiccant wheels often include only a limited number of input parameters or have restricted operating ranges. This study addresses these limitations by generating a dataset encompassing 32,975 scenarios, which was used to develop 192 models using four methods such as multiple linear regression (MLR), decision tree (DT), support vector machine (SVM), and multilayer perceptron (MLP). The models were categorized into two groups to allow for various analyses. Group A models predict the process air outlet conditions of the desiccant wheel, namely temperature (Tpo) and humidity (omega po). Group B models, in contrast, predict the required regeneration air inlet temperature (Tri) necessary to achieve a desired process air exit humidity (omega po), while also predicting Tpo. MLP models demonstrated the best accuracy across both groups. In Group A, the model coded as MLP-7 achieved an RMSE of 0.2017 degrees C for Tpo, and MLP-6 yielded an RMSE of 0.0656 g/kg for omega po. In Group B, MLP-7 recorded an RMSE of 0.1764 degrees C for Tpo, while Tri had an RMSE of 0.7892 degrees C. Additionally, in Group A, the models coded as RS, 3rdCross, DT-28, and SVM-5 also delivered reliable results, while in Group B, the RS, 2ndCross, 3rdCross, DT-28, and SVM-3 models performed well.
Açıklama
Anahtar Kelimeler
Desiccant wheel, LT3 molecular sieve, Multiple linear regression, Machine learning, Support vector machine, Multilayer perceptron, Decision tree
Kaynak
International Communications in Heat and Mass Transfer
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
164
