Modeling of Odor from a Particleboard Production Plant

Velič, Annamarie; Šurina, Igor; Ház, Aleš; Vossen, Frans; Іванова, Ірина Євгенівна; Soldán, Maroš

doi:10.1080/02773813.2019.1668416

Cited by 1 publication

(1 citation statement)

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“…Therefore, we consider applied studies. For example, odor source prediction [40], odor intensity prediction according to distance, odor intensity classification [13] and application for other types of odors other than livestock odors, such as water [12] and factory [41] odors. These studies will help to significantly positively impact the lives of odor facilities, such as livestock facilities, as well as the lives of the residents in the surrounding area.…”

Section: Data Availability Statementmentioning

confidence: 99%

Evaluation of Odor Prediction Model Performance and Variable Importance according to Various Missing Imputation Methods

et al. 2022

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The aim of this study is to ascertain the most suitable model for predicting complex odors using odor substance data that has a small number of data and a large number of missing data. First, we compared the data removal and imputation methods, and the method of imputing missing data was found to be more effective. Then, in order to recommend a suitable model, we created a total of 126 models (missing imputation: single imputation, multiple imputations, K-nearest neighbor imputation; data preprocessing: standardization, principal component analysis, partial least square; and predictive method: multiple regression, machine learning, deep learning) and compared them using R2 and mean absolute error (MAE) values. Finally, we investigated variable importance using the best prediction model. The results identified the best model as a combination of multivariate imputation using Bayesian ridge as the missing imputation method, standardization for data preprocessing, and an extremely randomized tree as the predictive method. Among the odor compounds, Methyl mercaptan, acetic acid, and dimethyl sulfide were identified as the most important odor compounds in predicting complex odors.

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Section: Data Availability Statementmentioning

confidence: 99%