dc.description.abstract | Based on the machine learning algorithm and data analysis technique, this thesis studies the energy saving for the cloth setting machine in the textile industry and chiller in the air conditioning system. In the textile factories, the master who is familiar with the machine parameters is reducing year by year, hence, the useful and helpful experience could not pass on. On the other hand, the same parameters cannot reflect small-volume, large-variety production. The first purpose of this study is to build an accurate input-output model in the fabric drying process, therefore, a series of familiar machine learning algorithms, such as decision tree, AdaBoost, KNN (k-nearest neighbors), XGBoost, neural network, voting regressor, and stacking regressor are applied. The grid search is used to find the best parameters of machine learning models, and the integrated learning methods are used to reduce variation, bias and deviation such that the model with higher accuracy can be established. A back-propagation network with embeddings is trained with one-cycle policy to achieve satisfactory moisture content prediction. After comparing the performance of machine learning models, the best trained model is XGBoost. The model will predict the moisture of the fabric, and the SHAP (Shapley Additive exPlanations) value will be calculated to explain the positive or negative relationship between the input and output of model. When a set of test data with a larger moisture content is given, we use the aforementioned best model to simulate the operation of the stenter machine so that we can know how to adjust input characteristics to achieve a satisfactory fabric moisture content. Thus the purpose of energy saving is achieved.
In the air condition system, the chiller accounts for about 60% of total power consumption. Actually, for the 200-ton chiller, the performance is only about 70% in comparison with the new one after it runs for three years. Therefore, the second purpose of this study is to cluster different efficient chillers and look for the input characteristics (parameters) of inefficient causes. An XGBoost model is chosen and SHAP value is calculated to select the four features which have the highest correlation with the output. The highest average silhouette score is used to determine the clustering of energy-consuming chillers. The clustering results and actual chiller data are compared and analyzed to visualize the causes of the inefficiency of the chiller. Finally, a chiller diagnostic system with a graphical user interface is established to perform on-line diagnosis. An independent sample t test is used to test whether the current chiller efficiency is worse than the model estimated efficiency. When the test is true, it indicates the chiller efficiency starts to deteriorate. Then through improved factor analysis, we can find the possible input characteristics (parameters) of inefficient causes. | en_US |