Abstract
The chiller machine is one of the most electricity-consuming parts of factory facilities in high-tech industries such as semiconductor and TFT-LCD manufacturing. To reduce variability and optimize the chiller allocation, researchers have come up with various solutions, but few of them can indeed be widely adopted due to differences of factory layout, machine types, data collections, etc. This study proposes a solution that integrates big data analytics and machine learning techniques to automatically provide recommendations of chiller optimization for energy saving. The optimal chiller adjustment is defined as the condition that the required cooling load for a wafer fab is satisfied while and the electricity consumption is minimized. In the meantime, those adjustment alternatives considering chiller healthy status to obviate inappropriate combinations. Hence, engineers only need to judge the rationality of these recommendations to adjust chillers so that can guarantee operation effectiveness and efficiency as well as empower intelligent manufacturing. An empirical study was conducted in a leading semiconductor company in Taiwan to demonstrate the validity of the proposed approach.