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Clay drying is one of the most important steps in the construction materials industry. Due to its high-energy requirement, clay drying is one of the most energy-consuming processes contributing to greenhouse gas emissions, motivating the search for strategies to increase the energy efficiency of the process. The process involves several operating variables whose historical records can generate a large amount of data, which can be organized and analyzed efficiently using data mining techniques (DMT). In this paper, the performance variables of a material-industry rotary dryer were identified and analyzed. A cluster analysis was performed using the Ward's classification method with 1729 operating data, identifying the kind of materials processed and the operating configurations. A linear regression model was constructed using the RRELIEFF feature selection algorithm in Orange: Data Mining Toolbox in Python. The main thermal-energy-consumption variables were the production rate, power, inlet gas temperature, and product moisture delta. These four variables represent the dryer's specific energy consumption index ( I c ) more suitably than the classic Energy Vs Production model. Consequently, it was established that a global-aeration-factor reduction could improve the process efficiency by 7% to 10% at the average production rate. [ABSTRACT FROM AUTHOR]