## 1 Introduction  
Recent developments in sensing and computing facilities, high-speed networks, and storage capabilities enable practitioners in various industrial fields to collect and archive a sheer volume of datasets from different perspectives (e.g., performance measurements, control parameter values, etc.) and across the entire industrial production process (raw materials preparation, production quality evaluation, etc.). The availability of such unprecedented amounts of data together with the recent advances of artificial intelligence (AI) technologies such as ensemble learning, artificial neural networks (ANNs), etc., stimulate the incorporation of machine learning (ML)-based approaches into industrial manufacturing. For example, many such efforts in the cement industry are already underway to test and refine machine learning approaches to improve the control of their production devices including raw mills [1, 2], rotary kiln [3, 4], ball mills [5, 6, 7], conveyors [8, 9], blenders [10], as well as other related manufacturing activities such as cement clinker quality control [11], concrete porosity prediction [12], energy consumption estimation [13], electricity cost optimization [14], hydrating behavior prediction [15], fault detection and diagnosis [4], etc. The process control of cement manufacturing and production is traditionally conducted manually by human operators. These human operators usually adopt an iterative approach between empirically adjusting the values of control parameters through the distributed control system (DCS) of the cement production plants and observing the corresponding status measurements to stabilize and optimize the manufacturing process and product quality of their respective industrial domains. In the new \"intelligent\" approach of industrial system control, artificial intelligence (AI) engineers employ model-basedoptimization and data-driven methods such as machine learning (ML) algorithms to construct pre-trained models to predict, control, and optimize factory production performance. In these approaches, human operators, who are usually industrial experts, will assist AI practitioners in model training and parameter tuning by incorporating domain-specific knowledge. To support process efficiency and cement product quality, one must take into consideration many factors including the discharging speed of raw materials, the temperature of heating devices, the rotation speed of kiln, the fan speed of the ventilation system, and the pressure of the grate cooler, etc. Various sensors are deployed to monitor, measure, and record these factors, and based on which, predictive models are trained and utilized to optimize characteristics that directly influence the cement product quality. It has long been well-recognized that the cement product quality in a cement plant is largely determined by the cement clinker quality in the rotary kiln [16], in which the content of free calcium oxide (f-CaO) is one of the most important cement quality indicators. The clinker free calcium oxide refers to the calcium compounds that exist in cement materials that do not react with silicates to form hydration products, whose content in the cement clinker directly manifests the strength, hardening time, shrinkage, and other properties of cement products. Therefore, accurately predicting the content of f-CaO in cement clinker is of great significance for the control and optimization of cement manufacturing process. In this work, we employ a two-step data-driven approach to realize accurate prediction of free calcium oxide content in cement clinker to help optimize the cement production process. Our proposed method first preprocesses the DCS control data and rules out the records that are insignificant or negative in predicting the f-CaO content.