## 1 Introduction  
The cement manufacturing process is comprised of a number of sub-operations such as quarrying, crushing, raw milling, burning, cooling, and cement grinding. The control of cement grinding is an essential part of the process of cement production. It has remained a challenging problem for years because of the existing model uncertainties, nonlinearities, variation in the feedstock, and multi-factor interdependencies. Modeling, optimization, and control of integrated grinding circuits are some of the major challenges in the efficiency and productivity of cement production systems. Minimizing energy consumption while simultaneously improving product quality and process efficiency would be a major contribution to the cement industry, and reduce global energy consumption and greenhouse gas emissions [1; 2; 3; 4; 5]. However, the large non-linear nature of the process poses major challenges for creating feasible control tactics to maintain process stability and increase efficiency and productivity. Therefore, in our experience of more than 20 years in dealing with cement production, predicting the events that affect the production process is highly desirable, and with the recent technological advancements in real-time data acquisition and analytics becomes achievable in practice. This predictive capability to look ahead to the quality-related variables in the milling plant will allow the use of corrective control policies in order to improve the stability of the system. In our view, such capabilities can be deployed in any industrial setting that has a similar closed-loop controlled milling process regardless of the raw material (e.g., pulp and paper, beverage brewery and water/wastewater treatment industries). Figure 1 shows a schematic of a closed-loop cement grinding process control system [6]. Inside the rotating ball mill, the feed flow supplies raw material (natural stones of various sizes and humidity) to be ground by steel balls. A bucket elevator is used to transport the mill product into the separator where depending on adjustable airflow rate and speed, it is divided into a flow of rejected oversized particles (named coarse return), which are returned to the mill inlet to be ground again (grinding mostly is performed in closed circuit) and a flow of fine particles, which forms the final product. In the cement grinding process, coarse return is one of the main parameters of the process, representing the product quality output. It has a major role in setting the optimization and control objective function of the closed-loop process. Measurement of coarse returns allows for better control of process efficiency as variations in the performance of the grinding circuit occur over time. Understanding the basic and interaction effects of the mill and separator variables on coarse return is an essential factor of the process improvement and is the motivation of this study. In particular, if the coarse returns flow rate increases, then the production flow needs to be reduced so as to not aggravate the increased quality problem. Predicting coarse return helps with stabilizing the process by controlling the controllable system's parameters such as fresh feed and/or separator's speed. Nevertheless, grinding is an unstable and complex process whose variables have coupling, time-varying delay, and non-linear characteristics caused by natural variation, mill load, and fluctuation of the raw material hardness and grindability. It leads to obstruction of the mill and interruption of the grinding process. The phenomenon is known as 'plugging' which increases the complexity of process control and optimization by conventional multivariable data analytics and machine learning methods. This paper proposes a fully connected deep neural network (FCDNN) model to predict the upcoming coarse return in the cement grinding process up to 15 [min] ahead.