## 3 Background and Problem Statement
### Prediction of Free Calcium Oxide (f-CaO) Content  
As stated in Section 3.1, the cement clinker production is a very complex process that involves various components and many manipulatable parameters. The ultimate objective of the production process control is to obtain the best quality of the clinker by iteratively adjusting the control parameter values through the distributed control system (DCS) of the cement plants in response to the observations and measurements of the quality attributes of the cement clinker. The free calcium oxide (f-CaO) in cement clinker, which refers to the calcium oxide (CaO) present in the cement clinker that has not participated in the chemical reactions and exists in a free state, reflects the quality of the clinker to a significant extent. The residual unreacted calcium oxide left in the clinker after the burning process only starts to hydrate slowly only after the cement has undergone hydration, hardening, and achieved a certain level of strength. This hydration of free calcium oxide is usually accompanied by a certain degree of uneven expansion, which can potentially lead to a decrease in cement strength, causing cracking or even collapse and resulting in poor cement stability. Therefore, the content of free calcium oxide (f-CaO) in cement clinker is commonly regarded as one of the most important indicators used to assess cement quality. Currently, most cement manufacturing plants use manual laboratory methods to determine and measure the f-CaO content in cement clinker, which takes around one hour from sampling the clinker to obtaining the test results. This approach has certain drawbacks: it has a lag in providing guidance for adjustments in the operating conditions of cement rotary kilns, and the test results are susceptible to various factors such as the technical skills and experience of the human operators. As a result, there is a certain level of measurement inaccuracy, delay, and error in the test results of f-CaO content in cement clinker, making it difficult to be used for promptly detecting and addressing issues during the cement production. Figure 1: A simplified schematic view of the cement clinker calcination process. It is difficult to adjust control parameters of cement production and achieve satisfactory results by first constructing analytical models of the complex cement manufacturing process and then relying on the test results of f-CaO content to conduct the adjustments due to poor process understanding and delayed delivery of f-CaO content data. Because of this, using soft sensors based on data-driven models derived from the actual input-output process data, which in many cases are readily available in different forms of distributed control system (DCS) deployed in most cement plants, is becoming a common approach adopted in the intelligent transformation practice of cement industry. By monitoring the status measurements in real time, such soft measurement methods can provide accurate data support for predictive process control, thereby addressing challenges in establishing traditional non-linear analytical process models. It is particularly suitable for cement production, which is a continuous process and is influenced by different equipment at various stages, to establish non-linear models by extracting features from historical multidimensional data and accurately predicting f-CaO content in cement linker. To save the cost and provide more accurate and timely guidance for the process control of cement production, we address the limitations of traditional manual laboratory methods for f-CaO content tests and train machine learning models that characterize the relationship between key measurable variables in the process of cement production and the content of f-CaO in cement clinker. Based on the models trained using datasets of time-series measurements, we predict the f-CaO content in cement clinker at a future time based on the ongoing monitoring and recording of the processing status of cement production.