## 6 Conclusions and Future Work  
We applied machine learning (ML) methods to perform effective quality control for cement production by identifying abnormal operating conditions in the clinker manufacturing process and predicting the content of free calcium oxide (f-CaO) in cement clinker. Our ML approach was based on the control data measured and collected in the distributed control system (DCS) of a real-life cement production plant.  
Considering that the predictive results under normal operating conditions are more valuable as references to optimize the cement production process, we trained a ResNet50 deep learning model to identify abnormal operating conditions based on real-time thermal images and further eliminated associated DCS data under abnormal operating conditions. The model training in this work was based on frequently sampled production line data, which enhanced the information richness concerning various measurable indicators of the cement production process within the data samples with respect to the prediction of the f-CaO content. We performed various data preprocessing on the DCS production line data, including outlier detection, data smoothing, as well as downsampling, to reduce data redundancy and minimize the effects of abnormal values and abrupt variations that existed in the original dataset on the model training.  
It is of our interest to further improve the performance of the both steps in the proposed methods by adopting more advanced models such as RNN, Prophet, Attention, etc. as well as other helpful sophisticated approaches such as automated feature selections.  
In addition, in observation of the techniques developed in the field of explainable artificial intelligence (XAI), we would like to adopt them in our work of integrating AI technologies to transform and transition traditional production factories to smart manufacturing industries. For example, using techniques such as feature importance ranking to further refine feature engineering/selection to speed up the model training and improve the solution efficiency; using techniques such as saliency maps and layer-wise relevance propagation to highlight and correlate the inputs and outputs of the trained machine learning models. Such work will significantly improve the transparency and interpretability of the models used in our product solutions, which will help the domain users to conceive trustworthy in our AI-based solutions in various manufacturing industries.  
Besides the cement industry, it is also of our great interest to explore the opportunities of adopting AI/ML technologies in various other manufacturing and production industries including petroleum industry, e.g., oil/gas production optimization, pipeline monitoring and leak detection; energy industry, e.g., power grid safety monitoring, power consumption forecasting, etc.  
Conceptualization, X.L. (Xukang Lyu), Z.Z. and J.M.; methodology, X.L. (Xukang Lyu), D.C., X.L. (Xingran Lu) and D.Y.; software, X.L. (Xukang Lyu) and X.L. (Xingran Lu); validation, X.L. (Xukang Lyu), X.L. ([PERSON]), Z.Z. and J.M.; investigation, X.L. ([PERSON]), D.C.; resources, X.L. ([PERSON]), Z.Z. and J.M.; data curation, X.L. ([PERSON]), Z.Z. and J.M.; writing--original draft preparation, X.L. ([PERSON]) and D.Y.; writing--review and editing, X.L. ([PERSON]) and D.Y.; visualization, X.L. ([PERSON]) and D.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Training loss vs. validation loss of the adopted models.