64 
 
REFERENCES 
[1] M. Fallahpour, A. Fatehi, B. N. Araabi, and M. Azizi, “A neuro-fuzzy controller 
for rotary cement kilns,” IFAC Proc. Vol., vol. 17, no. 1 PART 1, pp. 13259–
13264, 2008, doi: 10.3182/20080706-5-KR-1001.1458. 
[2] J. Baek and Y. Choi, “Deep neural network for predicting ore production by 
truck-haulage systems in open-pit mines,” Appl. Sci., vol. 10, no. 5, 2020, doi: 
10.3390/app10051657. 
[3] R. E. King, “Intelligent control in the cement industry,” IFAC Proc. Ser., vol. 
21, no. 4, pp. 303–307, 1989, doi: 10.1016/s1474-6670(17)54510-3. 
[4] S. W. Hagemoen, “Expert system application for lime kiln automation,” IEEE 
Conf. Rec. Annu. Pulp Pap. Ind. Tech. Conf., pp. 91–97, 1993, doi: 
10.1109/papcon.1993.255821. 
[5] FLSmidth, “Advanced process control for the cement industry,” FLSmidth, 
2014. 
[6] J. P. John, “Parametric Studies of Cement Production Processes,” J. Energy, 
vol. 2020, pp. 1–17, 2020, doi: 10.1155/2020/4289043. 
[7] K. W. Winspear and L. G. Morris, “AUTOMATION AND CONTROL IN 
GLASSHOUSES,” Acta Hortic., vol. XIX, no. 2, pp. 61–70, 1965, doi: 
10.17660/actahortic.1965.2.10. 
[8] H. Zermane and H. Mouss, “Internet and fuzzy based control system for rotary 
kiln in cement manufacturing plant,” Int. J. Comput. Intell. Syst., vol. 10, no. 1, 
pp. 835–850, 2017, doi: 10.2991/ijcis.2017.10.1.56. 
[9] “Kiln Control and Operation - INFINITY FOR CEMENT EQUIPMENT.” 
https://www.cementequipment.org/cement-plant-operation-ccr-operator/kiln-
control-operation/ (accessed Nov. 24, 2021). 
[10] Holcim (Lanka) Ltd, “NON-CONFIRM SITUATION NON-CONFIRM 
SITUATION,” 2016. 
[11] J. C. Duchi, P. L. Bartlett, and M. J. Wainwright, “Randomized smoothing for 
(parallel) stochastic optimization,” Proc. IEEE Conf. Decis. Control, vol. 12, 
pp. 5442–5444, 2012, doi: 10.1109/CDC.2012.6426698. 
[12] S. Biswal and G. R. Sabareesh, “Design and development of a wind turbine test 
rig for condition monitoring studies,” 2015 Int. Conf. Ind. Instrum. Control. 
ICIC 2015, pp. 891–896, Jul. 2015, doi: 10.1109/IIC.2015.7150869. 
[13] J. Zenisek, F. Holzinger, and M. Affenzeller, “Machine learning based concept 
drift detection for predictive maintenance,” Comput. Ind. Eng., vol. 137, p. 
106031, Nov. 2019, doi: 10.1016/j.cie.2019.106031. 
[14] Á. L. Orille-Fernández, N. Khalil, and S. Bogarra Rodríguez, “Failure risk 
65 
 
prediction using artificial neural networks for lightning surge protection of 
underground MV cables,” IEEE Trans. Power Deliv., vol. 21, no. 3, pp. 1278–
1282, Jul. 2006, doi: 10.1109/TPWRD.2006.874643. 
[15] A. K. Jain, J. Mao, and K. M. Mohiuddin, “Artificial neural networks,” 
Computer, vol. 29, no. 3. pp. 31–44, Mar. 1996, doi: 10.1109/2.485891. 
[16] M. M. Saritas, “Performance Analysis of ANN and Naive Bayes Classification 
Algorithm for Data Classification,” Int. J. Intell. Syst. Appl. Eng., vol. 7, no. 2, 
pp. 88–91, Jun. 2019, doi: 10.18201/ijisae.2019252786. 
[17] S. Gomes Soares and R. Araújo, “An on-line weighted ensemble of regressor 
models to handle concept drifts,” Eng. Appl. Artif. Intell., vol. 37, pp. 392–406, 
2015, doi: 10.1016/j.engappai.2014.10.003. 
[18] J. H. Shin, H. B. Jun, and J. G. Kim, “Dynamic control of intelligent parking 
guidance using neural network predictive control,” Comput. Ind. Eng., vol. 120, 
pp. 15–30, 2018, doi: 10.1016/j.cie.2018.04.023. 
[19] B. Hesser, Daniel Frank; Markert, “Tool wear monitoring of a retrofitted CNC 
milling machine us... - RWTH AACHEN UNIVERSITY Chair and Institute of 
General Mechanics - English,” RWTH-2018-230550, vol. 19, pp. 1–4, 
Accessed: Nov. 29, 2021. [Online]. Available: https://www.iam.rwth-
aachen.de/go/id/ssih/file/750306/lidx/1/. 
[20] A. Mattes, U. Schopka, M. Schellenberger, P. Scheibelhofer, and G. Leditzky, 
“Virtual Equipment for benchmarking Predictive Maintenance algorithms,” 
2012, doi: 10.1109/WSC.2012.6465084. 
[21] M. Kuhn and K. Johnson, Applied predictive modeling. Springer New York, 
2013. 
[22] J. Brownlee, “Data Preparation for Machine Learning: Machine Learning 
Mastery,” Ambiguous Childhoods  Peer Social. Sch. Agency a Zambian Village, 
pp. vii–viii, 2020, Accessed: Nov. 29, 2021. [Online]. Available: 
https://machinelearningmastery.com/data-preparation-for-machine-learning/. 
[23] J. Brownlee, “How to Use StandardScaler and MinMaxScaler Transforms in 
Python,” Machine Learning Mastery, 2020. 
https://machinelearningmastery.com/standardscaler-and-minmaxscaler-
transforms-in-python/ (accessed Nov. 29, 2021). 
[24] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical 
Machine Learning Tools and Techniques. Elsevier Inc., 2016. 
[25] J. Brownlee, “How to use Data Scaling improve Deep Learning Model Stability 
and Performance,” 2019. https://machinelearningmastery.com/how-to-
improve-neural-network-stability-and-modeling-performance-with-data-
scaling/ (accessed Nov. 29, 2021). 
66 
 
[26] A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve 
neural network acoustic models,” 2013. 
[27] S. Arad, “Thermal analysis of the rotary kiln through FEA 2 State of Art,” no. 
May, pp. 182–187, 2015. 
[28] S. Umar, “Reference Guide For Process Performance Engineers (4th ed),” 
Holcim, no. April, 2007.