Designing a Decision Making Support Infor mation System for the Operational Control of In dustrial Technological Processes

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Adelina Faradian 1,* Teimuraz Manjafarashvili 1 Nikoloz Ivanauri 1

1. Department of Information Science, Ivane Javakhishvili Tbilisi State University, 0177, university str. 2, Tbilisi, Georgia

* Corresponding author.


Received: 3 Jan. 2015 / Revised: 17 Apr. 2015 / Accepted: 22 Jun. 2015 / Published: 8 Aug. 2015

Index Terms

Fuzzy-Logic, Information Systems, Lime Kiln, Knowledge Base, Expert Knowledge, Linguistic Variables, Fuzzy Rules


Fuzzy logic is a new and innovative technology that was used in order to develop a realization of engineering control. In recent years, fuzzy logic proved its great potential especially applied to automatization of industrial process control, where it enables the control design to be formed based on experience of experts and results of experiments. The projects that have been realized reveal that the application of fuzzy logic in the technological process control has already provided us with better decisions compared to that of standard control technique. Fuzzy logic provides an opportunity to design an advisory system for decision-making based on operator experience and results of experiments not taking a mathematical model as a basis. The present work deals with a specific technological process ─ designing a support decision making information system for the operational control of the lime kiln with the use of fuzzy logic based on creation of the relevant expert-objective knowledge base.

Cite This Paper

Adelina Faradian, Teimuraz Manjafarashvili, Nikoloz Ivanauri, "Designing a Decision Making Support Information System for the Operational Control of Industrial Technological Processes", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.9, pp.1-7, 2015. DOI:10.5815/ijitcs.2015.09.01


[1]Barnabas, B. (2013). Mathematics of Fuzzy Sets and Fuzzy Logic.

[2]Ganesh, M. (2006). Introduction to fuzzy sets and fuzzy logic. 

[3]Deepa, S. N., Sivanandam, S. N., & Sumathi, S. (2007). Introduction to Fuzzy Logic.

[4]Gebhardt J., Von Altrock C. (1996). Proceedings of the Fifth IEEE International Conference on Fuzzy Systems.

[5]Chandra, S. (2013).Waste Materials Used in Concrete Manufacturing.

[6]Pascenco, A.A., Serbin, V.L., & Starchevsky, E.A. (1975). Cementing materials. Publisher Association   "Vitsa School”.

[7]Fraps, G. S. (1917). Effects of lime and carbonate of lime on acid phosphate.

[8]Naumenko, A.V., Naumenko, V.D., & Naumenko, I.V. (2004). Production of lime, limestone milk and the car-bonation gas in sugar factories.

[9]Oates, J. A. H. (2008). Lime and Limestone.

[10]Siddique, R., & Khan, M.I. (2011). Supplementary Ce-menting Materials. Springer.

[11]Ambrosio, B. (1989). Qualitative process theory using linguistic variables.

[12]Hanss, M. (2005). Applied Fuzzy Arithmetic.

[13]Kaufman, A., Madan, M.G., & Esposito, B. (1991). “Introduction to Fuzzy Arithmetic”.

[14]Zadeh, L. A. Linguistic Variables and Approximate Reasoning.

[15]Zadeh, L. A. The concept of a linguistic variable and its application to approximate reasoning.

[16]Lieb, H. H. (1993). Linguistic Variables.

[17]Klir, G. J., & Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic.

[18]Leonenkov, A. (2005). Fuzzy modeling in MATLAB and fuzzyTECH.

[19]Ruan, D., & Kerre, E. (2000). Fuzzy If-Then Rules in Computational Intelligence Using MATLAB”. Springer-Verlag Berlin Heidelberg (pp. 157-367).

[20]Wang, L.X., & Jerry, M. Mendel, J.M. (1991).Generating Fuzzy Rules from Numerical Data, with Applications.

[21]Ross, J.T. (2004). Fuzzy Logic with Engineering Applications.

[22]Bernadette, B.M., Yager, R.R., & Lotfi, A.Z. (1995). Fuzzy Logic and Soft Computing.