Prediction of the natural gas consumption in chemical processing facilities with genetic programming

Cinkarna Ltd. is a chemical processing company in Slovenia and the country’s largest manufacturer of titanium oxides (TiO 2 ). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In...

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Bibliographic Details
Published in:Genetic programming and evolvable machines 2016-09, Vol.17 (3), p.231-249
Main Authors: Kovačič, Miha, Dolenc, Franjo
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Consumption
Electrical Engineering
Genetic algorithms
Interpreters
Mathematical models
Natural gas
Programming Languages
Programming Techniques
Regression
Software Engineering/Programming and Operating Systems
Suppliers
Titanium dioxide
Titanium oxides
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Summary:Cinkarna Ltd. is a chemical processing company in Slovenia and the country’s largest manufacturer of titanium oxides (TiO 2 ). Chemical processing and titanium oxide manufacturing in particular requires high natural gas consumption, and it is difficult to accurately pre-order gas from suppliers. In accordance with the Energy Agency of the Republic of Slovenia regulations, each natural gas supplier regulates and determines the charges for the differences between the ordered (predicted) and the actually supplied quantities of natural gas. Yearly charges for these differences total 1.11 % of supplied natural gas costs (average 50,960 EUR per year). This paper presents natural gas consumption prediction and the minimization of associated costs. The data on daily temperature, steam boilers, sulfur acid and TiO 2 production was collected from January 2012 until November 2014. Based on the collected data, a linear regression and a genetic programming model were developed. Compared to the specialist’s prediction of natural gas consumption, the linear regression and genetic programming models reduce the charges for the differences between the ordered and the actually supplied quantities by 3.00 and 5.30 times, respectively. Also, from January until November 2014 the same genetic programming model was used in practice. The results show that in a similar gas consumption regime the differences between the ordered and the actually supplied quantities are statistically significant, namely, they are 3.19 times lower ( t test, p 
ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-016-9264-x