@article{2019,title={USE OF ARTIFICIAL NEURAL NETWORK TO IMPROVE ENERGY SAVING ON CONTINUOUS ANNEALING LINES},abstractNode={In today's competitive environment, the factors that will provide advantages for companies are to use energy efficiently and increase the production amount. In order to achieve this, the companies are collecting work teams, with these teams; they make energy conservation, environmental improvements and production-enhancing work. Governments provide incentives for AR-GE work and even funding support. In order to save energy and compete with rival companies, companies have to observe very different situations and change behavior. Sometimes this has become very important, so it was forced to change even the working principles of the established discipline. This has become very important; even so sometimes, they are forced to change the company's working principles of establishment. In this study, using YSA instead of the mathematical model used in the Continuous Annealing Line (STH), it was aimed to modeling the most suitable ones for the energy saving and production increase from the practices developed by the operators. In this way, it has been developed a system to perform their tasks instead of operators and increase energy savings. By providing standards in applications, it is aimed to reduce the number of non-tolerance products and thus to increase production.},author={Ömer Faruk SEZER},year={2019},journal={The Journal of Academic Social Science}}