Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Kimya Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2014
Tezin Dili: İngilizce
Öğrenci: Tuba Kahya
Danışman: SERKAN KINCAL
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Özet:In refinery utility generation, boilers, steam and gas turbines are the primary equipment, producing steam as well as electricity. In boilers, high pressure steam is produced by combustion of a mixture of waste gases from the production line and natural gas – called fuel gas. The steam is at relatively high pressures and is then reduced to lower pressures in steam turbines. The resulting medium and low pressure steam are sent to the refinery for use in processes. In a gas turbine, electricity is produced by compressed air using energy released from combustion of natural gas. In this study, the aim is to model the efficiencies of the steam production equipment, tracking their performance in an effort to optimize the equipment utilization. For this purpose, historical data from a refinery are verified. Material and energy balances are checked on individual units using ASPEN software to verify the data as a reference. Then, MATLAB software is used to adapt our situation in the plant. In these analyses, clustering methods are used. Results of the model and analysis of historical data allows us to make recommendations on which sensors need to be calibrated and how data integrity issues can be resolved. In the examined case, it was postulated that oxygen sensor at the stack gas line was accurate and then combustion air flow calculation was set forth to check mass balance. To check energy balance, some imaginary heat exchangers are defined at critical locations for which there should be no net heat loss/gain. Based on these restrictions, the dataset of the plant over a period of 3 months was corrected. After ensuring data integrity, efficiency values of multiple boilers in production are analyzed. These efficiency values can be used reliably in an overall optimization scheme to optimize equipment usage.