Khalid Salmanov

Work place: Engineering Institute of Technology, Perth, WA 6005, Australia



Research Interests:


Khalid Salmanov was born in Baku, Azerbaijan on 14th of February 1982. He obtained his bachelor’s degree in Oil and Gas Wells Driving in 2002 from the Azerbaijan State Oil Academy in Azerbaijan. Then, he decided to deepen his knowledge in the oil industry hence he enrolled and graduated his master’s degree in Environmental Protection Measures Applied in the Oil Industry in 2004 in a cooperation with Azerbaijan State Oil Academy (Azerbaijan), University of Nizza (France), and University of Geneva (Italy). In 2004 he was employed by Rolls-Royce Energy Azerbaijan and worked as Control Engineer. During his employment with Rolls-Royce, Khalid gained a technical diploma from the Cleveland Institute of Electronics INC, Cleveland, Ohio USA in the faculty of Industrial Electronics with PLC Technology. He worked for Rolls-Royce over 8 years and his last position when he left the company was Automation Manager for Central Asia Region. Later in his career he joints British Petroleum (BP) as Senior Instrumentation and Control Engineer based in Azerbaijan. In 2020 he obtained Master Engineer degree in Industrial Automation with Institute of Engineering and technology, Perth, Australia. Currently, Khalid is working for Croda Europe Limited as Instrumentation and Control Engineer at the chemical production plant based in the United Kingdom. Khalid also is a Chartered Engineer with Institute of Engineering and Technology, UK.

Author Articles
Data Analysis for the Aero Derivative Engines Bleed System Failure Identification and Prediction

By Khalid Salmanov Hadi Harb

DOI:, Pub. Date: 8 Dec. 2021

Middle size gas/diesel aero-derivative power generation engines are widely used on various industrial plants in the oil and gas industry. Bleed of Valve (BOV) system failure is one of the failure mechanisms of these engines. The BOV is part of the critical anti-surge system and this kind of failure is almost impossible to identify while the engine is in operation. If the engine operates with BOV system impaired, this leads to the high maintenance cost during overhaul, increased emission rate, fuel consumption and loss in the efficiency. This paper proposes the use of readily available sensor data in a Supervisory Control and Data Acquisition (SCADA) system in combination with a machine learning algorithm for early identification of BOV system failure. Different machine learning algorithms and dimensionality reduction techniques are evaluated on real world engine data. The experimental results show that Bleed of Valve systems failures could be effectively predicted from readily available sensor data.

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