Instrumentation dans l'industrie pétrochimique
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/10962
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Item Remote level control and monitoring in a laboratory tank system using Internet of Things(Université M’Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2025) Belabassi, Rofaida; Habbi, Hacene (Promoteur)The Aim of this project is to design and implement an Internet of Things (IoT) level control and monitoring system for a laboratory tank that employs sensors, microcontrollers, and cloud connectivity to track liquid levels in real time. The IoT system employs the smart ESP32 board which takes full control, monitoring and supervision of the level in the tank during system operation. Basically, level measurements are gathered from an ultrasonic sensor and send out to the Blynk IoT platform, which allows supervising remotely and in real time the level readings and get alerts sent straight to users’ smartphones. The system is intended to be inexpensive, scalable, and flexible enough to accommodate many usage cases, especially in areas like oil and gas industry where automation and resource efficiency are crucial for production system reliability and safety.Item Evolving cloud-based fault monitoring system for an industrial gas turbine(Université M’Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2024) Fadel, Mohamed Saout El Hak; Messaoudi, Mohammed Rida; Habbi, Hacene (Promoteur)The Thesis focuses on the development and implementation of an evolving fault monitoring system for an industrial gas turbine. Building on our study of turbine during our internship, this work is dedicated to creating an evolving cloud-based system of AnYa-type to detect and identify different fault scenarios in the industrial gas turbine. This model leverages real-time data and non-parametric methods to adapt to dynamic environments, enhancing efficiency and reliability in complex systems. Our efforts have been directed towards building this monitoring system to ensure continuous learning and adaptation, ultimately improving operational performance under normal and fault modes.Item Intelligent calibration of level sensors using functional link neural networks with piecewise linear interpolation(Université M’Hamed Bougara Boumerdès : Faculté des Hydrocarbures et de la Chimie, 2024) Benhacine, Souheil Nadjmeddine; Naili, Mohamed Chakib; Habbi, Hacene (Promoteur)This Study focuses on designing an intelligent calibration model for level sensors by using the Functional Link Artificial Neural Network (FLANN). The FLANN has simple architecture and requires less computational effort compared to other neural networks models. This made it good enough in extending the linearity of many sensors and transducers as reported in recent literature. Despite its advantages, the standard FLANN model has limitations in terms of generalization and accuracy. To overcome this shortcoming, we propose in this study an approach that relies on integrating Piecewise Linear (PWL) interpolation with the FLANN model. This approach aims to improve the overall performance of the calibration process, offering better generalization capabilities. Building on an extensive experimental investigation of the intelligent calibration model on the typical problem of level sensors calibration, this manuscript outlines the methodology, implementation steps, and findings of our study.