I2OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing

Ramzey, Hazem; Badawy, Mahmoud; Elhosseini, Mostafa A.

doi:10.3390/en16042023

Cited by 12 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Supervisory control and data acquisition (SCADA) systems have been used in the oil industry to monitor processes over the last few decades [22]. However, SCADA systems have many drawbacks, such as the interoperability of the hardware and software and low-density time and space.…”

Section: Iiot In Oil and Gas Industrymentioning

confidence: 99%

See 1 more Smart Citation

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

View full text Add to dashboard Cite

Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model.

show abstract

Section: Iiot In Oil and Gas Industrymentioning

confidence: 99%

“…Furthermore, wireless sensor networks (WSN) have been employed in the oil industry for monitoring and data collection [25]. Nevertheless, they are not uniform systems and frequently require more collaborative communication [22].…”

Section: Iiot In Oil and Gas Industrymentioning

confidence: 99%

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Edge computing is centered around processing sensor data not in central nodes but at the network periphery, nearer to the data origin. The methodology proposed in [22] employs a decentralized network architecture designed to accommodate local data generation and mobile computing. By integrating edge computing, an intermediary layer forms between IoT devices and the cloud, bringing advantages like enhanced processing speed, on-the-spot decision making, automatic alert systems, and decreased complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods

et al. 2023

View full text Add to dashboard Cite

Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption.

show abstract

“…Eclipse Mosquitto is used as a broker on the Raspberry Pi 4. Briefly, Mosquitto is an open-source message broker, which is a lightweight MQTT broker that can be used on all devices, including full servers and low-power boards, to publish and subscribe for messages [27] The Pi 4 is powered using a USB-C connection, which also powers connected peripherals in the downstream direction [28]. In a local network, the Raspberry Pi serves as the local server and hosts the MQTT broker (Mosquitto), and the Node-RED application.…”

Section: Monitoring System Designmentioning

confidence: 99%

Design of a Real-Time Monitoring and Controlling System for the PEM Fuel Cell Model Based on LabVIEW with IoT Technology

2023

IJIES

View full text Add to dashboard Cite

In this research, a predictive voltage neural controller and remote monitoring for the nonlinear proton exchange membrane fuel cell (PEMFC) system are implemented in real-time. This paper's major purpose is to precisely and rapidly determine the appropriate hydrogen partial pressure (PH2) control action to improve the fuel cell's nonlinear performance under varying load currents and to enable remote monitoring of the nonlinear PEMFC system based on the internet of things (IoT). The laboratory, virtual instrument engineering workbench (LabVIEW) package is used to demonstrate the real-time performance of the proposed predictive voltage controller applied to the 150-watt PROTIUM PEMFC, which will be used to generate the appropriate amount of hydrogen partial pressure control action that will enter the fuel cell for stabilizing the desired output voltage. The message queuing telemetry transport (MQTT) protocol and a Raspberry Pi 4 acting as a local server are the building blocks upon which the monitoring component of the proposed system is implemented in order to monitor the desired output voltage, the fuel cell output voltage, and PH2. The Raspberry Pi collects the necessary fuel cell data and sends it to the node-red dashboard for monitoring. According to the simulation and the experimental results obtained using the proposed predictive controller on PROTIUM PEMFC, the proposed controller can generate an accurate, prompt, and timely reaction to the hydrogen partial pressure control action to reduce the tracking voltage error and to get rid of the fuel cell output voltage oscillation. The proposed experimental work was compared to the simulation findings to confirm its effectiveness in terms of effectively tracking the desired output voltage, providing a fast response, and achieving the optimal partial pressure of hydrogen. However, in the simulation findings, a voltage error of 0.01 volts was observed without any oscillation. On the other hand, the experimental results indicate a slightly higher voltage error of approximately 0.1 volts, accompanied by oscillations of around ± 0.1 volts.

show abstract

I2OT-EC: A Framework for Smart Real-Time Monitoring and Controlling Crude Oil Production Exploiting IIOT and Edge Computing

Cited by 12 publications

References 22 publications

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods

Design of a Real-Time Monitoring and Controlling System for the PEM Fuel Cell Model Based on LabVIEW with IoT Technology

Contact Info

Product

Resources

About