Multiple Surface Pipeline Leak Detection Using Real-Time Sensor Data Analysis

Pipelines enable the largest volume of both intra and international transportation of oil and gas and play critical roles in the energy sufficiency of countries. The biggest drawback with the use of pipelines for oil and gas transportation is the problem of oil spills whenever the pipelines lose containment. The severity of the oil spill on the environment is a function of the volume of the spill and this is a function of the time taken to detect the leak and contain the spill from the pipeline. A single leak on the Enbridge pipeline spilled 3.3 million liters into the Kalamazoo river while a pipeline rupture in North Dakota which went undetected for 143 days spilled 29 million gallons into the environment.Several leak detection systems (LDS) have been developed with the capacity for rapid detection and localization of pipeline leaks, but the characteristics of these LDS limit their leak detection capability. Machine learning provides an opportunity to develop faster LDS, but it requires access to pipeline leak datasets that are proprietary in nature and not readily available. Current LDS have difficulty in detecting low-volume/low-pressure spills located far away from the inlet and outlet pressure sensors. Some reasons for this include the following, leak induced pressure variation generated by these leaks is dissipated before it gets to the inlet and outlet pressure sensors, another reason is that the LDS are designed for specific minimum detection levels which is a percentage of the flow volume of the pipeline, so when the leak falls below the LDS minimum detection value, the leak will not be detected. Perturbations generated by small volume leaks are often within the threshold values of the pipeline\u27s normal operational envelop as such the LDS disregards these perturbations. These challenges have been responsible for pipeline leaks going on for weeks only to be detected by third-party persons in the vicinity of the leaks. This research has been able to develop a framework for the generation of pipeline datasets using the PIPESIM software and the RAND function in Python. The topological data of the pipeline right of way, the pipeline network design specification, and the fluid flow properties are the required information for this framework. With this information, leaks can be simulated at any point on the pipeline and the datasets generated. This framework will facilitate the generation of the One-class dataset for the pipeline which can be used for the development of LDS using machine learning. The research also developed a leak detection topology for detecting low-volume leaks. This topology comprises of the installation of a pressure sensor with remote data transmission capacity at the midpoint of the line. The sensor utilizes the exception-based transmission scheme where it only transmits when the new data differs from the existing data value. This will extend the battery life of the sensor. The installation of the sensor at the midpoint of the line was found to increase the sensitivity of the LDS to leak-induced pressure variations which were traditionally dissipated before getting to the Inlet/outlet sensors. The research also proposed the development of a Leak Detection as a Service (LDaaS) platform where the pressure data from the inlet and the midpoint sensors are collated and subjected to a specially developed leak detection algorithm for the detection of pipeline leaks. This leak detection topology will enable operators to detect low-volume/low-pressure leaks that would have been missed by the existing leak detection system and deploy the oil spill response plans quicker thus reducing the volume of oil spilled into the environment. It will also provide a platform for regulators to monitor the leak alerts as they are generated and enable them to evaluate the oil spill response plans of the operators

Fifth-generation small cell backhaul capacity enhancement and large-scale parameter effect

The proliferation of handheld devices has continued to push the demand for higher data rates. Network providers will use small cells as an overlay to macrocell in fifth-generation (5G) for network capacity enhancement. The current cellular wireless backhauls suffer from the problem of insufficient backhaul capacity to cater to the new small cell deployment scenarios. Using the 3D digital map of Lagos Island in the Wireless InSite, small cells are deployed on a street canyon and in high-rise scenarios to simulate the backhaul links to the small cells at 28 GHz center frequency and 100 MHz bandwidth. Using a user-defined signal to interference plus noise ratio-throughput (SINR-throughput) table based on an adaptive modulation and coding scheme (MCS), the throughput values were generated based on the equation specified by 3GPP TS 38.306 V15.2.0 0, which estimates the peak data rate based on the modulation order and coding rate for each data stream calculated by the propagation model. Finding shows achieved channel capacity is comparable with gigabit passive optical networks (GPON) used in fiber to the ‘X’ (FTTX) for backhauling small cells. The effect of channel parameters such as root mean squared (RMS) delay spread and RMS angular spread on channel capacity are also investigated and explained

End-To-End Security in Communication Networks: A Review

Digital communications and e-commerce reshape corporate processes and add new risks to business activities. The recent outbreak of the coronavirus (COVID 19) pandemic, has led to an increase in the demand for instant messaging and videoconferencing. There has been the need to maximize the availability of messaging, and mostly videoconference platforms. These platforms provide end-to-end communications services. Organizations have asked staff to work from home which necessitates work from home as well as meeting up with regular work schedule meetings which are carried out via these platforms mentioned. Communication Networks provide channels for such tasks to be carried out. These networks are used for the transmission for a wide range of valuable and confidential information. As a result, they draw the interest of persons who want to intercept or manipulate data or interrupt or damage the storage or communication of the networks. In this study a review of security as it pertains end-to-end connection is presented, we looked at a brief background of the issues which includes a description of security engineering with attendant examples, made our findings and observations. The paper ends with a brief in the form of a case study of a few ICT firms that provide end-to-end services. Such services fall into the category of instant messaging (IM), videoconferencing and remote management, showing how much they value end-to-end encryption and the impact it could have on their businesses

Advancing PoC Devices for Early Disease Detection using Graphene-based Sensors

Early detection of diseases is key to better disease management and higher survival rates. It aims at discovering conditions that have already produced biochemical changes in body fluids, but have not yet reached a stage of apparent physical symptoms or medical emergency. Therefore, early disease detection relies majorly on biochemical testing of biological fluids such as serum, in the body. The laboratories for these tests require biochemical-based instrumentations that are bulky and not commonly available especially in developing countries. Moreover, the tests are expensive and require trained personnel to conduct and interpret results. On the other hand, Lab-on-a-Chip (LOC) biosensors have a potential to miniaturize the entire biochemical/laboratory methods of diagnostics into versatile, inexpensive and portable devices with great potential for low-cost Point-of-Care (POC) applications. They are capable of providing accurate and precise information on the measured health indices for sub-clinical level of diseases. Nanotechnology-inspired biosensors have further advantages of low limit of detection (required for early diagnosis), real-time analysis and lesser sample volume requirement. Of all other nanomaterials, graphene is said to be the most promising, suitable for biosensing due to its biocompatibility and consistent signal amplification even under the conditions of harsh ionic solutions found in the human body. This paper reviews the potentials, fundamental concepts and related works in using Graphene-based Field Effect Transistors (GFETs) as biosensors for early disease diagnosis. This paper also highlights a low-cost patterning mechanism for preparing SiO2/Si substrate for metal deposition (of the source and drain electrodes of FETs)

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Surface pipeline leak detection using realtime sensor data analysis

The most common type of Leak Detection System (LDS) is designed to detect leaks that generate a sufficient pressure variation which can be detected at either the inlet or the outlet sensors. However, the pressure variation from low-pressure leaks at locations far away from the inlet and outlet is dissipated before they arrive at these sensors. Thus, these leaks can continue for weeks before they are detected. This work developed a leak detection architecture which comprised of a pressure sensor installed in the middle of the pipeline segment. This sensor was found to be more sensitive to leak-induced pressure variations from leaks far away from the inlet and outlet and this was because leak-induced pressure variation was higher at the midpoint even when it had diminished to zero at the inlet or outlet. The work also developed a Leak Detection as a Service (LDaaS) platform, which utilizes the leak detection algorithm developed from this research and pressure values from the inlet and midpoint sensor to detect real-time pipeline leaks. The midpoint sensor utilizes an exception-based transmission protocol capable of extending the sensor's battery life. Operators can subscribe to the Leak Detection service by installing the midpoint sensor and transmitting the inlet and the midpoint pressure values to the platform. This platform will monitor the pipeline in real-time and detect both the high-pressure and low-pressure leaks which would have ordinarily been missed by the traditional LDSs

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