Electromagnetic ultrasonic guided wave long-term monitoring and data difference adaptive extraction method for buried oil-gas pipelines

An increasing number of buried oil-gas pipelines are generated in recent years and defects occur with higher probability in their long-running process. This work proposes an electromagnetic ultrasonic guided wave (EUGW) long-term monitoring scheme for buried oil-gas pipelines and a data difference adaptive extraction method for the monitoring data. The T (0,1) mode guided wave is selected because of its non-dispersive characteristic. For the electromagnetic acoustic transducer (EMAT), a circumferentially magnetized nickel strap is bonded on the pipe to provide the bias magnetic field and the excitation coils are winded on the nickel strap to provide the dynamic magnetic field. A detection stub is planted on the ground and the connector of buried coils is installed in the stub. The difference array is constructed and adaptive gain and attenuation are performed in the data difference adaptive extraction method. The EUGW long-term monitoring scheme is on-site applied for buried oil pipes in Jinan city, Shandong province, China. The EMAT is installed and buried with the pipe, and the periodical detection is conducted from December 2015 till now, once per month. A pit is found and verified by excavation at the distance 9.25 m from the buried EMAT on the flow direction. On-site detection results show that the data extraction method can dramatically improve the signal to noise ratio of the monitoring data and accurately extract the difference and variation of the pipe's structural health condition that occurred in the long-term service. The EUGW long-term monitoring scheme is proved to be a feasible method with prosperous prospect for the structural health monitoring of buried oil-gas pipelines

Development of Electrostatic and Piezoelectric Sensor Arrays for Determining the Velocity and Concentration Profiles and Size Distribution of Pneumatically Conveyed Bulk Solids

One way countries around the world are increasing the proportion of renewable fuels for electricity generation is to convert coal fired power stations to co-fired (biomass/coal fired) or converting coal fired power stations to burn only biomass fuels. This however has led to measurement challenges monitoring the complex multi-phase flow of the pulverised fuels entering the furnace due to the complex shape of biomass particles. To meet these measurement challenges a novel electrostatic sensor array and piezoelectric sensor array have been developed. The electrostatic sensor array consists of an array of electrostatic electrode pairs that span the diameter of the pipe. Consequently the electrostatic sensor array is capable of determining the particle velocity and concentration profiles as well as detecting specific flow regimes such as roping. The piezoelectric impact sensor array consists of an array of piezoelectric individual impact sensors that span the diameter of the pipe. The piezoelectric sensor array is capable of determining the particle concentration and size distribution profiles. Experimentation has been carried out on laboratory scale pneumatic conveying systems using a variety of materials such as coal, biomass, coal/biomass blends and plastic shot. Experiments using the electrostatic sensor array have shown that it is indeed capable of determining the particle velocity and concentration profiles in both dilute developed and undeveloped flows. Analysis of the standard deviation of the velocity profiles as well as the correlation coefficient profiles have indicated that parts of the pipe cross section have a more stable flow compared to others. Data obtained through on-line and off-line experimentation using the piezoelectric sensor array has shown that through selective frequency filtering of the impact signal particle size can be determined assuming the particle velocity and the mechanical properties of the conveyed pulverised materials are known. By using a threshold voltage to determine when an impact has occurred on each element of the piezoelectric sensor array the particle concentration profile has been determined. The concentration profiles measured by the piezoelectric sensor array were verified using the electrostatic sensor array

Microwave measurement techniques for industrial process monitoring and quality control

Process monitoring and quality control by sensor measurements are essential for the automatisation and optimisation of many industrial manufacturing processes. This thesis is concerned with microwave sensing, which is a measurement modality with potential to improve the in-line sensing capabilities in several industries. Two process-industrial measurement problems are considered that involve the estimation and detection of permittivity variations for granular media in a fluidised or flowing state. For these problems, we present microwave measurement techniques based on resonant cavity sensors, accounting for the electromagnetic design and modelling of the sensor, signal processing algorithms, and experimental evaluation in relevant industrial settings. These measurement techniques make simultaneous use of multiple resonant modes with spatial diversity to improve the measurement capabilities. Furthermore, we exploit model-based signal processing algorithms where knowledge of the underlying physics is utilised for improved estimation and detection.The first problem is to monitor the internal state of a pharmaceutical fluidised bed process used for film-coating and drying of particles. The metal vessel that confines the process is here treated as a cavity resonator and the complex resonant frequency of eight different cavity modes are measured using a network analyser. Based on the resonant frequencies, we estimate parameters in a low-order model for the spatial permittivity distribution inside the vessel, which can be related to process states such as the liquid and solid content of the particles in different regions.The second measurement problem is an aspect of quality control, namely the detection of undesirable objects in flowing granular materials. We present measurement techniques based on resonant cavity sensors that are capable to detect the presence of small dielectric objects embedded in a flowing granular material. Detection algorithms that exploit the statistics of the noise caused by material density fluctuations and the characteristic signatures caused by an object passage event, are evaluated based on experiments which lead to quantitative assessments of the detection performance

On-Line Nonintrusive Detection of Wood Pellets in Pneumatic Conveying Pipelines Using Vibration and Acoustic Sensors

This paper presents a novel instrumentation system for on-line nonintrusive detection of wood pellets in pneumatic conveying pipelines using vibration and acoustic sensors. The system captures the vibration and sound generated by the collisions between biomass particles and the pipe wall. Time-frequency analysis technique is used to eliminate environmental noise from the signal, extract information about the collisions, and identify the presence of wood pellets. Experiments were carried out on an industrial pneumatic conveying pipeline to assess effectiveness and operability. The impacts of various factors on the performance of the detection system are compared and discussed, including different sensing (vibration sensor versus acoustic sensor), different time-frequency analysis methods (wavelet-based denoising versus classic filtering), and different system installation locations

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance

Particle size distribution estimation of a powder agglomeration process using acoustic emissions

Washing powder needs to undergo quality checks before it is sold, and according to a report by the partner company, these quality checks include an offline procedure where a reference sieve analysis is used to determine the size distributions of the powder. This method is reportedly slow, and cannot be used to measure large agglomerates of powders. A solution to this problem was proposed with the implementation of real time Acoustic Emissions (AE) which would provide the sufficient information to make an assessment of the nature of the particle sizes. From the literature reviewed for this thesis, it was observed that particle sizes can be monitored online with AE but there does not appear to be a system capable of monitoring particle sizes for processes where the final powder mixture ratio varies significantly. This has been identified as a knowledge gap in existing literature and the research carried out for this thesis contributes to closing that gap. To investigate this problem, a benchtop experimental rig was designed. The rig represented limited operating conditions of the mixer but retained the critical factors. The acquired data was analysed with a designed hybrid signal processing method based on a time domain analysis of impact peaks using an amplitude threshold approach. Glass beads, polyethylene and washing powder particles were considered for the experiments, and the results showed that within the tested conditions, the designed signal processing approach was capable of estimating the PSD of various powder mixture combinations comprising particles in the range of 53-1500 microns, it was also noted that the architecture of the designed signal processing method allowed for a quicker online computation time when compared with other notable hybrid signal processing methods for particle sizing in the literature