Monitoring Techniques Research Articles

Future directions in geological research impacting renewable energy and carbon capture: A synthesis of sustainable management techniques

Geological research plays a pivotal role in shaping the future of renewable energy and carbon capture initiatives, offering insights into sustainable management techniques. This review synthesizes the future directions in geological research that impact renewable energy and carbon capture, focusing on sustainable management techniques. Future geological research will increasingly focus on enhancing the integration of renewable energy sources into existing energy systems. This includes the development of innovative geological mapping techniques to identify and characterize renewable energy resources with greater precision, aiding in the selection of optimal sites for energy production. Additionally, there will be a growing emphasis on utilizing geological data to assess the feasibility of carbon capture and storage (CCS) projects, particularly in volcanic regions, where the unique geological characteristics offer potential for efficient carbon sequestration. Another key aspect of future geological research is the advancement of monitoring and modeling techniques to evaluate the long-term performance and environmental impact of renewable energy and CCS projects. This includes the use of advanced geophysical and geochemical methods to monitor subsurface changes associated with energy extraction and carbon storage, ensuring the effectiveness and safety of these practices. Furthermore, future research will explore the potential of geological formations, such as deep saline aquifers and depleted oil and gas reservoirs, for large-scale carbon storage. This will involve developing strategies to enhance storage capacity and mitigate the risk of CO2 leakage, contributing to the sustainable management of carbon emissions. In conclusion, future geological research will play a critical role in advancing renewable energy and carbon capture technologies, offering sustainable management techniques that are essential for addressing climate change. By focusing on innovative mapping, monitoring, and modeling approaches, researchers can pave the way for a more sustainable energy future.

25 years recording of the long-base fluid tiltmeter installed at 2.850 m a.s.l. observatory of Pizzi Deneri on Etna volcano

Etna is a highly active volcano with frequent eruptions and is closely observed with different monitoring techniques. One of the monitoring methods is a network of short-base tiltmeters that provides continuous control of ground deformation. Since 1996, this network has been integrated with a long-base fluid tiltmeter installed in two 80 m long connected tunnels at the high-altitude (2850 m a.s.l.) volcanological observatory of Pizzi Deneri (PDN) located on the volcano’s summit. The instrumentation was devised with an innovative configuration composed of mercury, free to move along the entire length of the tiltmeter in response to the ground tilt, and laser sensors to measure the changes in the mercury levels. In this study, for the first time, we present the entire 25-year series of data recorded by this instrumentation. During this long interval, the Etna volcano was characterized by numerous major eruptions due to dyke intrusions. The tilt variations recorded in the short-term during the rapid intrusive phases are presented and discussed. These signals contributed both to the modeling of eruptive processes, and in particular in the case of the 2002–2003 eruption, to the real-time understanding of the ongoing eruptive phenomenon, supporting the correct hazard assessment. In the medium-long term (years to decades), we show and discuss that the PDN signal is mainly related to the marked sliding of the entire eastern sector of the volcano.Graphical

A new approach to monitoring solvent extraction processes for the nuclear industry

As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is building the Beartooth test bed. Beartooth will include a cascade of centrifugal contactors, glove box lines, and solidification and dissolution equipment to aid in the progression of novel separation techniques and to provide hands-on opportunities to early-career separation engineers. Beartooth will incorporate novel monitoring techniques using sensors and machine learning algorithms to inform process operators of separation conditions. This research is examining unconventional monitoring technologies such as incorporating acoustic microphones, vibration sensors, infrared cameras, red-green-blue color sensors, among others into nuclear separation processes. Research is ongoing in the use of machine learning methods to detect faults and alert operators of typical and anomalous events. Results from this research have the potential to impact safeguards-by-design efforts and real-time decision making. This overview will detail preliminary measurement results from acoustic, vibration, and color sensors.

Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest

Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG sensors are catching huge research attention in different applications for measurement and monitoring of physiological parameters. In this paper, we report a simple technique for remote monitoring of sleep disorder using a smart vest implemented with four FBG stress sensors located at different sides of the vest and free space optics (FSO) transmission system. The sleep disorder of the patient is monitored in real time through shifts in the original Bragg wavelengths of sensors by stress loading during random changes in patient’s sleeping postures. The reflected wavelength from a stress loaded sensor at a certain posture is transmitted over 0.5 km long FSO channel towards remote medical center, photodetected, and then can be processed in a PC to record the restlessness in a certain time interval in terms of total number of times sleeping postures are changed, total time spent at a certain posture etc. To correctly detect the stress loaded FBG sensor at the medical center, various parameters of FBG sensors and demultiplexer are carefully adjusted to minimize the power leakages from unloaded sensors that may result into errors in the detection. Maximum dynamic range around 45 dB has been achieved ensuring accurate detection. This study not only provides a cost-efficient and non-intrusive solution for monitoring the sleep disorder of patients but also can be used for real-time monitoring of various other ailments, such as lung, brain, and cardiac diseases in future.

A novel approach to runway overrun risk assessment using FRAM and flight data monitoring

Abstract Runway overruns (ROs) are the result of an aircraft rolling beyond the end of a runway, which is one of the accident’s types that most frequently occurs on aviation. The risk of an RO arises from the synergistic effect among its precursors, such as unstable approaches, long touchdowns and inadequate use of deceleration devices. To analyse this complex socio-technical system, the current work proposes a customised functional resonance analysis method, called FRAM-FDM, as traditional techniques of risk and safety assessment do not identify the interactions and couplings between the various functional aspects of the system itself, especially regarding human and organisational components. Basically, FRAM-FDM is the coupling of a traditional FRAM with flight data monitoring (FDM) techniques, used here to quantify the variabilities of the flight crew performance while executing the required activity (i.e. the landing). In this proposal, these variabilities (i.e. the FRAM functions aspects) are aggregated by the addend of a logistic regression, resulting in a model to evaluate the flare operations and the brake application profile effect on the remaining distance to the end of the runway, used as a reference to classify the landing as acceptable or not. The present application of the FRAM-FDM assesses the operational risk of a sample fleet in overrunning the runway during landing, highlighting the brake pedal application profile as the most relevant contributor. The model improves the knowledge about the system behaviour, being useful to direct flight crew training.

Review and analysis of the failure risk mitigation via monitoring for monopile offshore wind structures

Structural health monitoring systems are gaining more attention in the offshore wind industry as they offer valuable insights into the integrity status of operating assets. Whilst most Class Society Rules and International Standards recognise the importance of implementing a condition-based maintenance strategy, they only provide theoretical guidance. Ultimately, it is the asset owner’s responsibility to determine how to integrate this strategy into their broader operation and maintenance planning, including identifying which components to monitor and which monitoring techniques to employ. The present paper applies the Failure Mode, Effects, and Criticality methodology to identify the most critical failure modes to prioritise for monopile offshore wind structures. Hence, the potential benefits of incorporating structural health monitoring systems within a condition-based maintenance strategy and exploiting lifetime extension possibilities are assessed. To achieve this, a novel 4-step methodology is introduced, involving understanding failure mechanisms (time-dependent behaviour and pattern), current regime (inspections at intervals), monitoring options (both direct and indirect), improvement potential evaluation through 5 key performance indicators relevant for optimal O&M, which is more comprehensive and realistic than considering only monetary consequence of failure. The study addressed the prevailing failure modes in 5 categories, namely fatigue, corrosion, deformation, buckling and displacement, and connection failure, aiming to demonstrate potential improvement in terms of risk mitigation against these failure modes. The results of this study can significantly help offshore wind developers optimise where and how to allocate their resources for structural health monitoring, resulting in long-term cost reduction opportunities.

Comprehensive calibration and laboratory validation of a Micro Electromechanical System sensor–based flexible inclinometer

Abstract Real-time monitoring of slopes, tunnels, and dams is important for ensuring the long-term stability and reliability of such structures. Despite the successes of current technologies in many applications, a gap still exists in certain areas such as precision in steep slopes and complex soil conditions. This study has designed a flexible inclinometer based on an array of Micro Electromechanical System (MEMS) sensors to enhance the accuracy and flexibility of existing monitoring techniques. The inclination angle of each flexible inclinometer measurement unit was measured to monitor the horizontal or vertical displacement of the target structural body. We used the Levenberg–Marquardt (LM) algorithm for optimizing the MEMS sensors based calibration and designed multiple experiments to test the accuracy of the proposed method. Experimental results show that the calibrated flexible inclinometer measurement unit has an inclination angle of less than 0.04° and the accuracy of the flexible inclinometer lies within ±0.4 mm in the horizontal attitude and 1.6 mm in the vertical attitude. Our research has developed a novel tool for geotechnical engineering monitoring that can aid in increasing the precision of real-time assessment and prediction of structural stability.

Peri-Ultrasound Modeling to Investigate the Performance of Different Nonlinear Ultrasonic Techniques for Damage Monitoring in Plate Structures

Abstract Peri-ultrasound modeling which is based on nonlocal peridynamics is found and proven to be effective for modeling nonlinear waves propagating and interacting with damages in structures. This work presents the peri-ultrasound modeling to investigate the performance of three commonly used nonlinear ultrasonic (NLU) techniques—wave mixing, higher harmonic generation (HHG), and sideband peak count-index (or SPC-I) for monitoring damages (or cracks) in three-dimensional (3D) plate structures. Cracks can be defined as “thin cracks” and “thick cracks” according to the horizon size mentioned in peridynamics. Peri-ultrasound modeling results reveal that the SPC-I results are consistent with other reported numerical modeling and experimental results available in the literature. However, the modulation indicator (MI) from the wave mixing model only shows consistent trends for thin cracks but not for thick cracks and its reliability is affected by the initial excitation bandwidth. The relative acoustic nonlinearity factor β from the HHG technique shows consistent trends for thick cracks but not for thin cracks. It can be concluded from the obtained parametric analysis results that the SPC-I technique is more robust and reliable for monitoring damages in engineering structures.

A comparative evaluation of sample preparation techniques for the therapeutic monitoring of carbamazepine in human plasma by RPLC/DAD

A comparative evaluation of sample preparation techniques for the therapeutic monitoring of carbamazepine in human plasma by RPLC/DAD

Diffusion-driven fed-batch fermentation in perforated ring flasks.

Simultaneous membrane-based feeding and monitoring of the oxygen transfer rate shall be introduced to the newly established perforated ring flask, which consists of a cylindrical glass flask with an additional perforated inner glass ring, for rapid bioprocess development. A 3D-printed adapter was constructed to enable monitoring of the oxygen transfer rate in the perforated ring flasks. Escherichia coli experiments in batch were performed to validate the adapter. Fed-batch experiments with different diffusion rates and feed solutions were performed. The adapter and the performed experiments allowed a direct comparison of the perforated ring flasks with Erlenmeyer flasks. In batch cultivations, maximum oxygen transfer capacities of 80mmolL-1h-1 were reached with perforated ring flasks, corresponding to a 3.5 times higher capacity than in Erlenmeyer flasks. Fed-batch experiments with a feed reservoir concentration of 500gglucoseL-1 were successfully conducted. Based on the oxygen transfer rate, an ammonium limitation could be observed. By adding 40gammonium sulfateL-1 to the feed reservoir, the limitation could be prevented. The membrane-based feeding, an online monitoring technique, and the perforated ring flask were successfully combined and offer a new and promising tool for screening and process development in biotechnology.

Environmental DNA metabarcoding reflects spatiotemporal fish community shifts in the Scheldt estuary

Estuarine ecosystems face increasing anthropogenic pressures, necessitating effective monitoring methods to mitigate their impacts on the biodiversity they harbour. The use of environmental DNA (eDNA) based detection methods is increasingly recognized as a promising tool to complement other, potentially invasive monitoring techniques. Integrating such eDNA analyses into monitoring frameworks for large ecosystems is still challenging and requires a deeper understanding of the scale and resolution at which eDNA patterns may offer insights in species presence and community composition space and time. The Scheldt estuary, characterized by its diverse habitats and complex currents, is one of the largest Western European tidal river systems. Until now, it remains challenging to obtain accurate information on fish communities living in and migrating through this ecosystem, consequently confining our knowledge to specific locations. To explore the potential of eDNA based monitoring, we simultaneously combine stow net fishing with eDNA metabarcoding, to assess spatiotemporal shifts in the Scheldt estuary's fish communities. In total, we detected 71 fish species in the estuary using eDNA metabarcoding, partly overlapping with historic fish community data gathered at the different study locations and in contrast to only 42 species using stow net fishing during the same survey period. Community compositions found by both detection methods varied among sampling locations, driven by a clear correlation to the salinity gradient. Limited effects of sampling depth and tide were observed on the eDNA metabarcoding data, allowing a significant reduction of the eDNA sampling effort for future eDNA fish monitoring campaigns in this study system. Our results further demonstrate that seasonal shifts in fish species occurrence can be detected using eDNA metabarcoding. Combining eDNA metabarcoding and stow net fishing further enhances our understanding of this vital waterway's diverse fish populations, allowing a higher resolution and more efficient monitoring strategy.

Stereoscopic Monitoring technique for motor area tumors.

The balance between a comprehensive intraoperative-neuromonitoring (IONM) for both upper and lower limb while ensuring the reliability of motor-evoked potentials (MEPs) is paramount in motor area surgery. It's commonly hard to obtain a good simultaneous stimulation of both upper/lower limb, a series of factors can bias MEP accuracy and inappropriate stimulation intensity can result in unreliable monitoring. The presented IONM technique is based on the concurrent use of both transcranial and strip electrodes to facilitate a simultaneous monitoring of both upper and lower limb at optimized stimulation intensities, increasing IONM accuracy, during motor-area surgery. Ten nonconsecutive motor area tumors were studied. A good visualization of both limbs was observed in the series, at a low amperage (1.2mA from strip-electrode and 165,3mA from transcranial-electrode). Our analysis confirm concordance between IONM data and postoperative outcome: a MEP reduction >20% and >50% was coherent with postoperative mRS changes, without false negative IONM. The technique demonstrated to be accurate in providing a good simultaneous neurophysiological evaluation of both upper/lower limb with an optimized and stimulation amplitude; it warrants a low encumbrance of electrodes in the surgical field. Results confirm its feasibility and reliability. Considering the relatively small sample, this study represents a "proof of concept" rather than an applicability study.

Statistical process monitoring creates a hemodynamic trajectory map after pediatric cardiac surgery: A case study of the arterial switch operation

AbstractPostoperative critical care management of congenital heart disease patients requires prompt intervention when the patient deviates significantly from clinician‐determined vital sign and hemodynamic goals. Current monitoring systems only allow for static thresholds to be set on individual variables, despite the expectations that these signals change as the patient recovers and that variables interact. To address this incongruency, we have employed statistical process monitoring (SPM) techniques originally developed to monitor batch industrial processes to monitor high‐frequency vital sign and hemodynamic data to establish multivariate trajectory maps for patients with d‐transposition of the great arteries following the arterial switch operation. In addition to providing multivariate trajectory maps, the multivariate control charts produced by the SPM framework allow for assessment of adherence to the desired trajectory at each time point as the data is collected. Control charts based on slow feature analysis were compared with those based on principal component analysis. Alarms generated by the multivariate control charts are discussed in the context of the available clinical documentation.

Top predator status and trends: ecological implications, monitoring and mitigation strategies to promote ecosystem-based management

The conservation and management of marine ecosystems hinge on a comprehensive understanding of the status and trends of top predators. This review delves into the ecological significance of marine top predators, examining their roles in maintaining ecosystem stability and functioning through an integrated analysis of current scientific literature. We first assess the efficacy of various monitoring methods, ranging from traditional field observations to cutting-edge technologies like satellite tracking and environmental DNA (eDNA) analysis and evaluating their strengths and limitations in terms of accuracy, spatial coverage, and cost-effectiveness, providing resource managers with essential insights for informed decision-making. Then, by synthesizing data from diverse marine ecosystems, this study offers a comprehensive overview of the trends affecting top predator populations worldwide. We explore the multifaceted impacts of human activities, climate change, and habitat degradation on the abundance and distribution of these key species. In doing so, we shed light on the broader implications of declining top predator populations, such as trophic cascades and altered community structures. Following a thorough assessment of successful strategies for reversing the decline of top predators, a compilation of recommendations is presented, encompassing effective governance interventions. A crucial aspect of effective ecosystem-based management is the implementation of robust monitoring strategies. Mitigation measures are imperative to reverse the adverse impacts on marine top predators. We present a comprehensive array of mitigation options based on successful case studies. These include the establishment of marine protected areas, the enforcement of fisheries regulations, and the promotion of sustainable fishing practices. We deepen the synergies between these strategies and their potential to mitigate human-induced stressors on top predator populations to safeguard their pivotal role in maintaining marine ecosystem structure and function. By examining marine top predators’ ecological significance, analyzing population trends, discussing monitoring techniques, and outlining effective mitigation strategies, we provide a comprehensive resource for researchers, policymakers, and stakeholders engaged in fostering ecosystem-based management approaches. We conclude that integrating these insights into current management frameworks will be essential to safeguard both top predators and the broader marine environment for future generations.

Utilizing InSAR for Surface Stability Monitoring in Mining Sites: A Case Study of Sukari Gold Mine in Egypt

Abstract. The monitoring of mining sites is crucial as a result of the safety and environmental issues linked to mining activities. Traditional surveying and monitoring tools exhibit limitations in terms of spatial coverage and are associated with various concerns regarding time and cost efficiency. Hence, the mining industry has shown increased interest in the time-series analysis of interferometric synthetic aperture radar (InSAR). This is primarily due to its precision and wide spatial coverage advantages. This study employed an InSAR technology, persistent scatterer interferometry (PSI), to identify potential geohazards within the Sukari gold mine (SGM), located in Egypt. A total of 23 images were collected from the ascending orbit of Sentinel-1 sensor between January 7, 2022, and November 3, 2022, for the SGM mining location. Results revealed relatively stable measurements in non-mining areas of the site, with a surface displacement within ±10mm per year in the line-of-sight (LOS) direction. However, several active mining operation areas showed significant surface motion. Various sections of the open pit of the SGM exhibited displacement, including the southwestern edge (-16.8mm) and the northeastern edge (35.7mm), and the highest velocity was detected at a section of the western edge, which demonstrated an annual displacement of -64mm in the LOS. Additionally, notable surface motion was observed in some parts of the two tailings storage facilities at the site. The results of this study illustrate the importance of InSAR techniques in mining site-wide coverage stability monitoring.

The Influence of Project Monitoring Process on the Performance of Technical Vocational Educational and Training Institution’s Construction in Kenya

This study aimed to investigate the influence of project monitoring processes on the performance of TVET Institution's construction in Kenya. This study adopted a descriptive design with a target population of 146 and a sample size of 107 of TVET Institution's Construction. Data collection instrument was questionnaire. The quantitative data were analysed using inferential analysis. From the findings, the project planning process (β = 19.062, p< 0.05) was found to be positively significant to the performance of TVET Institution's construction in Kenya. The study recommends that various aspects that contribute to monitoring Processes and enhancing the performance of construction projects are monitoring project variations, monitoring project progress, resource tracking and use of monitoring tools and techniques.

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

This paper addresses the application of artificial intelligence (AI) techniques for the classification of faults in wind turbine. Wind Energy Conversion Systems have become a focal point in the research of renewable energy sources. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. Many condition monitoring techniques that are based on steady-state analysis are being applied to wind generators. Bearing faults in this generator causes mechanical vibrations and the variations in the air gap density. The air gap flux density is modulated and currents are generated at different frequencies related to the mechanical vibrations. Characteristic bearing frequencies are associated with the physical construction of bearing and its failure mode. Classical spectral analysis using the Fourier transform was used to detect different bearing failure modes. The magnitudes of the frequencies components formed by the bearing defect are small compared to the rest of the current spectrum. This large difference in magnitude makes difficult the detection and classification of bearing faults. In this paper, we show that the utilization of different approaches of artificial intelligence such as neuronal network (NN) and support vector machines (SVM) can be discriminate different failure modes and gives a good basis for an automatic and non-invasive condition monitoring for wind turbine.

Informing conservation management of the bilby (Macrotis lagotis) in the Pilbara: a review of research and future directions

For threatened species that occur across multi-use landscapes, a coordinated cross-tenure management approach is desirable to achieve long-term conservation outcomes. To provide a comprehensive understanding of the progress towards achieving conservation priorities for the greater bilby (Macrotis lagotis) in the Pilbara region of Western Australia (WA), and inform ongoing research and funding investment, we review a research program that has focused on the species over the past decade. Priorities were initially established at a stakeholder workshop in 2013, and research targeting key areas has greatly enhanced ecological knowledge of the bilby in the Pilbara and elsewhere in WA. Highlights include the development of a non-invasive scat DNA-based abundance monitoring technique, an improved understanding of habitat use and diet, including the importance of Acacia spp. that host root-dwelling larvae, and the threat posed by introduced predators. Proposed future research directions focus on better understanding habitat requirements and the association between key food resources and fire, evaluating genetic structure and gene flow at the landscape-scale and examining threat interactions. Management to mitigate a range of threats including introduced predators, grazing livestock/feral herbivores and fire is recommended, with consideration for an integrated approach.

Monitoring underground hydrogen storage migration and distribution using time-lapse acoustic waveform inversion

The ongoing global energy shift from fossil fuels to renewable sources highlights the importance of underground hydrogen storage (UHS) as a sustainable mechanism to counterbalance the seasonal inconsistencies of renewable energy. Comparable to geological CO2 sequestration, UHS necessitates precise subsurface imaging and a thorough understanding of hydrogen behavior within geological formations, including its location and migration patterns. Since direct subsurface measurement is unattainable, there is a pronounced need for robust subsurface characterization and monitoring techniques. This research focuses on UHS feasibility in enduring storage scenarios, employing seismic monitoring strategies adapted from CO2 storage practices. Specifically, we integrate Continuous Active Source Seismic Monitoring (CASSM) and Time-Lapse Full Waveform Inversion (TLFWI) to scrutinize UHS. The study employs synthetic crosshole surveys that initially model the coexistence of hydrogen and water within fractured reservoirs, aiming to pinpoint hydrogen-related velocity alterations in acoustic waveforms. Subsequently, we utilize a time-lapse synthetic model of hydrogen injection to emulate CASSM observations. Utilizing TLFWI in conjunction with White's model, we successfully process the CASSM data to discern and quantify temporal variations in velocity and saturation, which effectively maps the spatial and temporal distribution of mobile hydrogen. The results affirm the capabilities of TLFWI and CASSM as proficient monitoring systems for UHS. This study aspires to offer a dependable methodology for the administration of large-scale and long-term geological hydrogen storage.

Seismic Characterization of Subsurface Structures at Rock Valley, Nevada

ABSTRACT The Source Physics Experiment (SPE) aims at improving explosion monitoring techniques by investigating source characteristics of chemical explosions in geologic formations. One of the critical tasks in Rock Valley Direct Comparison (RV/DC), SPE phase III, is to prepare for the main experiment by characterizing the subsurface structures at the test site. Based on the seismic data acquired during an accelerated-weight-drop (AWD) seismic survey at Rock Valley, we first pick the P-wave first-arrival travel times and derive a P-wave velocity model using the adjoint-state first-arrival travel-time tomography. We then apply reverse-time migration to the processed seismic data and obtain high-resolution images of the subsurface structures along the two main survey lines. Our migration results show several reflectors corresponding to major geologic formation boundaries. We employ a multitask machine learning model to enhance the reverse-time migration images and identify faults from these images. We find that our automatically picked faults correlate well with the locations of known faults in the region in addition to many geologically undetected faults. Our subsurface characterization results refine our understanding of the geology in this region and provide valuable velocity and structural information for RV/DC geologic model building and fault identification.