CERES :: Browsing by Author "Nnabuife, Somtochukwu Godfrey"

Browsing by Author "Nnabuife, Somtochukwu Godfrey"

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Open Access
Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system
(Elsevier, 2021-11-24) Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Rana, Zeeshan A.; Whidborne, James F.
A method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.
Open Access
A comparative analysis of different hydrogen production methods and their environmental impact
(MDPI, 2023-11-29) Nnabuife, Somtochukwu Godfrey; Darko, Caleb Kwasi; Obiako, Precious Chineze; Kuang, Boyu; Sun, Xiaoxiao; Jenkins, Karl W.
This study emphasises the growing relevance of hydrogen as a green energy source in meeting the growing need for sustainable energy solutions. It foregrounds the importance of assessing the environmental consequences of hydrogen-generating processes for their long-term viability. The article compares several hydrogen production processes in terms of scalability, cost-effectiveness, and technical improvements. It also investigates the environmental effects of each approach, considering crucial elements such as greenhouse gas emissions, water use, land needs, and waste creation. Different industrial techniques have distinct environmental consequences. While steam methane reforming is cost-effective and has a high production capacity, it is coupled with large carbon emissions. Electrolysis, a technology that uses renewable resources, is appealing but requires a lot of energy. Thermochemical and biomass gasification processes show promise for long-term hydrogen generation, but further technological advancement is required. The research investigates techniques for improving the environmental friendliness of hydrogen generation through the use of renewable energy sources. Its ultimate purpose is to offer readers a thorough awareness of the environmental effects of various hydrogen generation strategies, allowing them to make educated judgements about ecologically friendly ways. It can ease the transition to a cleaner hydrogen-powered economy by considering both technological feasibility and environmental issues, enabling a more ecologically conscious and climate-friendly energy landscape.
Open Access
A computational fluid dynamics study of flared gas for enhanced oil recovery using a micromodel
(MDPI, 2022-12-19) Were, Stephanie; Nnabuife, Somtochukwu Godfrey; Kuang, Boyu
The current handling of gas associated with oil production poses an environmental risk. This gas is being flared off due to the technical and economic attractiveness of this option. As flared gases are mainly composed of methane, they have harmful greenhouse effects when released into the atmosphere. This work discusses the effectiveness of using this gas for enhanced oil recovery (EOR) purposes as an alternative to flaring. In this study, a micromodel was designed with properties similar to a sandstone rock with a porosity of 0.4, and computational fluid dynamics (CFD) techniques were applied to design an EOR system. Temperature effects were not considered in the study, and the simulation was run at atmospheric pressure. Five case studies were carried out with different interfacial tensions between the oil and gas (0.005 N/m, 0.017 N/m, and 0.034 N/m) and different injection rates for the gas (1 × 10−3 m/s, 1 × 10−4 m/s, and 1 × 10−6 m/s). The model was compared with a laboratory experiment measuring immiscible gas flooding. Factors affecting oil recoveries, such as the interfacial tension between oil and gas, the viscosity, and the pressure, were studied in detail. The results showed that the surface tension between the oil and gas interphase was a limiting factor for maximum oil recovery. The lower surface tension recovered 33% of the original oil in place. The capillary pressure was higher than the pressure in the micromodel, which lowered the amount of oil that was displaced. The study showed the importance of pressure maintenance to increase oil recovery for immiscible gas floods. It is recommended that a wider set of interfacial tensions between oil and gas be tested to obtain a range at which oil recovery is maximum for EOR with flared gas.
Open Access
Development of gas-liquid flow regimes identification using a noninvasive ultrasonic sensor, belt-shape features, and convolutional neural network in an S-shaped riser
(IEEE, 2021-07-14) Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Whidborne, James F.; Rana, Zeeshan A.
The problem of classifying gas-liquid two-phase flow regimes from ultrasonic signals is considered. A new method, belt-shaped features (BSFs), is proposed for performing feature extraction on the preprocessed data. A convolutional neural network (CNN/ConvNet)-based classifier is then applied to categorize into one of the four flow regimes: 1) annular; 2) churn; 3) slug; or 4) bubbly. The proposed ConvNet classifier includes multiple stages of convolution and pooling layers, which both decrease the dimension and learn the classification features. Using experimental data collected from an industrial-scale multiphase flow facility, the proposed ConvNet classifier achieved 97.40%, 94.57%, and 94.94% accuracy, respectively, for the training set, testing set, and validation set. These results demonstrate the applicability of the BSF features and the ConvNet classifier for flow regime classification in industrial applications.
Open Access
Development of gas-liquid slug flow measurement using continuous-wave Doppler ultrasound and bandpass power spectral density
(MDPI, 2021-01-08) Nnabuife, Somtochukwu Godfrey; Sharma, Prafull; Aburime, Ebuwa Iyore; Long’or Lokidor, Pauline; Bello, Abdulrauf
This paper addresses the issues of slug detection and characterization in air-water two-phase flow in a vertical pipeline. A novel non-invasive measurement technique using continuous-wave Doppler ultrasound (CWDU) and bandpass power spectral density (BPSD) is proposed for multiphase flow applications and compared with the more established gamma-ray densitometry measurement. In this work, analysis using time-frequency analysis of the CWDU is performed to infer the applicability of the BPSD method for observing the slug front and trailing bubbles in a multiphase flow. The CWDU used a piezo transmitter/receiver pair with an ultrasonic frequency of 500 kHz. Signal processing on the demodulated signal of Doppler frequency was done using the Butterworth bandpass filter on the power spectral density which reveals slugs from background bubbles. The experiments were carried out in the 2” vertical pipeline-riser at the process system engineering laboratory at Cranfield University. The 2-inch test facility used in this experiment is made up of a 54.8 mm internal diameter and 10.5 m high vertical riser connected to a 40 m long horizontal pipeline. Taylor bubbles were generated using a quick-closing air valve placed at the bottom of the riser underwater flow, with rates of 0.5 litres/s, 2 litres/s, and 4 litres/s. The CWDU spectrum of the measured signal along with the BPSD method is shown to describe the distinctive nature of the slugs
Open Access
Dual-Chamber microbial fuel cell for Azo-Dye degradation and electricity generation in Textile wastewater treatment
(Elsevier, 2025-09) Ndive, Julius Nnamdi; Eze, Simeon Okechukwu; Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Rana, Zeeshan A.
Textile wastewater, particularly azo dyes, poses significant environmental challenges due to its poor biodegradability and toxicity. This study explores a dual-chamber microbial fuel cell (MFC) for simultaneous wastewater treatment and electricity generation. The MFC consists of an anaerobic anode chamber and an aerobic cathode chamber, separated by a proton exchange membrane (PEM). Electroactive microorganisms in the anode chamber metabolize organic substrates, including azo dye contaminants, breaking them down into simpler by-products. Electrons released during this process flow through an external circuit to generate current, while protons migrate across the PEM to the cathode chamber for oxygen reduction. Electrochemically active microbes were isolated from azo-dye-contaminated soil, and their degradation abilities validated through assays. Optimized carbon-based electrodes and a Nafion 117 PEM were used to enhance conductivity and microbial activity. UV–Vis spectroscopy tracked dye degradation, with the absorbance peak of reactive yellow dye at 410 nm decreasing from 2.9 to 0.4, indicating effective azo-bond cleavage. The MFC achieved peak voltage and current outputs of 0.20 mV and 0.16 mA, respectively, demonstrating its dual functionality. Adding NaCl as a supporting electrolyte further improved ionic conductivity and performance. This study demonstrates MFC technology as a sustainable solution for industrial wastewater challenges, integrating microbial degradation with bioelectricity generation. Future work should address scalability, operational stability, and advanced electrode designs to enhance its practical applications.
Open Access
Exergy Analysis and Evaluation of the Different Flowsheeting Configurations for CO2 Capture Plant Using 2-Amino-2-Methyl-1-Propanol (AMP)
(MDPI, 2019-06-24) Osagie, Ebuwa; Aliyu, Aliyu M.; Nnabuife, Somtochukwu Godfrey; Omoregbe, Osaze; Etim, Victor
This paper presents steady-state simulation and exergy analysis of the 2-amino-2-methyl-1-propanol (AMP)-based post-combustion capture (PCC) plant. Exergy analysis provides the identification of the location, sources of thermodynamic inefficiencies, and magnitude in a thermal system. Furthermore, thermodynamic analysis of different configurations of the process helps to identify opportunities for reducing the steam requirements for each of the configurations. Exergy analysis performed for the AMP-based plant and the different configurations revealed that the rich split with intercooling configuration gave the highest exergy efficiency of 73.6%, while that of the intercooling and the reference AMP-based plant were 57.3% and 55.8% respectively. Thus, exergy analysis of flowsheeting configurations can lead to significant improvements in plant performance and lead to cost reduction for amine-based CO2 capture technologies.
Open Access
Gas-liquid flow regimes identification using non-intrusive Doppler ultrasonic sensor and convolutional recurrent neural networks in an S-shaped riser
(Elsevier, 2022-01-19) Kuang, Boyu; Nnabuife, Somtochukwu Godfrey; Sun, Shuang; Whidborne, James F.; Rana, Zeeshan A.
The problem of gas-liquid (two-phase) flow regime identification in an S-shaped riser using an ultrasonic sensor and convolutional recurrent neural networks (CRNN) is addressed. This research systematically evaluates three different schemes with four CRNN-based classifiers over fourteen experiments. Four metrics are used as the evaluation criteria: categorical accuracy, categorical cross-entropy, mean square error (MSE), and computation graph complexity. Compared with existing results, a compatible performance is achieved while considerably reducing the model complexity. The testing and validation accuracies were 98.13% and 98.06%, while the complexity decreased by 98.4% (only 117,702 parameters). The proposed approach is i) accurate, low complexity, and non-intrusive and hence suitable for industry, and ii) could provide a benchmark for flow regime identification.
Open Access
Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps
(Elsevier, 2019-08) Nnabuife, Somtochukwu Godfrey; Pilario, Karl Ezra; Lao, Liyun; Cao, Yi; Shafiee, Mahmood
The accurate prediction of flow regimes is vital for the analysis of behaviour and operation of gas/liquid two-phase systems in industrial processes. This paper investigates the feasibility of a non-radioactive and non-intrusive method for the objective identification of two-phase gas/liquid flow regimes using a Doppler ultrasonic sensor and machine learning approaches. The experimental data is acquired from a 16.2-m long S-shaped riser, connected to a 40-m horizontal pipe with an internal diameter of 50.4 mm. The tests cover the bubbly, slug, churn and annular flow regimes. The power spectral density (PSD) method is applied to the flow modulated ultrasound signals in order to extract frequency-domain features of the two-phase flow. Principal Component Analysis (PCA) is then used to reduce the dimensionality of the data so as to enable visualisation in the form of a virtual flow regime map. Finally, a support vector machine (SVM) is deployed to develop an objective classifier in the reduced space. The classifier attained 85.7% accuracy on training samples and 84.6% accuracy on test samples. Our approach has shown the success of the ultrasound sensor, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which is beneficial to operators in industrial practice. The use of a non-radioactive and non-intrusive sensor also makes it more favorable than other existing techniques.
Open Access
Integration of renewable energy sources in tandem with electrolysis: a technology review for green hydrogen production
(Elsevier, 2025-03-10) Nnabuife, Somtochukwu Godfrey; Hamzat, Abdulhammed K.; Whidborne, James F.; Kuang, Boyu; Jenkins, Karl W.
The global shift toward sustainable energy solutions emphasises the urgent need to harness renewable sources for green hydrogen production, presenting a critical opportunity in the transition to a low-carbon economy. Despite its potential, integrating renewable energy with electrolysis to produce green hydrogen faces significant technological and economic challenges, particularly in achieving high efficiency and cost-effectiveness at scale. This review systematically examines the latest advancements in electrolysis technologies—alkaline, proton exchange membrane electrolysis cell (PEMEC), and solid oxide—and explores innovative grid integration and energy storage solutions that enhance the viability of green hydrogen. The study reveals enhanced performance metrics in electrolysis processes and identifies critical factors that influence the operational efficiency and sustainability of green hydrogen production. Key findings demonstrate the potential for substantial reductions in the cost and energy requirements of hydrogen production by optimising electrolyser design and operation. The insights from this research provide a foundational strategy for scaling up green hydrogen as a sustainable energy carrier, contributing to global efforts to reduce greenhouse gas emissions and advance toward carbon neutrality. The integration of these technologies could revolutionise energy systems worldwide, aligning with policy frameworks and market dynamics to foster broader adoption of green hydrogen.
Open Access
Investigation of water droplet size distribution in conventional and sustainable aviation turbine fuels
(Society of Automotive Engineers, 2022-05-17) Ugbeh Johnson, Judith; Carpenter, Mark; Okeke, Nonso Evaristus; Nnabuife, Somtochukwu Godfrey; Mai, Nathalie
Water droplet size variation has been established in the literature as an important variable that influences the behavior and characteristics of water in fuel emulsion. However, with the growing demand for sustainable aviation fuels (SAF), no data is available that shows how these fuels will affect the size of dispersed water droplets and their frequency distribution. To address this lack of knowledge, this study explores and presents experimental results on the characterization of dispersed water droplets in alternative fuels and Jet A-1 fuel under dynamic conditions. The alternative fuels comprised of two fully synthetic fuels, two fuels synthesized from bio-derived materials, and one bio-derived fuel. The data and statistics presented reveal that water droplet frequency and size distribution are sensitive to changes in fuel composition. Observations showed an evident transition of the droplet percentile over time in the cumulative frequency distribution; this could be attributed to droplet coalescence to form larger droplets. Mean droplet diameters between 3 and 6 μm were observed for all the fuels tested. With further analysis based on recommendations proposed in this work, the data may assist in providing insight to filter manufacturers.
Open Access
New multiphase flow measurements for slug control.
(Cranfield University, 2019-01) Nnabuife, Somtochukwu Godfrey; Whidborne, James F.; Lao, Liyun
Severe slug flow is undesirable in offshore oil production systems, particularly for late-life fields. Active control through choking is one of the effective approaches to mitigating/controlling severe slug flow in oil production pipeline-riser systems. However, existing active slug control systems may limit oil production due to overchoking. Another problem in most active control systems is their dependency on information obtained from subsea measurements such as riser base pressure for active slug flow control. Both of these control challenges have been satisfactorily solved through the introduction of new multiphase flow topside measurements that are reliable and efficient in providing flow information for active slug control systems. By using Venturi multiphase flow topside measurements and Doppler ultrasonic measurements, an active slug flow control system is proposed to suppress severe slug flows without limiting oil production. Experimental and simulated results demonstrate that under active slug control, the proposed system is able not only to suppress slug flow but also to increase oil production compared to manual choking. Another objective of this research was to assess the applicability of continuous-wave Doppler ultrasonic (CWDU) techniques for accurate identification of gas-liquid flow regimes in pipeline-riser systems. Firstly, flow regime classification using the kernel multi-class support-vector machine (SVM) approach from machine learning (ML) was investigated. For a successful industrial application of this approach, the feasibility of conducting principal component analysis (PCA) for visualising the information from intrinsic flow regime features in two-dimensional space was also investigated. The classifier attained 84.6% accuracy on test samples and 85.7% accuracy on training samples. This approach showed the success of the CWDU, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which would be possible for industrial application. Secondly, an approach that classifies the flow regime by means of a neural network operating on extracted features from the flow’s ultrasonic signals using either discrete wavelet transform (DWT) or power spectral density (PSD) was proposed. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thereby showing the superiority of the DWT in feature extraction of flow regime classification. This approach demonstrates the employment of a neural network and DWT for flow regime identification in industrial applications, using CWDU. The scheme has significant advantages over other techniques in that it uses a non-radioactive and non-intrusive sensor. The two investigated methods for gas-liquid two-phase flow regime identification appear to be the first known successful attempts to objectively identify gas-liquid flow regimes in an S-shape riser using CWDU. The CWDU approaches for flow regime classification on pipeline-riser systems were successful and proved possible in industrial applications.
Open Access
Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networks
(Elsevier, 2020-07-26) Nnabuife, Somtochukwu Godfrey; Kuang, Boyu; Whidborne, James F.; Rana, Zeeshan
The problem of predicting the regime of a two-phase flow is considered. An approach is proposed that classifies the flow regime using Deep Neural Networks (DNNs) operating on features extracted from Doppler ultrasonic signals of the flow using the Fast Fourier Transform (FFT) is proposed. The features extracted are categorised into one of the four flow regime classes: the annular, churn, slug, and bubbly flow regimes. The scheme was tested on signals from an experimental facility. To increase the number of samples without losing key classification information, this paper proposes a Twin-window Feature Extraction (TFE) technique. To further distinguish the performance of the proposed approach, the classifier was compared to four conventional machine learning classifiers: namely, the AdaBoost classifier, bagging classifier, extra trees classifier, and decision tree classifier. Using the TFE features, the DNNs classifier achieved a higher recognition accuracy of 99.01% and greater robustness for the overfitting challenge, thereby showing the superiority of the DNNs in flow regime classification when compared to the four conventional machine-learning classifiers, which had classification accuracies of 55.35%, 86.21%, 82.41%, and 80.03%, respectively. This approach demonstrates the application of DNNs for flow regime classification in chemical and petroleum engineering fields, using a clamp-on Doppler ultrasonic sensor. This appears to be the first known successful attempt to identify gas-liquid flow regimes in an S-shaped riser using Continuous Wave Doppler Ultrasound (CWDU) and DNNs
Open Access
Present and projected developments in hydrogen production: a technological review
(Elsevier, 2022-04-05) Nnabuife, Somtochukwu Godfrey; Ugbeh-Johnson, Judith; Okeke, Nonso Evaristus; Ogbonnaya, Chukwuma
Energy supplies that are safe, environmentally friendly, dependable, and cost-effective are important for society's long-term growth and improved living standards, though political, social, and economic barriers may inhibit their availability. Constantly increasing energy demand is induced by substantial population growth and economic development, putting an increasing strain on fossil fuel management and sustainability, which account for a major portion of this rising energy demand and, moreover, creates difficulties because of greenhouse gas emissions growth and the depletion of resources. Such impediments necessitate a global shift away from traditional energy sources and toward renewables. Aside from its traditional role, is viewed as a promising energy vector and is gaining international attention as a promising fuel path, as it provides numerous benefits in use case scenarios and, unlike other synthesized carbon-based fuels, could be carbon-free or perhaps even negative on a life-cycle criterion. Hydrogen (H2) is one of the most significant chemical substances on earth and can be obtained as molecular dihydrogen through various techniques from both non-renewable and renewable sources. The drive of this paper is to deliver a technological overview of hydrogen production methods. The major challenges, development and research priorities, and potential prospects for H2 production was discussed.
Open Access
The prospects of hydrogen in achieving net zero emissions by 2050: a critical review
(Elsevier, 2023-05-25) Nnabuife, Somtochukwu Godfrey; Oko, Eni; Kuang, Boyu; Bello, Abdulrauf; Onwualu, Azikiwe Peter; Oyagha, Sherry; Whidborne, James F.
Hydrogen (H2) usage was 90 tnes (Mt) in 2020, almost entirely for industrial and refining uses and generated almost completely from fossil fuels, leading to nearly 900 Mt of carbon dioxide emissions. However, there has been significant growth of H2 in recent years. Electrolysers' total capacity, which are required to generate H2 from electricity, has multiplied in the past years, reaching more than 300 MW through 2021. Approximately 350 projects reportedly under construction could push total capacity to 54 GW by the year 2030. Some other 40 projects totalling output of more than 35 GW are in the planning phase. If each of these projects is completed, global H2 production from electrolysers could exceed 8 Mt by 2030. It's an opportunity to take advantage of H2S prospects to be a crucial component of a clean, safe, and cost-effective sustainable future. This paper assesses the situation regarding H2 at the moment and provides recommendations for its potential future advancement. The study reveals that clean H2 is experiencing significant, unparalleled commercial and political force, with the amount of laws and projects all over the globe growing quickly. The paper concludes that in order to make H2 more widely employed, it is crucial to significantly increase innovations and reduce costs. The practical and implementable suggestions provided to industries and governments will allow them to fully capitalise on this growing momentum.
Open Access
Prospects of low and zero-carbon renewable fuels in 1.5-degree net zero emission actualisation by 2050: a critical review
(Elsevier, 2022-10-12) Anika, Ogemdi Chinwendu; Nnabuife, Somtochukwu Godfrey; Bello, Abdulrauf; Okoroafor, Esuru Rita; Kuang, Boyu; Villa, Raffaella
The Paris Climate Agreement seeks to keep global temperature increases under 2° Celsius, ideally 1.5° Celsius. This goal necessitates significant emission reductions. By 2030, emissions are expected to range between 52 and 58 GtCO2e from their 2016 level of approximately 52 GtCO2e. This review paper explores a number of low and zero-carbon renewable fuels, such as hydrogen, green ammonia, green methanol, biomethane, natural gas, and synthetic methane (with natural gas and synthetic methane subject to CCUS both at processing and at final use) as alternative solutions for providing a way to rebalance transition paths in order to achieve the goals of the Paris Agreement while also reaping the benefits of other sustainability targets. The results show renewables will need to account for approximately 90% of total electricity generation by 2050 and approximately 25% of non-electric energy usage in buildings and industry. However, low and zero-carbon renewable fuels currently only contributes about 15% to the global energy shares, and it will take about 10% more capacity to reach the 2050 goal. The transportation industry will need to take important steps toward energy efficiency and fuel switching in order to achieve the 20% emission reduction. Therefore, significant new commitments to efficient low-carbon alternatives will be necessary to make this enormous change. According to this paper, investing in energy efficiency and low-carbon alternative energy must rise by a factor of about five by 2050 in comparison to 2015 levels if the 1.5 °C target is to be realised.
Open Access
Pseudo-image-feature-based identification benchmark for multi-phase flow regimes
(Elsevier, 2020-12-08) Kuang, Boyu; Nnabuife, Somtochukwu Godfrey; Rana, Zeeshan
Multiphase flow is a prevalent topic in many disciplines, and flow regime identification is an essential foundation in multiphase flow research. Computer vision and deep learning have achieved numerous excellent models, but many have not demonstrated satisfactory performance in fundamental research, including flow regime identification. This research proposes an advanced pseudo-image feature (PIF) as the flow regime descriptor and a benchmark of multiple deep learning classifiers. The PIF simulates the image format and compactly encodes the flow regime to a pseudo-image, which explicitly displays the implicit flow regime signals. This research further evaluates three proposed and five existing popular deep learning classifiers. The proposed benchmark provides a baseline for applying deep learning in flow regime identification. The proposed fully convolutional network (FCN) classifier achieved state-of-the-art performance, and the testing and verification accuracy respectively reached 99.95% and 99.54%. This research suggests that PIF has an excellent capability for flow regime representation, and the proposed deep learning classifiers achieve superior performance in flow regime identification compared to the existing classifiers. Industries can utilize the proposed multiphase flow identification technology to obtain greater production efficiency, productivity, and financial gain
Open Access
Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser
(Elsevier, 2024-02) Kuang, Boyu; Nnabuife, Somtochukwu Godfrey; Whidborne, James F.; Sun, Shuang; Zhao, Junjie; Jenkins, Karl W.
Two-phase flow regime identification is an essential transdisciplinary topic that spans digital signal processing, artificial intelligence, chemical engineering, and energy. Multiphase flow systems significantly impact pipeline safety, heat transfer, and pressure drop; therefore, precisely identifying the governing flow regime is crucial for effective modeling and design. However, it is challenging due to the geometrical complexity of flow regimes in multiphase flow. With the advances in sensor measurement and machine learning, applying non-destructive tests and self-supervised learning to practical industrial problems has become technically feasible and cost-effective. This study applies a weak-supervised learning-based two-phase flow regime identification solution using a non-destructive tests ultrasonic sensor in an S-shape riser experimental bed by proposing a self-supervised feature extraction algorithm. The proposed self-supervised feature extraction algorithm reduces time/labor consumption and human error in data annotation using SSL, which provides full supervision without manual annotation. The self-supervised feature extraction algorithm uses a bottlenecked neural network and encoder-decoder structure to extract compact features. The self-supervised feature extraction algorithm performance is evaluated using an established convolutional neural network-based classifier. The source data was collected from a 10 × 50 m riser experimental rig. The dataset is made available to the community as part of this study. The performance of the approach is comparable with state-of-the-art methods and is also the first successful attempt to apply self-supervised learning to multiphase flow regime ultrasonic signal identification. This study achieved 98.84%, 0.000663, 0.00312, and 7.71 × 10^5 in accuracy, root mean square error, categorical cross-entropy, and model complexity, respectively. The practical experiment justifies the robustness, fairness, and practicability in the practical application environment. The proposed self-supervised feature extraction brings new approaches and inspirations for the feature extraction step in identifying a two-phase flow regime, and it will be beneficial to generalize this study in different riser shapes in the future.
Open Access
Semantic terrain segmentation in the navigation vision of planetary rovers – a systematic literature review
(MDPI, 2022-11-01) Kuang, Boyu; Gu, Chengzhen; Rana, Zeeshan A.; Zhao, Yifan; Sun, Shuang; Nnabuife, Somtochukwu Godfrey
Background: The planetary rover is an essential platform for planetary exploration. Visual semantic segmentation is significant in the localization, perception, and path planning of the rover autonomy. Recent advances in computer vision and artificial intelligence brought about new opportunities. A systematic literature review (SLR) can help analyze existing solutions, discover available data, and identify potential gaps. Methods: A rigorous SLR has been conducted, and papers are selected from three databases (IEEE Xplore, Web of Science, and Scopus) from the start of records to May 2022. The 320 candidate studies were found by searching with keywords and bool operators, and they address the semantic terrain segmentation in the navigation vision of planetary rovers. Finally, after four rounds of screening, 30 papers were included with robust inclusion and exclusion criteria as well as quality assessment. Results: 30 studies were included for the review, and sub-research areas include navigation (16 studies), geological analysis (7 studies), exploration efficiency (10 studies), and others (3 studies) (overlaps exist). Five distributions are extendedly depicted (time, study type, geographical location, publisher, and experimental setting), which analyzes the included study from the view of community interests, development status, and reimplementation ability. One key research question and six sub-research questions are discussed to evaluate the current achievements and future gaps. Conclusions: Many promising achievements in accuracy, available data, and real-time performance have been promoted by computer vision and artificial intelligence. However, a solution that satisfies pixel-level segmentation, real-time inference time, and onboard hardware does not exist, and an open, pixel-level annotated, and the real-world data-based dataset is not found. As planetary exploration projects progress worldwide, more promising studies will be proposed, and deep learning will bring more opportunities and contributions to future studies. Contributions: This SLR identifies future gaps and challenges by proposing a methodical, replicable, and transparent survey, which is the first review (also the first SLR) for semantic terrain segmentation in the navigation vision of planetary rovers.
Open Access
Slug flow control in an S-shape pipeline-riser system using an ultrasonic sensor
(Elsevier, 2021-12-11) Nnabuife, Somtochukwu Godfrey; Tandoh, Henry; Whidborne, James F.
Slugging flow poses significant challenges to the offshore multiphase flowline and riser systems. Slug flow is characterized by an uneven flow regime whereby pipeline pressures, temperatures, or flow volume rates fluctuate. One of the most common causes of severe slugging is low pressures which causes buildup of fluid over time, consequentially causes flow and pressure oscillations. This mostly occurs in vertical risers or wells. The negative effects of severe slugging have prompted numerous studies, investments, and efforts to reduce or eliminate the slugging flow. Several active slugging control techniques have been investigated in the oil and gas industries for decades. However, many of these techniques still run the risk of limiting hydrocarbon production due to inappropriate over choking. Other challenges for active slug control include the fact that some systems rely mainly on subsea measurements such as riser base pressure, and most of these subsea measurements are costly, difficult to maintain, not always available, and can be unreliable. As a result, to achieve an efficient slugging control performance, reliable, robust, and efficient measurements that are more sensitive to slugging flow for control are required, which is the motivation for this work. The control of riser slug flow using non-radioactive, non-invasive, and non-intrusive Continuous-wave Doppler ultrasound has been investigated in this work, and provides good control performance. It achieved a larger valve opening than an open-loop unstable system. This outperforms manual choking, which maintains stability at a much lower valve opening.