Faculty Research Projects
Machine Learning based Defect Detection in Automated Ultrasonic Testing for Weld Inspection
Title:
Machine Learning based Defect Detection in Automated Ultrasonic Testing for Weld Inspection
Investigator:
Description:
Automated Ultrasonic Testing (AUT) is one of the Non-destructive testing (NDT) techniques used in the oil and gas, automotive and space industry. AUT can identify defects in pipeline girth weld inspection. After inspection operation, it is required that a licensed inspector detects, characterizes, and sizes defects in the AUT images of weld profile to accept or reject it. Although human inspectors are equipped with skills required for these critical tasks, they may misinterpret the weld profile due to personal judgment, work environment, or tedium. This is due to the multiple parameters they should consider and the high workload. Therefore, the wrong decisions may lead to costly mistakes and time waste. This project addresses AUT data misinterpretation by developing an AI-powered scheme that assists the human operator through AUT data interpretation during weld inspection. The current AI-based algorithms that detect flaws in ultrasonic data are built on synthetic data and laboratory-conducted weld inspections, which is not ideal for industrial applications. Development of our machine learning-based software is based on gigabytes of proprietary data provided by CRC-Evans. It detects and characterizes defects immediately after each welding profile scan to help the operator make accurate and fast decisions about the welding profile status.
Improving Sustainability of the Natural-gas Midstream Value Chain via Advanced Digital Twin Development
Title:
Improving Sustainability of the Natural-gas Midstream Value Chain via Advanced Digital Twin Development
Investigator:
Description:
The U.S. oil and gas midstream market will continue increasing significantly in the coming years, bringing tremendous business opportunities to the midstream industry. However, the industry is also facing enormous environmental challenges and renewable energy competition in the current era of industrial decarbonization. To support the healthy and sustainable development of the industry, the advanced digital twin development for a general natural-gas midstream value chain (NGMVC) will be performed in this project. It will develop rigorous process models from raw gas compression, gas/liquid separation, mercury removal, sweetening, dehydration, NGL recovery, and NGL fraction to LNG liquefaction and helium recovery. Other critical industrial sectors such as sulfur recovery, LNG storage, and nitrogen removal will also be modeled. Based on the NGMVC model, three key opportunities for enhancing sustainability will be investigated: (1) system-wise heat integration to increase energy efficiency; (2) industrial electrification for rotating and heating equipment of the NGMVC system to fundamentally reduce greenhouse gas emissions; and (3) sensitivity impact analysis under uncertainties and process upsets to improve the flexibility, adaptability, and resilience capability of the NGMVC system. The study will provide scientific knowledge, data, and valuable technical support for the long-term sustainable development of the natural gas midstream industry.
Multi-scale Modeling of LNG Pipeline Risk Assessment under Dual Impact of Flow induced Vibrations and Severe Weather Events
Title:
Multi-scale Modeling of LNG Pipeline Risk Assessment under Dual Impact of Flow-induced Vibrations and Severe Weather Events
Investigator:
Dr. Jiang Zhou
Description:
The transportation of liquefied natural gas (LNG) through pipelines is vital for meeting global energy demands. However, the integrity of LNG pipelines can be jeopardized by the combined effects of flow-induced vibrations and severe weather events. This proposal aims to develop a multi-scale modeling framework to assess the risks associated with LNG pipelines under these dual impacts. The research will focus on creating a coupled fluidstructure interaction model that integrates fluid dynamics, structural mechanics, and meteorological data. By considering various scales, from the overall pipeline system to individual components, the proposed model will provide a comprehensive evaluation of pipeline risks. Realistic meteorological data, including wind speed, temperature, and precipitation, will be incorporated to simulate the impact of severe weather events on the pipeline system. Quantitative risk metrics will be developed to assess fatigue life, stress levels, and failure probabilities, enabling a thorough understanding of pipeline safety and reliability. The model will be validated using experimental data, and case studies will be conducted to analyze the risk profiles of specific LNG pipeline systems. The outcomes of this research will contribute to improved risk assessment methodologies, inform pipeline design and maintenance strategies, and support decision-making for the secure and efficient transportation of LNG.
Phase II: Incipient Leakage Detection Through Embedded Sensors and AI on Drones Based on 5G
Title:
Phase II: Incipient Leakage Detection Through Embedded Sensors and AI on Drones Based on 5G
Investigator:
Description:
This is the second phase of the project, aiming to develop a high-speed and highly accurate hydrogen leak detection system using integrated sensors and fifth generation (5G) wireless technology. Hydrogen, as a clean and sustainable energy source, requires robust monitoring and detection systems for safe transportation. Existing sensing technologies have limitations in detecting early leaks in harsh outdoor environments like buried pipelines in remote areas. To overcome these limitations, this project proposes integrating sensors to compensate for the lack of information and coverage. It utilizes 5G wireless technology for real-time data analysis and transmission through edge computing. The system's key features include stability, low cost, and low maintenance. It accelerates data processing and enables real-time detection using advanced anomaly detection models. Its scalability and flexibility allow for integrating data from different sensors, improving detection capability. The project emphasizes a multi-sensor approach, integrating electromagnetic, ultrasonic, and optical sensors for increased accuracy and reliability in leak detection applications. Figure 1 shows the proposed architecture of a multi-sensor system based on 5G for ultra-fast, low-cost, and real-time detection and reduction of hydrogen leakage suitable for a wide range of environmental conditions. Overall, the proposed 5G-based sensing system with integrated sensors and AI-based detection algorithms offers a new solution to address the challenges of hydrogen leak detection, it enables safe and efficient hydrogen transportation, promoting sustainable energy practices.
Developing and formulating metal dithiolene near-IR tracers for pipeline leak detection
Title:
Developing and formulating metal dithiolene near-IR tracers for pipeline leak detection
Investigator:
Dr. Perumalreddy Chandrasekaran
Description:
Pipelines play an important role in transportation of crude oil from oil fields to refineries, where it is refined into fuels and other products, then from the refineries to end users. Transportation of crude oil and petroleum products using pipelines is a safe, economical, and clean technique, and over 70% of petroleum products are moved globally utilizing pipelines. However, the pipelines are prone to leaks and spills, due to corrosion or abrupt pressure change. Currently, traditional methods such as monitoring pressure drop, and volume change are a way to detect leaks. Although these methods are adequate, and yet cannot detect the leak location fast before the leak becomes a costly hazardous problem. Our research goal is to develop a fast and precise method for leak detection by developing nickel-dithiolene based near-IR tracers to be introduced into the pipelines at ppm level. These nickel dithiolene have strong absorption at near-IR region, and the leak could be detected along the pipeline using drones mounted with near-IR sensors.
A Prototype Thermoplastic Composite Pipe Support Pad for Preventing Corrosion
Title:
A Prototype Thermoplastic Composite Pipe Support Pad for Preventing Corrosion
Investigator:
Description:
Corrosion at pipe supports is a major cost to industry as well as a safety concern. Pipe support pads designed to minimize the entrapment of moisture while electrically isolating the pipe from the support can mitigate the problem. Our industrial mentor has a designed a “road-bump” type pipe support pad that has proven to be very effective. This project investigates alternative materials, specifically thermoplastic nanocomposites, and designs that can offer similar or greater performance at a reduced cost. The work builds off a previously funded CMMS grant in which we designed and built a unique rig for creep testing to study the behavior of thermoplastic composites under static compressive load. The current proposal includes improvements to the rig as well as a program to collect a much larger dataset for screening candidate pipe support pad materials. The proposal extends the work to include the development of a prototype and covers efforts to explore commercialization potential by leveraging resources from the Entrepreneurship Institute at Lamar University.
Digital Transformation of Industrial Asset Performance Management : Development of AI/ML Methods and Addressing Challenges to Deployment
Title:
Digital Transformation of Industrial Asset Performance Management : Development of AI/ML Methods & Addressing Challenges to Deployment
Investigator:
Description:
This project aims to continue and expand upon the work completed in the previous year’s project, “Intelligent/Adaptive Performance and Reliability Assessment Tools for End Users of Turbine-Compressor Trains". The focus will be on the digital transformation of industrial asset performance, mechanical reliability, and operation monitoring systems. The objectives of this project are two folds. One is to develop physics-informed machine learning (ML) models and tools that could assess the performance and component health conditions of the turbine-compressor train to facilitate the adaptation of condition-based maintenance (CBM) for end users. The second is to address the practical challenges to deploy and integrate the ML models into each company’s data pipeline with interactive user interface, to empower employees with enhanced performance & reliability monitoring functionalities. Several companies, including Golden Pass LNG, Motiva, Total Energies, have expressed interests in potential collaboration in this project.
Data mining of EnerG-ID site data to identify potential correlations and predictive patterns
Title:
Data mining of EnerG-ID site data to identify potential correlations and predictive patterns
Investigator:
Description:
A diverse population of microflora has been identified in oil reservoirs throughout the oil-producing regions of the world. These organisms are known to affect product quality, processing behavior and equipment integrity. Our knowledge in the literature to data relies upon samples that are collected at one location but processed and analyzed elsewhere, often days or weeks later. This delay results in final samples that deviate from the source, due to the dynamic behavior and relative instability of the organisms and samples being collected. As such, the data diverse and immediate data provided by EnerG-ID would allow for a significant paradigm shift of the understanding of dynamic petrochemical site behaviors. Utilizing the unique and robust data from EnerG-ID, the PI will perform data analysis to determine trends, biases, and correlations and identify exciting areas of data analysis and collection to further the understanding and effectiveness of microflora prediction, monitoring, treatment, and process development. The infancy of this analysis means that all results and findings will be at the bleeding-edge of field, potentially shaping the microflora and petrochemical mid-stream industries for the foreseeable future.
Do mergers, acquisitions, and corporate takeovers benefit shareholders of firms in energy businesses and related industries?
Title:
Do mergers, acquisitions, and corporate takeovers benefit shareholders of firms in energy businesses and related industries?
Investigator:
Description:
The purpose of this study is to detect and analyze if there are synergies and benefits in mergers, acquisitions, and corporate takeovers especially when there are vertical and horizontal integrations. This preliminary award will lead to additional funding in two ways:
- This project will help to expand the previous risk management grant funded by CMMS.
- Significant and useful discoveries will lead to applying for and obtaining a major grant from the US Department of Commerce EDA in the academic years 2024/2025.
Detection of Methane Leaks in Soils
Title:
Detection of Methane Leaks in Soils
Investigator:
Dr. Philip Cole
Description:
We propose the continuation of the successful 2018/2019/2020 CICE Projects: Quantitative Optical Gas Imaging of Methane Leaks using Drone-Mounted Infrared Camera Systems. We propose to expand these studies of identifying methane leaks from pipes buried in various soils with varying the flow rates and methane leakages. We seek to identify and quantify methane leaks remotely and empirically understand the fluid dynamics of methane leakage through various soils, which then may exit into the atmosphere. This research project promotes the CMMS’s mission in adapting novel and innovative methane-detection technologies for solving challenges faced by the petroleum industry in the midstream arena. Our research further promotes the public good by coordinating with the Texas Commission on Environmental Quality in protecting Texas’s public health and natural resources in providingaffordablecompliance. In 2003, the Texas Commissionon Environmental Qualityordered studies to determine whether Optical Gas Imaging technology, via infrared cameras, could be used to better monitor fugitive emissions, or addressing the inadequacies of the EPA’s Method 21. These studies impact three focus areas: Greenhouse Gas Management, Corrosion Detection, and Midstream Optimization. Not only is minimizing methane leaks good for the environment, but it also enhances the bottom line of industry profit margins.
Study on FLNG Sloshing Impacts
Title:
A Comprehensive Study on Sloshing Impacts for FLNG Gas Pre-treatment Systems via the Integration of Multi-scale Simulation, Experimental Validation, and AI Technologies
Investigator:
Dr. Qiang Xu
Description:
Liquid sloshing on vessels subjected to forced ocean-wave acceleration could cause
tremendous operating or safety issues for the FLNG (floating liquefied natural gas) process, such as the hydraulic jump and liquid entrainment in vapor phase of various separators. Hitherto, studies are still lacking in this important area. In this project, a comprehensive study will be performed to quantitatively investigate the sloshing impact on FLNG gas pre-treatment systems, including the maximum hydraulic jump, the maximum forces on equipment wall, and the decrease of separation efficiencies under different ocean-wave conditions. The study will firstly develop a multi-scale simulation scheme coupling computational fluid dynamics (CFD) and process modeling to simulate and characterize dynamic sloshing behaviors for FLNG gas pre-treatment units. Next, the experimental unit supported by Schlumberger Inc. will be employed to validate and refine the developed simulation models. Finally, artificial intelligence (AI) technologies such as the artificial neural network (ANN) method will be utilized to learn and predict the sophisticated sloshing behaviors effectively and efficiently based on abundant simulation results. The study will help characterizing and predicting sloshing impacts on FLNG gas pre-treatment systems. It could also help for the future design, control, and operation for floating production, storage, and offloading equipment.
Surge Analysis for LNG Loading Arms
Surge Analysis for LNG Loading Arms Using Fluid Dynamic Simulation for Suppression of Hydraulic Hammer and Optimization of Piping System
Investigators:
Dr. Xianchang Li
Dr. Xinyu Liu
Dr. Jenny Zhou
Description:
In a pipeline that transports liquids such as liquefied natural gas (LNG), the opening/closing of a valve during an emergency can cause a sudden change in velocity, which results in a hydraulic hammer. The high surge pressure may damage the pipeline, its supports and equipment such as pumps and sensors, hence considered a serious concern. The loading arm of an LNG plant is a critical equipment susceptible to hydraulic hammers. This objective of this project is to identify the issues and create effective solutions for the loading arm operations through fluid dynamic simulation of the entire system under various operation scenarios. By simulating the transient LNG flow in the pipeline, we can evaluate how valves, pumps, and other components dynamically interact with each other during a surge event. We will quantitatively determine the forces generated by transient pressure to help optimize the pipeline design and validate the surge suppression equipment to mitigate potentially catastrophic effect of hydraulic hammer/surge and other undesirable system transients like check valve and relief valve chatter. This project will empower Lamar’s professors to collaborate with local midstream industries. The results can serve as the pilot research for seeking larger external funding.
BLE-Based Cognitive IoT System for Pipeline Networks
A BLE-based Cognitive IoT system for Pipeline Sensor Networks
Investigator:
Dr. Xingya Liu
Description:
While many technologies (e.g., drone, RF, WiFi, Satellite, etc.) have been developed for monitoring systems, their prohibitively high installation complexity, short coverage range, low security-awareness, and/or high energy consumption make them unsuitable for large-scale sensors in long-distance monitoring systems, such as Pipeline. Billions of dollars are spent every year in this country on pipeline maintenance and repair due to the damage cost by natural and human factors, not to mention the indirect economic losses. On the other hand, the Bluetooth Low Energy (BLE) has been rapidly developed these years as a candidate for the backbone technique to fulfill the promise of the Internet of Things (IoT). It has been applied to many areas given its large-scale self-organized feature.
In this project, we plan to design a BLE-based sensor network for Midstream pipeline monitoring. BLE sensors are deployed as IoT tags to form a beacon-advertising system, which can uniquely identify each sensor in the system simultaneously and keep tracking their quantity. Other benefits derived from the
proposed system include the high quality of service, high security, low cost, and low energy consumption. The system can adapt to any onsite pipeline (sensor-side) and off-the-shelf smartphone/PC (receiver-side) without modification on their existing infrastructures.
Anti-corrosive Superhydrophobic Top Coating with High Mechanical Durability for Pipeline Infrastructures
Title:
Develop Anti-corrosive Superhydrophobic Top Coating with High Mechanical Durability for Pipeline Infrastructures
Researchers:
Description:
Corrosion, a significant problem of pipeline infrastructures, generates enormous costs to the oil and gas industries. The cost of corrosion to these industries surpasses $1.3 billion each year, according to NACE International’s estimates. Therefore, corrosion protection coatings with more extended performance are desired. Dr. Yao and his research group have been working on novel coatings specifically for metallic materials to enhance significant material properties such as anticorrosion. Results showed that the superhydrophobic top coating offer effective protection. However, mechanical durability remains a primary concern of these coatings; therefore, there is a need to develop and test durable superhydrophobic top coatings with enhanced surface resistance to corrosion. The main objectives of the proposed project are to develop and characterize novel top coatings for enhanced corrosion resistance and mechanical durability. Also, this project will study the mechanism that governs mechanical durability on the superhydrophobic top coatings.
AI Algorithms for Corrosion Prediction
Development of AI algorithms for Corrosion Prediction in Midstream Industry
Investigator:
Dr. Sidney Lin
Description:
Corrosion has been causing the mid-stream industry billions of dollars per year. Even though ultrasonic sensors and smart pigging technologies are available for corrosion measurement, these reactive approaches are always too late. There are rigorous first principles (electrochemistry, reaction kinetics, thermodynamics, etc.) based models for corrosion prediction, however, due to inherent model complexity, they are very difficult to be implemented for online corrosion prediction.
This project aims at developing accurate AI algorithms based on simulation data generated from sound first-principles based models. The hydrocarbon composition and physical property data will represent real-world data from different wells and tanks. If successful, the research results will help bridge the gap from “post-corrosion” measurement to proactive corrosion management, revolutionize the practice of corrosion management, enhance the reliability of mid-stream assets, improve operation efficiency and reduce potential environmental risks
Drone and AI Solutions for Oil and Gas Pipeline Resilience
DARTS – Drone and Artificial Intelligence Reconsolidated Technological Solution for Increasing Oil and Gas Pipeline Resilience
Investigator:
Dr. Seokyon Hwang
Description:
Pipelines are the critical asset of the oil and gas midstream industry for transporting the essential energy resources. Meanwhile, the leakage of pipelines can impose significant impacts on public safety, the economy, and the ecosystem. The need for reliable pipeline operation and effective preventive maintenance grows as the demand for oil and gas increases. Among various problems causing pipeline failures, this research focuses on the root causes that can contribute to developing damages, such as cracks or fractures, on pipes and pipe joints, which, in turn, result in oil and gas leakage. The root causes to be tackled in this study include pipeline misalignment, deterioration of supporting structure, and soil movement near the pipeline. The ultimate goal of this research endeavor is to develop DARTS – Drone and Artificial Intelligence Reconsolidated Technological Solution. The specific purposes of DARTS are to detect the targeted problems and predict the progress of the detected problems by using periodically collected image data, aiming to support decision-making for predictive maintenance. Within the scope of the proposed project, the research team will conduct experiments to examine the feasibility of DARTS. To this end, the team will develop an early-stage model for collecting data—still images and video streams—and algorithms for identifying the targeted problems. The model and algorithms will be tested at the lab and field on a small-scale in collaboration with a midstream industry expert. DARTS is envisioned to help the midstream industry with enhancing pipeline maintenance, increasing system resiliency, and promoting environmental and public safety.
Design, Surface Modification of Corrosion Resistant High-Entropy Alloys
Design and Surface Modification of Corrosion Resistant High-Entropy Alloys for Oil and Gas Pipelines
Investigator:
Dr. Zhe Fan
Description:
Corrosion of oil and gas pipelines results in significant economic costs and substantial damages in midstream industry. The pipelines in service are generally made of cast iron and steels which may not be corrosion resistant. Operating pipelines in a safe, environmentally friendly, and economically efficient manner, demands adequate replacements of the existing pipeline materials with advanced alloys or time-efficient maintenance and repair of these materials. In this project, we aim to i) design corrosion resistant high-entropy alloys* as potential replacements for the existing pipeline materials, and ii) explore advanced surface modification techniques to improve the corrosion resistance of alloys. A direct comparison will be drawn between pipeline steels and high-entropy alloys with/without surface modification. This project will uncover the corrosion responses of these alloys along with the associated microstructural and chemical changes. The research results can reveal the underlying mechanisms for corrosion resistant high-entropy alloys and provide novel surface modification methods to improve the corrosion resistance of pipeline materials, offering alternative solutions to alleviate corrosion cost and damage in pipeline operation.
*High entropy-alloys generally contain 4 or more principal elements in near equiatomic composition. The name, high-entropy alloys, comes from the high mixing entropy due to multiple principal elements.
Deep Learning-Based Defect Classification and Prediction for Compressors
Deep Learning-based Defect-Classification and Prediction for Midstream Compressors
Investigator:
Dr. Maryam Hamidi
Description:
In the previous phase of this project, compressors’ microphone data, provided by Well Checked Systems, was successfully analyzed and a deep learning-based auditory model for compressor anomaly was developed. Currently at Well Checked, when an anomaly is detected, a maintenance technician is assigned to manually inspect and diagnose the type of anomaly. Next, the technician reports information such as the time stamp of inspection and the type of failure diagnosed. In this proposed project, Lamar University will continue collaborating with Well Checked to automate the process, by predicting the time and type of anomaly and issuing maintenance orders to repair the anomaly. Here, first a database will be designed for technician reports, real time and historical time-series data, including the raw audio data and the 2-dimensional representations of before and after an anomaly. Next, a model will be developed and trained to predict the time and type of anomaly (classification), and the prediction model will be tested with real-time monitoring of Well Checked compressors. A failure prediction will be considered successful once confirmed by a technician, and finally, the training dataset will be updated using a feedback loop when a technician finds a new failure.
Assessment of Corrosion Development
Assessment of Corrosion Development
Investigator:
Dr. Rafael Tadmor
Description:
We developed a surface property (interfacial modulus, Gs), that quantifies the resilience of a solid to interact with a given liquid. With the help of Gs, we can characterize the outer walls of a unit (e.g. the outer wall of a pipe) in terms of its resilience to interact with liquids of choice. Such an outer wall will also be influenced by corrosion that develops at the inner side of that wall. This is due to the corrosion being a voltaic phenomenon and metals being a conductor: an electric potential at one point is conducted throughout the metal. A paint coating at the outer surface can only reduce the corrosion on that side, but not eliminate it. The paint itself is also influenced by the corrosion since this is the medium in which the metal is oxidized. This proposal aims to use our lab expertise in characterizing surfaces, especially utilizing the interfacial modulus, to assess the amount of corrosion in a wall. This includes a cleaning protocol after which we perform the tests that will allow us to characterize Gs, and from it we will develop a calibration curve, that would allow us to estimate the amount of corrosion.
Hybrid Model to Detect Crude Oil Leaks in a Pipeline System
Title:
Develop a Hybrid Model to Detect Crude Oil Leak in Midstream Pipeline System
Investigators:
Dr. Yueqing Li
Dr. Xinyu Liu
Description:
Pipelines are considered as one of the most practical transportation means in midstream for petroleum products, such as crude and product oil. Therefore, the safe operation of pipeline is extremely important. Pipeline failures such as blockage and leakage may result in environmental pollution, economic losses, and even serious threats to human safety and property. Consequently, monitoring pipelines is an important task. Detection of exact fault quantity and its location is necessary for smooth operation of factories and industries and environmental safety. It is essential to develop a timely and reliable method to detect and locate pipeline leakage. This project aims to develop a hybrid model to detect crude oil leak in midstream pipeline system. With the self-developed pipeline system, hardware-based methods and software-based methods will be tested. Then, a hybrid model will be developed regarding the detection performance (accuracy, time) and cost. The proposed model will be tested in different scenarios based on the real environment and the most practical operating parameters will be finalized. The study is expected to improve the performance of oil leak detection in the pipeline system in midstream.
Novel Approach to Evaluating Pitting Corrosion
Title:
A Novel Approach for Evaluating Pitting Corrosion
Investigator:
Dr. Chun-wei Yao
Description:
Corrosion could degrade the mechanical properties of equipment, lowering materials’ performances. Corrosion damage could lead to rupture and raise safety issues. The localized corrosion phenomena involving pitting corrosion, have been a hot topic for advanced corrosion studies. However, the prediction of localized corrosion is a problem for various reasons. Out of all limiting factors, the most significant one being the fact that all the reactions and other thermodynamic events occur at a microscopic scale, and the passive film is generally of nanometer thickness, and the location of initiation is even smaller. Therefore, there is a need to study the process of localized corrosion occurring on coatings at a micrometer/nanometer resolution. This project will study the mechanism that governs pit initiation and propagation on coatings. Furthermore, the PI will evaluate the mechanical performance of coatings with pit growth.
Sensor Development for Water Wash Unit in NGL Fractionation Trains
Title:
Sensor Development for Water Wash Unit of an NGL Fractionation System
Investigator:
The water wash unit is an important front-end processing unit in ordinary NGL (natural gas liquids) fractionation systems to remove excessive methanol and other contaminants from the feedstock. If not well operated, however, the outlet of the water wash unit would carry too much free water with containments to the downstream process, resulting in significant economic losses and product quality issues. The current operating problem of the water wash unit in many NGL fractionation systems is that the process effluent water concentration from the unit cannot be accurately and timely measured by physical sensors, so that effective monitoring and control strategies cannot be established to appropriately operate the unit. In this project, the feasibility of deploying a soft sensor to improve the operating performance of the water wash unit in a representative NGL fractionation system will be studied based on the provided plant process, operational, and lab analysis data. If feasible, a soft-senor algorithm will also be developed and tested to help online monitoring of the water effluent concentration from the water wash unit; otherwise, a research plan to improve the current plant data acquisition system will be provided. In this project, the plant data will be collected and utilized to support the soft-sensor feasibility study and development.
Microwave-Assisted Strategy
Title:
Development of a Microwave-Assisted, Four-Phase Crude Oil Demulsification Strategy
Investigator:
Dr. Clayton Jeffryes
Description:
Water-in-oil and oil-in-water emulsions are stable blends of water and oil frequently formed by the high shear mixing of process water and product petroleum during operations such as extraction, pumping or desalting of crude oils. Breaking emulsions into their separate water and oil phases is costly, but necessary. This demulsification process is typically achieved using thermal energy and expensive chemical demulsifiers. However, the PI’s lab has made significant progress toward developing a chemical-free, microwave demulsification process with economic promise. This process can demulsify most crude:water emulsions, but further increasing the demulsification rate would be economically beneficial. The use of hydrophilic and hydrophobic materials integrated into the demulsification unit has shown promise to enhance phase separation. Gas flotation is another tool to separate oil:water emulsions. However, these tools have never been used in combination with a microwave-enhanced separation. This work proposes to develop a separation method using hydrophilic and hydrophobic materials to capture the oil and water and gas flotation to facilitate the separation free oil from the emulsion. This constitutes a four phase (solid, gas, oil, water) separation scheme to enable a chemical-free crude oil demulsification. This process is novel, and method validation would have high commercial value.
Integrated Allam Cycle-LNG Complex
Title:
Integrated Allam Cycle-LNG Complex for Greenhouse Gas Reduction and Efficient Energy Supply
Investigator:
Dr. Daniel Chen
Description:
Liquified natural gas (LNG) plants use natural gas (NG) powered compressors/pumps/engines to run the refrigeration train, which is a source of air pollutants such as methane, VOCs, COx, nitrogen dioxides (NOx), and PM (soot). In oil and gas fields, compressors and generators are also often powered by NG and most of the heavy-duty equipment and trucks use diesel fuels that emit even more soot, COx, and NOx. LNG and electricity would be suitable alternatives to power the drilling, treatment, and transportation in the upstream and midstream operations in terms of reducing noise, exhaust, methane, and CO2 emissions. NET Power has developed the oxyfuel, carbon- neutral, high-efficiency super-critical CO2 Allam cycle that generates pipeline-grade CO2 for utilization/storage. The proposed work seeks to perform process modeling, economic analysis, and environmental impact study of an integrated Allam cycle and LNG complex by comparing the use of carbon-neutral electricity and LNG with the baseline purchasing electrical power from grids, running equipment by natural gas, and running heavy-duty trucks/machines with diesel. Sensitivity analysis with gas/electricity prices, LNG/electricity delivery costs, and CO2 transport/storage costs, and CO2 45Q tax credit will be performed. Annual methane and CO2 reductions and levelized cost of electricity will be estimated.
Digital Twin Development for Real-Time Price Driven Optimization of an NGL Fractionation Train
Title:
Digital Twin Development for Real-Time Price Driven Optimization and Control of an NGL Fractionation Train
Investigator:
Dr. Sujing Wang
Description:
The NGL (natural gas liquids) fractionation train is a critical midstream facility that produces a range of important products from natural gas to support many downstream processes as critical feedstock or energy sources. Due to various uncertainty impacts, prices of raw gas and different NGL products will inevitably fluctuate, which provides a grand challenge and also a good opportunity for an NGL fractionation train to timely adjust its operating conditions and optimize its product portfolio. In this project, a robust and dynamic digital twin will be developed for real-time optimization and control of a typical NGL fractionation train. The study starts with building a complete and dynamic fractionation train model, which will be tested and fine-tuned by the reconciliated plant data. Next, process optimization will be conducted based on the current prices of raw gas and NGL products. The objective is to maximize the gross profit of the studied NGL fractionation train so as to identify the optimized product portfolio and associated operating conditions. After that, the developed digital twin will be applied to the plant distributed control system (DCS) to support offline or online control decisions in a real-time manner.
Mitigation of Water Hammer in LNG Pipeline
Title:
Holistic Approach in Mitigation of Water Hammer in LNG Pipeline
Investigator:
Dr. Xinyu Liu
Description:
As an undesired hydraulic phenomenon in pipe system, water hammer is usually caused by a valve closing and opening, a pump stopping and starting, or condensation in the piping system. Various water hammer events commonly occur in LNG piping system, nuclear power plant, and hydro-power systems. The high surge pressure and forces may damage the pipeline, its supports and equipment such as pumps and sensors, hence considered a serious concern. The loading arm of an LNG plant is a critical equipment that is susceptible to hydraulic hammer. In addition, under some operating conditions, condensation- induced water hammer can occur. The objective of this project is to gain a deeper understanding of the physical mechanism of the water hammer phenomenon and to develop a suite of mathematical models to estimate the overpressure, loads, stresses, and the associated structural vibrations. The potential damagescan then be assessed in terms of pipe rupture, bending/displacement of pipe support, cavitation damage to the pump blade. Furthermore, different pressure stabilization equipment and damping facility will beinvestigated for mitigating the water hammer effects.