Title:
Real-Time Defect Detection for Autonomous Welding using Machine Learning
Investigator:
Dr. Hassan Zargarzadeh
Description:
Autonomous welding (AW) is widely used in industries such as oil and gas, automotive, and shipbuilding and is employed in building and jointing large-scale structures. While defective or imperfect welds rarely occur in the process, even a minuscule defective weld is the reason for scrapping the entire structure, or restarting the process which leads to financial loss, and waste of time. Preventive machine learning-based solutions can be leveraged to minimize the loss. Because of the nature of the process, such approaches have the potential of detecting defective welds and by monitoring the autonomous welder operating states. This project proposes developing an online defect detection AI-based scheme for AW. Existing models are black box and not interpretable. To provide explainability for classification models, explainable classifiers will be developed to provide a measure for the model decision-making performance. Further, Bayesian optimization methods will be utilized to tune the models’ parameter for performance maximization. While this is an emerging and new field, the PI has been one of the first contributors to it [1][2]. Stanley Oil Gas and Stanley Black & Decker are the mentors and potential investors in this endeavor, soon after the preliminary results of this project are produced.
Funding Source:
Internal Grant
Title:
Optimization for Natural Gas Liquefaction with Helium Recovery for LNG Industry
Investigator:
Dr. Qiang Xu
Description:
The worldwide market of liquefied natural gas (LNG) will have significant increase in the coming decade, which brings tremendous opportunities to U.S. LNG producers. Meanwhile, the natural gas reserve is the main resource of helium, an inert and precious component that has been critically and irreplaceably used throughout the science, medicine, aerospace, and electronics industries (e.g., optimal fibers and semiconductors). Helium has promising global market demand and growth, which makes it more profitable to recover from natural gas feed to LNG liquefaction plants via integrated processes operations such as cryogenic separation, refrigeration, and flash. In this project, process dynamic simulation and optimization will be performed to study the feasibility, controllability, and profitability of the helium recovery together with the LNG liquefaction process. Sensitivity analysis of the helium recovery performance based on varying operating conditions such as feed composition, flow rate, and natural gas liquefaction temperature will also be thoroughly examined. The optimum operating conditions will be provided for the minimum energy consumption and maximum recovery of grade A helium. The study may provide significant benefits in both LNG and helium productions in the U.S., where the currently abandoned treasure of helium in natural gas reserves will be monetized.
Funding Source:
Internal Grant
Title:
Intelligent/Adaptive Performance and Reliability Assessment Tools for End Users of Turbine- Compressor Trains
Investigator:
Dr. Xinyu Liu
Description:
Turbomachinery, especially in form of turbine-compressor trains, are the most critical and cost intensive assets maintained by LNG production, transportation, midstream, refining and power generation facilities. Unexpected failures in these systems carry significant expense and ramifications, such as lost opportunity costs due to downtime. The objective of this project is to develop models and tools that could assess the performance and component health condition of the turbine-compressor train to facilitate the adaptation of condition-based maintenance (CBM) for end users. By focusing our research on gas turbine- compressor trains, Lamar has chance to specialize our deliverables and expertise to fill the void of reliability services, products, and training tailored to the rapidly growing niche market of local LNG exporters. Golden Pass LNG has expressed interest in long term collaboration in this project. We will solicit additional cooperation from the growing number of local industry operations using gas turbine compressors. increasing variety of sample systems, operating data, as well as corresponding OEM curves for a more generalized and accurate model.
Funding Source:
Internal Grant
Title:
Development of a Rapid, Inexpensive, Field Measurement to Reduce Process Stream Demulsification Costs
Investigator:
Dr. Clayton Jeffryes
Description:
Emulsions are stable oil-water blends, frequently with dissolved gasses, minerals, metals, and particulates. These emulsions are formed during various crude oil operations, such as CO2- facilitated crude oil extraction, hydraulic fracturing, and desalting at refineries. Separation of the crude oil is expensive and requires high levels of energy and chemical demulsifiers. The cost of chemical demulsifiers in crude processing is significant, so methods to inexpensively optimize their use is of commercial interest. Currently, the industry-standard method to determine demulsification loading is the “Bottle Test,” which mixes the emulsion with varying quantities of demulsifier in a bottle and observes the separation under gravity. This method works poorly if the emulsion is difficult to break or if the crude oil phase is too heavy. Also, this method is often performed by certified 3rd party contract labs or vendors, which increases expense and information turnaround time. This project builds upon previous experience using microwaves to develop a rapid, inexpensive, microwave-facilitated, benchtop measurements to determine minimum demulsifier costs to produce on-specification crude oil from crude oil emulsions. The nature of the microwave method enables on-site lab and field measurements, as well as application to crude emulsions that are not amenable to the Bottle Test.
Funding Source:
Internal Grant
Title:
Detection of Methane Leaks in Soils – Phase II
Investigator:
Dr. Phil Cole
Description:
Quantitative Optical Gas Imaging of Methane Leaks using Drone-Mounted Infrared Camera Systems and Phase I of the CMMS award 2021/2022. We are seeking funding for a continuing grant. 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 providing affordable compliance. In 2003 the Texas Commission on Environmental Quality ordered 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 four focus areas: Greenhouse Gas Management, Corrosion Detection, Midstream Optimization, and Management for Midstream Infrastructure Resilience.
Funding Source:
Internal Grant
Title:
Evaluating Various LNG Plant Electrification Options
Investigator:
Dr. Daniel Chen
Description:
Liquified natural gas (LNG) plants use natural gas (NG) powered compressors to run the refrigeration train, which emits methane, CO2, nitrous oxide (N2O), nitrogen oxides (NOx), and PM (soot). LNG companies plan to cut greenhouse gas (GHG) emissions and electrification is on top of the list for achieving decarbonization. This study will evaluate four options: 1) No electrification but implementing Directed Inspection and Maintenance (DI&M) program to reduce methane emissions (base case); 2) Electrification with purchased power from grids; 3) Electrification with natural gas combined cycle (NGCC); 4) Integrating the LNG plant with the Allam cycle and Air Separation Unit (ASU). Carbon intensity of electricity should be less than 600 MMT/GWh to be beneficial. NET Power has developed the oxyfuel, carbon-neutral, high-efficiency super-critical CO2 Allam cycle that generates pipeline-grade CO2 for utilization/storage in addition to water. Environmental performances will be compared in terms of GHG/ NOx emissions and water recovery. Economic performances will be evaluated in terms of capital investment, operating costs, payback period, levelized cost of LNG with 45Q tax credit. The proposed work is also built upon our previous results of integrating hot and cold energy among LNG, Allam cycle, and ASU to improve overall energy efficiency.
Funding Source:
Internal Grant
Title:
Design and Development of an IT/OT Emerged Experiment Platform and Testbed for Energy Infrastructure Cybersecurity Enhancement
Investigator:
Dr. Tianxing Cai
Description:
Industrial processes are vulnerable to cyber-attacks. Modern Process Control Systems (PCS) has brought significant economic and operational benefits, but it also shifted the architecture of PCS from a completely isolated environment to an open, “system of systems” integration with traditional ICT systems, susceptible to cyber-attack. In this study, the team will design and develop an IT/OT-emerged Experimentation Platform embedded with ICS to characterize and simulate the critical assets of the midstream infrastructure for industrial cybersecurity enhancement. Various scenarios of cyber-attacks will be simulated, and the investigators will examine how these events affect multiple aspects of asset management. A vulnerability assessment of the industrial system will be conducted, and the results will be used to develop a list of priority action plans for industrial cybersecurity enhancement.
Funding Source:
Internal Grant
Title:
Corrosion Prevention at Pipe Supports
Investigator:
Dr. Robert Kelley Bradley
Description:
Corrosion is accelerated at pipe supports due to entrapment of moisture and unintended metal-to-metal contact. Each year there are serious safety and economic impacts due to pipe support corrosion in gas transport and distribution lines. Though solutions have been developed to help mitigate pipe support corrosion, additional research is needed to understand and improve the current technology. The proposed work focuses on pipe support pads designed to minimize moisture entrapment via use of a “road-bump structure to minimize contact area with the pipe. To prevent galvanic and stray current corrosion the pads must be made from durable, electrically insulating material, namely plastic, but the small contact area with the pipe results in large amounts of stress. Most inexpensive plastic will creep and deform over time resulting in a saddle shape that traps moisture and/or fractures. Procedures for developing new materials, pad designs, and analytical methods will be developed through the proposed work. Funding will help establish a niche research area at Lamar that will be of importance to the midstream industry.
Funding Source:
Internal Grant
Title:
Incipient Leakage Detection through embedded sensors and AI on Drones
Investigator:
Dr. Reza Barzegaran
Description:
Magnetic Imaging plays an important role in many applications. Two of the most well- known applications are Magnetic Resonance Imaging (MRI) for medical diagnosis and nonintrusive evaluation commonly employed in inspecting cracks in metallic specimens. Monitoring and maintenance of complex and tiny components in electro-thermal system are problematic, especially when they are in extreme and outreach environment such as on outreach pipes and inaccessible chemical and power substations. There is a necessary need for a method to detect incipient leakage of these components to avoid massive failure and accordingly outage and extremely expensive and time-consuming maintenance.The nonintrusive condition monitoring using the drone system empowered with required sensors is proposed in this project. We will build state-of-the-art multidisciplinary sensing system embedded in the drone that will transfer the necessary data from components under monitoring to the supervisory system. This communication will be wireless and compatible with the communication infrastructure/protocols of the cyber physical system. The data received in the processing unit will go through visualization and comparison process with normal cases to detect abnormal situations. The proposed system detects electrical failures and leakage in pumps which leads to enhancing the reliability and resiliency of the systems in midstream sector.
Funding Source:
Internal Grant
Title:
Subsidence Implications on Pipeline Infrastructure in Southeast Texas
Investigator:
Dr. Reda Amer
Description:
Land subsidence poses a hazard to pipelines. This project intends to use satellite Synthetic Aperture Radar and airborne LiDAR datasets to identify subsidence hotspots in southeast Texas. Sentinel-1 SAR data from 2016 to 2022 will be processed using Persistent Scatterer Differential Interferometric Synthetic Radar (PSI) techniques to identify and quantify subsidence rates. The PSI's accuracy will be determined by comparing the results to data from the Continuously operating reference GPS. Two sets of LiDAR data, captured in 2006 and 2017, respectively, will be used to study the elevation change in Southeast Texas. ArcGIS will be used to create thematic layers of PSI, Digital Surface Model, and Digital Elevation Model to map the spatial distribution of subsidence hotspots and quantify the radius, slope, and vertical displacement of subsidence features. The calculated deformation parameters will be used to calculate the axial strain at each subsidence feature in order to determine which strain may cause pipeline failure. The geological, soil, and groundwater level data will be combined with the results of PSI and LiDAR elevation data to assess the geologic, soil, and groundwater level variability attributes associated with the various types of subsidence. GIS models will be created to identify current and future pipeline hazards caused by subsidence.
Funding Source:
Internal Grant
Title:
Dynamic Modeling and Simulation for LNG Loading and BOG Generation/Recovery at Export Terminals
Investigator:
Description:
Liquefied natural gas (LNG) is a prominent clean energy source available in abundance. LNG has high calorific value, while lower price and emissions. Vapors generated from LNG due to heat leak and operating-condition-changes are called boil-off gas (BOG). Because of the very dynamic in nature, the rate of BOG generation during LNG loading (jetty BOG, or JBOG) changes significantly with the loading time, which needs to be well studied. In this project, the LNG vessel loading process is dynamically simulated to obtain JBOG generation profiles. The effects of various parameters including holding-mode heat leak, initial-temperature of LNG ship-tank, JBOG compressor capacity, and maximum cooling-rate for ship-tank, on JBOG profile have been comprehensively studied. Meanwhile, possible JBOG reuse/recovery strategies have also been investigated in this work. Understanding JBOG generation would help in designing and retrofitting BOG recovery facilities in an efficient way. The study would also help proper handling of BOG problems in terms of minimizing flaring at LNG exporting terminals, and thus reducing waste, saving energy, and protecting surrounding environments.
Title:
H2S Removal from Oil and Gas Streams
Researchers:
Description:
The aim of this research is to develop and test a series of absorbents (known as scavengers) for the removal of hydrogen sulfide (H2S) from crude liquid oils. Crude oils that have sulfur concentrations more than 0.5 wt% are considered sour crudes, since they are characterized by a foul, odorous smell. Sour crudes are of lower quality and present serious health and environmental concerns. Therefore, sustainable measures to lower the sulfur content (i.e. crude oil sweetening) are of significant importance, financially and environmentally. Hydrogen sulfide (H2S), however, is normally removed using amine based absorbing materials, known as scavengers. Removing of H2S at the wellhead before transporting via pipeline or railcar increases the value of crude oil and in some cases is necessary to conform to legal transport laws. Phase 1 of this work explored the solubility and liquid phase activity coefficients for triazine-type scavengers. Phase 2 explores replacing trianzine compounds with ionic liquids. Triazine have a tendency to produce volatile organic compounds (VOCs) during the regeneration phase in the absorption/stripping process. Ionic liquids have relatively no boiling point, making them more environmentally attractive as an absorbent. Laboratory-based experiments will yield solubility parameters needed for equipment design and sizing.
Title:
Carbon Dioxide Transportation and Storage project (GoMCarb)
Researchers:
University of Texas
Description:
This is a collaboration with UT-Austin (lead institute). Through a $16.5 million grant from the Department of Entergy, the project - Offshore Gulf of Mexico Partnership for Carbon Storage - Resources and Technology Development (GoMCarb) – brings the two universities together with other carbon capture storage (CCS) stakeholders in government, academic and industry.
91ÖÆƬ³§ areas are:
Title:
Deep Learning-based Anomaly Detection for Midstream Infrastructures-Phase II
Researcher:
Description:
In Phase I of this project, Well Checked Systems International collaborated with Johns Hopkins University and a large well-known gas producer to develop deep learning-based auditory predictive analysis for compressor anomaly detection. The analytics used a matrix of microphone data to predict the failure and alert to preventively maintain the midstream components such as compressors before failure. In Phase II of the project to be performed by 91ÖÆƬ³§, the current network performance will be improved by producing a long-term classification network that has an integrated feedback loop to collect, classify, and retain anomalous audio data. This supervised feedback loop would workflow on the top of the anomaly detection method developed in Phase I. Meanwhile, incremental learning technique will be applied to enable the network to learn new knowledge from the audio data collected from new mechanical components without retraining the whole network and to keep old knowledge learned from existing components. Furthermore, a multi-stream deep learning neural network will be investigated to fuse audio data and other potential input signals, such as vibration and thermal image data, for a comprehensive multi-source-based device anomaly classification. Currently, the research team has signed NDA agreement and has received the audio data from Well Checked Systems International and is ready to start the second phase.
Funding Source:
Internal Grant
Title:
Study of Natural Gas Movement in Soil from an underground Pipeline Leak in Southeast Texas
Researcher:
Description:
The United States has about 3 million miles of natural gas (on-shore and off-shore) pipelines, which delivers about 28 trillion ft3 of natural gas per year [1]. While it is working at 200-1500 psi, the natural gas leak is difficult to prevent or detect, especially when a local leak is small and slow. The leaked natural gas causes financial loss, increases the global greenhouse effect, and creates potential dangers of fire or explosion. During 2010-2017, the natural gas pipeline incidents took close to 100 lives, injured about 500 people, and costed 1.1 billion dollars over the States [2]. The PIs propose to investigate the transport process of leaked natural gas from the underground pipeline in the porous medium of soil using computational fluid dynamics simulations and miniature-sized testbed experiments. The PIs plan to collaborate with the CTO, Gary Forister from the Infrared Cameras Inc. (ICI) on the analysis of research results and comparisons with the actual field data measured by ICI engineers [3]. The goal of this proposed project is to understand the effects of underground leak and soil conditions on the natural gas distribution on the ground level, which will help diagnose various leak situations based on the ground level detection using drone technology.
Funding Source:
Internal Grant
Title:
Biological and Physio-Chemical Treatment of Produced Water
Researcher:
Description:
The aim of the research is to develop and optimize a biological and physiochemical system to effectively treat the produced water from the midstream industry. The produced water is laden with chemical contaminants, such as high levels of salinity, dissolved solids, heavy metals, dissolved hydrocarbons, and radioactive materials. Current treatment options for produced water ranges from reinjection to the disposal wells to expensive processes such as membrane systems and chemical precipitation. These current treatment options are often viewed as highly costly and un-sustainable with ever-changing environmental regulations and guidelines. Therefore, sustainable green solutions for the produced water treatment are of significant importance, economically and environmentally. This research explores a combination of a biological and physiochemical system to effectively treat the produced water while improving the overall sustainability of the process through the generation of waste-to-value product streams. This research explores the viability of an-algal based photobioreactor system to bioremediate produced water with subsequent production of Exopolysaccharide and livestock feed supplements from the grown algal biomass. Also, this research proposal will evaluate the applicability of untreated and treaded produced water in concrete applications. Overall, this research seeks to provide alternative sustainable solutions to the midstream industry to treat the produced water.
Funding Source:
Internal Grant
Title:
Associated Gas Recovery Integrated with Solar Power for Produced Water Treatment
Researcher:
Dr. Daniel Chen
Description:
Flaring associated gas wastes valuable raw materials/energy while emitting air pollutants which intensify climate changes. Therefore, flare gas recovery (FGR) is high on the agenda of regulatory agencies and the oil & gas (O&G) industry. Currently, disposing untreated produced water by subsurface injection or evaporation are under scrutiny due to the shortage of available sites and scarcity of fresh water for fracking. The call for produced water treatment for recycle and reuse is getting louder. In our previous work, the ejector-based FGR-thermal vapor compression (EFGR-TVC) process has been shown to be technically viable and cost-effective. The rapid advancement of solar energy technology coupled with a high solar insolation in US southwest makes a compelling argument to utilize the solar energy in O&G operations. In this project, a novel solar-ejector-based FGR-TVC process is proposed for the on-site integration of the sunlight harvesting, FGR, and desalination processes. Specifically, the FGR is integrated with a photovoltaic panel/ solar collector/ storage assembly to desalinate pretreated produced water with a EFGR-TVC process. A beam splitter is employed to split sunlight for photovoltaic cells and solar preheater. The process design/modeling, economic evaluation, carbon/water footprint analyses will be performed and compared to a base-line disposal operation.
Funding Source:
Internal Grant
Title:
Dynamic Scheduling Optimization in Front-End Refinery Supply Chain Management
Researcher:
Dr. Qiang Xu
Description:
Petroleum refinery plays a vital role in our modern society. The scope of a front-end refinery supply chain (FERSC) includes vessel berths, port-side storage tanks, long-distance pipelines, charging tanks, refinery units, and oil-product distribution system. In reality, the performance of all FERSC facilities will inevitably decay over time. Thus, maintenance tasks for all facilities must be optimally planned and well-integrated with ordinary production schedules. In this study, a new methodology along with new optimization models will be developed for the scheduling of FERSC. It will simultaneously and dynamically determine the optimal schedule of ordinary FERSC productions and facility maintenance operations. Specifically, both proactive scheduling for multi-task preventive maintenance (PM) and reactive scheduling for emergent corrective maintenance (CM) under uncertainties will be systematically integrated. It will optimize production schedules by minimizing the total operating cost of the FERSC system. Meanwhile, it will also improve maintenance performances by selecting the best time for facilities to start their maintenance, as well as the best time for facilities to rejoin the system and resume their services after maintenance. The project will enrich the knowledge, methodology, and solutions for CMMS priority research fields on integration of O&G supply chain, PM/CM studies, and critical infrastructure resilience.
Funding Source:
Internal Grant
Title:
Detection of Methane Leaks in Soils
Researcher:
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. In2003theTexasCommissiononEnvironmentalQualityordered 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.
Funding Source:
Internal Grant
Title:
Numerical and Electrochemical Analyses of Pipeline Corrosion in Midstream Industry
Researcher:
Dr. Sidney Lin
Description:
The corrosion cost of the US oil and gas industry $1.4 billion in 2018. The pipeline corrosion is a not only a safety risk but also an environmental concern. Even though ultrasonic sensors and smart pigging technologies are available for corrosion measurement, these reactive approaches are always too late. Rigorous first principles (electrochemistry, reaction kinetics, thermodynamics, mechanics, etc.) based models can be used to predict corrosion accurately. Mathematical models will be developed in this project to predict pipeline corrosion. OLI Studio: Corrosion Analyzer, ProMax, and COMSOL MultiPhysics equipped with corrosion module will be used to calculate the corrosion rate when different crude oils flow through the pipeline. Electrochemical Impedance Spectroscopy (EIS) analysis instrument will be setup to evaluate corrosion resistance of each corrosion factor and a corrosion network will be proposed based on the simulation and measurement results. 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. The research period is from September 2021 to August 2022 and the total requested funding for this proposal is $50,000.
Funding Source:
Internal Grant
Title:
Corrosion and Friction Resistance of Advanced Polymer Coatings for Oil and Gas Pipelines
Researcher:
Dr. Zhe Fan
Description:
Corrosion of oil and gas pipelines results in significant economic costs and substantial damages in midstream industry. Two effective methods to alleviate corrosion are cathodic protection and coatings. Metallic, ceramic, and polymer coatings have been developed to protect pipelines made of cast iron and steels which may not be corrosion resistant. High performance coatings can serve as physical barriers between pipeline materials and corrosive environments. Recently, one type of advanced polymer coating, aromatic thermosetting polyester (ATSP), has been developed by ATSP Innovations. This coating has demonstrated outstanding performance under high stress and temperature. In this project, through collaboration with the research scientist at ATSP, we aim to i) evaluate the corrosion protection efficiency of ATSP coated commercial pipeline materials, ii) probe the bonding strength between the ATSP coatings and metals, and iii) explore the potential to utilize this coating in industry and other related research. We will compare the corrosion resistance of ATSP coatings with commercially used coatings, and optimize the corrosion and wear resistance of ATSP coating through processing and microstructure modification. This project will reveal the fundamental science behind the corrosion and wear response of the advanced ATSP coatings on pipeline materials and acceleration their practical applications.
Funding Source:
Internal Grant
Title:
Advanced Analysis of Risk Management Practices in Oil and Gas Industry
Researcher:
Dr. Gevorg Sargsyan
Description:
Upstream - midstream - downstream chain is a driver for local economies of US Southeast Texas region, UAE, Saudi Arabia, and Russia. A research grant funded by U.S. EDA discovered that efficient risk management in this sector is key to local economic growth, especially in light of the Colonial pipeline event, recent oil price sharp decline, environmental pressures, and risks. These risks highlight the need to support local economic growth by improving risk management strategies or by reusing the best practices of the past or other countries. Researchers will analyze risk management practices of upstream - midstream - downstream chain of the oil and gas industry in three countries. PARM methodology, previously developed by researchers, will help identify the best risk management practices of the upstream – midstream - downstream chainin target markets. It will also provide an opportunity to diagnose the challenges and past experiences of local stakeholders in this sector in UAE, Saudi Arabia, and Russia, and contrast with the results of the CMMS 2021 summer grant focused on SETX. This comprehensive analysis tool will help to analyze how companies in this industry do risk identification, assessment, response, and monitoring, to reach reliable conclusions and recommendations. The project goals are identified in the narrative.
Funding Source:
Internal Grant