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THE EQUITY TECHNOLOGY GROUP INCORPORATED

Address

20600 CHAGRIN BLVD STE 1200
SHAKER HEIGHTS, OH, 44122-5342
USA

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UEI: GVDMSBA9TS56

Number of Employees: 173

HUBZone Owned: No

Woman Owned: No

Socially and Economically Disadvantaged: No

SBIR/STTR Involvement

Year of first award: 2019

Phase I Awards

Phase II Awards

40%

Conversion Rate

$1,121,091

Phase I Dollars

$2,600,571

Phase II Dollars

$3,721,662

Total Awarded

Awards

Up to 10 of the most recent awards are being displayed. To view all of this company's awards, visit the Award Data search page.

SimPulse: Scalable Hydraulic Transients in 21st Century Piping Systems

Amount: $198,134 Topic: C55-01b

C55-01b-270667-AbstractThe world has started to transition to using hybrid-energy systems. As this transition progresses, many companies have started to re-purpose existing, aging assets. This re-purposing of aging assets can be prominently seen in the areas of biofuels processing and in hydrogen and carbon dioxide transmission pipelines. A core issue with repurposing aging assets lies in the often-overlooked complex piping systems and offsite piping networks that serve as the vascular network of any industrial processing operation. A leading driver of change in these industries is real world incidents that lead to the creation of new regulations governing the safe operation and management of these complex piping networks. Often, the industry’s software tools, management plans, and best practices lag any new regulations that are introduced. Common events, such as pump failures, in these complex networks can cause pressure pulsations or shocks that can catastrophically damage the downstream piping systems, leading to failure and environmental disaster. With the increasing complexity of modern-day piping systems and the multimodal demands that these systems face, improved simulation methods for predicting transient damage have never been more of a necessity in this ever-changing energy arena. To help Process Engineers accurately simulate upset conditions in complex piping networks, we will develop a scalable hydraulic transient software suite: SimPulse. The novelties of SimPulse are twofold: 1) a scalable backend that can manage a diverse range of event-driven transient phenomena in complex networks, and 2) a pipe-engineer tailored interface via our Standard Piping Language (SPL) input format that has been highly successful in our other commercial pipe-stress engineering products. The key challenge of modeling a piping network is the complex interconnection of pipes, junctions, valves, and fittings, coupled with the various event-driven phenomena that can occur, e.g., valve and pump failures, rapid phase transitions, and bursts. All these complexities require any simulation engine to be able to manage the computational scalability in both spatial and temporal contexts. Accurate simulation of hydraulic events is critical to the understanding of potential damage and the risk of rupture events causing widespread environmental damage. By connecting the hydraulic picture with the atmospheric plumes of leaks and bursts, this project will give a more accurate description of the overall risk landscape. The integration of high-performance computing libraries will allow scalability in this application domain not previously possible. At its core, SimPulse will solve hydraulic transients in complex piping networks, both in the normal operating state as well as after the occurrence of transient events. It will then relate this transient picture to potential damage via stress analysis, and risk mitigation. SimPulse will ensure that piping systems are safely code complaint, regardless of their complexity.

SBIR

Phase I

2023

DOE

BENGI: A Bayesian ENGine for Insights

Amount: $1,501,178 Topic: C51-02a

In the aging energy industries, pressurized fixed equipment and piping systems are exposed to harsh environments and operating conditions that promote a whirlwind of undesirable damage mechanisms. This damage may eventually result in catastrophic failure (leak or rupture) if left unchecked, with potentially significant consequences, especially if there is an environmental release of toxic or flammable fluids. Failure can also lead to loss of production, damage to nearby equipment, personnel injury, loss of life, and repair, replacement, and legal costs. To minimize the likelihood of unexpected failures, proactive inspection and maintenance plans are required. These plans require complex decision-making platforms to assist in integrating data and experience that ultimately end up in a series of decisions. From the foundational work accomplished in Phase I, we have developed an explainable AI engine for making such large-scale complex decisions. The core technology was made possible by leveraging DOE funded tensor engine libraries. This engine, called Bayesian ENGine for Insights (Bengi), is based on probabilistic cause-and-effect diagrams known as Bayesian Decision Networks. By using these networks, decisions under uncertainty are optimized in even the most complex scenarios to minimize cost, maximize profitability, and increase safety. Bayesian Networks properly blend data from various sources, such as human knowledge, mathematical models, numerical simulations, and field observations. In Phase I of this project: (1) We explored more advanced tensor contraction algorithms and data structures. In the end, we were able to integrate DOE HPC libraries into Bengi’s core tensor engine. Bengi now supports multiple backends.; (2) We built networks efficiently with a CPT library and thematic network generator. Now we can quickly generate networks on-the-fly without the use of a user-interface for systems level networks.; (3) Implemented the graph theoretic infrastructure to compute complex decision hierarchies. These algorithms were then used to implement the optimal decisions algorithm leveraging tensor solvers.; and (4) Incorporated these innovations into a user-interface, with a coherent user-experience in mind, to enable a wide array of users to have access to DOE-funded HPC software and resources. In Phase II of this project, the goal is to combine the modules that were developed in Phase I and extend, refine, verify, and commercialize those efforts as part of a complete decision-making solution for the next level of Life Cycle Management. To circumvent the limitations of traditional of Life Cycle Management and address the industry need for a financial-based automated decision support tool, we propose leveraging Bengi to develop Risk-Based Inspection Plus, a probabilistic cost-benefit analysis extension to traditional risk-based inspection that recommends optimal lifecycle decision strategies. During Phase II we will develop a market-ready the Risk-Based Inspection Plus software into a solution centered around facility-wide, financial-based inspection optimization with integrated fitness-for- service capabilities. The project is the fusion of tensor based HPC libraries and asset integrity management

SBIR

Phase II

2022

DOE

PipeSight: A High Performance Computing Platform for Pipeline Integrity Management

Amount: $249,871 Topic: 04a

In recent years, there have been a number of pipeline failures that have made national headlines. One such failure occurred in San Bruno, California in 2010 and resulted in 8 lives lost, 51 people injured and 38 homes destroyed. The catastrophic rupture of the pipeline created a 72 ft by 26 ft crater, and a 28 ft long, 3,000 lb piece of pipe was thrown 100 feet. The National Transportation Safety Board (NTSB) issued an incident report that determined the probable cause of the failure was the owner’s inadequate integrity management program. As of 2018, there were 301,227 miles of gas transmission pipelines in the United States, 20,435 miles of which are designated as being in high consequence areas. According to the Pipeline and Hazardous Materials Safety Administration (PHMSA), 56% of pipelines in the US were installed prior to the Minimum Federal Safety Standards being finalized in 1970. Pipelines constructed prior to 1970 were awarded grandfather status and were deemed exempt from the tighter controls placed on more modern pipelines. The pipeline that failed in San Bruno was one of these grandfathered pipelines. A crucial component of any effective integrity management program is the inspection and assessment of threats to the pipeline’s integrity. A typical pipeline inspection involves the use of an in-line inspection device that travels along inside the pipeline and takes a high resolution (millimeter-scale) ultrasonic scan of the pipe wall. This high resolution scan produces terabytes of data that is then interrogated to determine critical regions (threats) where damage may be present. These threats often number in the thousands. Each threat then undergoes an engineering critical assessment (ECA), which involves the numerical modeling and analysis of the damaged region. The large volume of data, combined with the potential for a large number of detected threats, directly lends itself to high-performance computing (HPC). The R&D group at The Equity Engineering Group will produce a user-friendly, HPC-accelerated platform for assessing the aging pipeline infrastructure in the United States. The platform will be web-based and will leverage high-performance cloud-computing resources. The proposed platform will contain the following five modules: (1) Data Import and Management – Upload, store and retrieve data from ILI inspection devices, along with other sources of data (e.g., streaming data from SCADA systems and field inspection data); (2) Screening Data Analysis – Process data using clustering algorithms combined with low level engineering assessments to identify and rank critical threats. Perform calculations using highly parallelized processes; (3) Advanced Engineering Analysis – Create advanced numerical models of the critical threats identified during the screening analysis and assess them using high performance cloud-computing resources; (4) Predictive Maintenance – Use the results from the advanced engineering analysis to enhance a high- performance Bayesian Decision Network to determine the timing of inspection, repair and replacement activities; and (5) Data and Results Visualization – Deliver the results in the web-based PipeSight platform, optimizing the presentation of the data based on the role of the user. Use principles from the business intelligence community to present analysis results in a hierarchical fashion.

SBIR

Phase I

2021

DOE

BENGI: A Bayesian ENGine for Insights

Amount: $249,558 Topic: 02a

The energy transport and refining sectors are challenged with reliably delivering safer and cleaner energy to US consumers while meeting an ever-growing global demand. The energy infrastructure of US refineries and pipelines is aging, and corrosion and damage mechanisms are constant threats to mechanical integrity and safety. However, governments and industry stakeholders are reluctant to replace or upgrade the existing infrastructure due to the immense cost. To better understand and mitigate the risks of aging infrastructure, strong technical analysis capabilities, combined with the optimization of monitoring and decision making, is critical. Today, this is accomplished via complex simulations and data analysis. However, what is often lacking in this is the decision and optimization support to save money and ensure safe operation. To this end, advanced life cycle management software will be developed that leverages state-of-the-art Bayesian Artificial Intelligence and High- Performance Computing paradigms. This will result in field engineers and plant managers to make more data-driven solutions that also grasp the underlying cause-effect relationships and maintain corporate memory. In Phase I, the main infrastructure for the Bayesian ENGine for Insights (Bengi) will be built out and enhanced with the infusion of Department of Energy High Performance Computing libraries. The project consists of 4 main modules that will lay the foundation for the industrial decision engine. The conditional probabilities libraries that relate probabilities of cause-effect events will be developed as the “Nuts & Bolts” for the engine. Then, a series of Tensor- based algorithms will be implemented with the infusion of HPC libraries. This is the primary challenge: bringing extreme-scale Bayesian Decision Network technology to the heavy industry sectors. Industrial scale Bayesian Networks will enable fast and efficient decision-making processes and allow engineers to maximize their prior knowledge. Finally, a user interface will be developed in parallel to enable end-users of various expertise to have access to the underlying R&D code base. The Bayesian AI technology developed here will be directly applicable to all aging equipment affected by pitting, weld defects, crack-like flaws, environmentally accelerated crack growth and various other damage mechanisms. These damage mechanisms also occur in many non-energy industries as well, and this engineering-based AI framework can help each of them reduce risk and make smarter life-cycle decisions. The direct cost of corrosion to the US is estimated to be approximately 3% of GDP and the indirect cost may well be above 6% of GDP. All industrial operations need to be able to effectively monitor, inspect and manage aging equipment. Further, the demand for petrochemicals and supply stocks in the midwestern United States are rapidly increasing, making well placed to deliver a regional impact. There is a large market for the engineering-based intelligent framework developed in this project to help operators safely extend the lives of their equipment. The commercial impact is not only in the unlocking of the data in heavy industries, but also enabling end-users to make Bayesian AI supported decisions to optimize workflows and drive down costs.

SBIR

Phase I

2021

DOE

Development of CAN^2 (Canister Corrosion Analysis, Assessment, and ActioN PlaNs), a Predictive Detection and Interpretation Software Platform for Life-Cycle Management of Spent Fuel Canisters

Amount: $1,099,393 Topic: 34b

When nuclear reactor performance drops below desired energy levels, the radioactive waste must be properly disposed. Until deep geological disposal sites are identified for very long-term storage of this waste (1000’s of years), interim storage methods must be used for much longer than initially intended (~100 years). With 1000’s of dry storage canisters presently loaded with spent nuclear fuel and more loaded each year, reactor sites and canister vendors must properly address aging effects in their aging management programs to ensure canister longevity. The primary objective of this work is to develop and deliver a web-based machine learning software tool that addresses aging effects for optimizing life-cycle decisions and ensuring long-term performance of welded stainless-steel dry storage canisters for the storage of spent nuclear fuel. Incorporating this tool into daily operations will promote pro-active decision-making and improved risk management to minimize the likelihood of a potentially catastrophic, failure event (i.e. loss of the canister’s containment boundary). The outcome of Phase I was a fully-functioning prototype software tool that successfully demonstrated the feasibility of the approach, including: (i) a web-based user-interface and online database to satisfy the workflow requirements of the end-users, (ii) the artificial intelligence Bayesian decision network engine, and (ii) the chloride-induced stress corrosion cracking model with supporting experimental data. Even though the Phase I prototype software is all encompassing and predictive, assumptions were made in populating the engine, and configuring the user-interface. During Phase II efforts, these assumptions will be relaxed to improve predictability and reduce uncertainty. Efforts will also focus on further anchoring the engine into the fundamentals of the damage mechanisms, as well as refinement, verification, validation, and commercialization of both the engine and the web-based user-interface and online database, to ensure market-readiness.

SBIR

Phase II

2020

DOE

Development of CAN2 (Canister Corrosion Analysis, Assessment and ActioN PlaNs), a Predictive Detection and Interpretation Software Platform for Life-Cycle Management of Spent Fuel Canisters

Amount: $199,856 Topic: 34b

When nuclear reactor performance drops below desired energy levels, the radioactive waste must be properly disposed. Until deep geological disposal sites are identified for very long-term storage of this waste (1000’s of years), interim storage methods must be used for much longer than initially intended (~100 years). With hundreds of dry storage canisters approaching or already exceeding their intended design life, the Department of Energy needs an aging management program to assess the long-term integrity of these canisters. The primary objective of this work is to develop a web-based machine learning software tool for optimizing life-cycle decisions and ensuring long-term performance of welded stainless-steel dry storage canisters for the storage of spent nuclear fuel. Incorporating this tool into daily operations will promote pro-active decision-making and improved risk management to minimize the likelihood of a potentially catastrophic, failure event (loss of the canister’s containment boundary). To ground the outcome of Phase I, and demonstrate the feasibility of the approach, working prototypes for each component of proposed tool will be developed, including: (i) the artificial intelligence Bayesian decision network engine, (ii) the chloride-induced stress corrosion cracking environ-mechanical model with supporting experimental data, and (iii) the web-based delivery interface. The outcome will be a fully functional life-cycle management tool that can make more informed decisions and guide operations without disregarding the need for simplicity and computational efficiency. Further refinement of the computational engine through subsequent phases of this project will further improve the predictive capability of proposed tool. The computational framework developed here, is directly applicable to other failure modes, across all energy industry sectors, making the commercialization of this technology extremely feasible. Not only is there an economic benefit to making better decisions that lower the risk of failure, the public benefits as well.

SBIR

Phase I

2019

DOE

HPC for the EEC: Industrial Trade Tools for the Aging Energy Infrastructure

Amount: $223,672 Topic: 03b

SBIR

Phase I

2019

DOE