Efficiency is the name of the game, and in today’s competitive market it is important to have as much information at your fingertips as possible in order to make the right decisions at the most opportune time. This session will review solutions that will enable operators to make better decisions on the use of algorithms that show the best time to use battery and shave peaking, and how to make microgrids and distributed generation work for you.

• Mr. Mark ScherluebbeSenior ManagerEY
  Robotic Process Automation in Power Gen

  Robotic Process Automation in Power Gen

  ×

  Robotics. That one word can conjure up a number of responses, from enthusiasm and optimism to fear and resistance. While some companies still see robotics solely as a disrupter, others have become a leader in looking for ways to seize its upside and embrace its benefits. Robotic Process Automation is predicted to revolutionize processes across industries, promising savings similar to automation on the shop floor. During this presentation, the audience has an opportunity to learn more about Robotic Process Automation (RPA) and understand what specific processes in the plant are good candidates for automation. The various benefits can include efficiency gains, cost savings, and data quality improvements to name just a few. The audience will learn the basic elements of RPA: What is it, How does it work, What are the benefits and myths about the technology. We will further discuss how to generate value from RPA technologies, which includes selecting suitable processes in a plant environment and present use cases for RPA that have been implemented successfully with utility companies. If time allows, the presentation will cover practical elements on how to get started with RPA.
  ×

  Mark Scherluebbe

  Senior Manager

  EY

  Mark Scherluebbe is a Senior Manager at Ernst & Young, where he specializes in power and utilities strategy related projects. Mark is an energy industry professional with over 20 years of experience in business process streamlining, performance evaluation, robotic process automation and financial modeling. He has over 6 years of direct experience with large power generation equipment, in particular gas and steam turbines. Mark is a Six Sigma Quality Black Belt with hands-on experience in all aspects of quality and performance improvement and a Senior Member of the American Society for Quality. Mark leads the RPA community for EY's Power and Utilities sector, connecting the lessons learned across various projects. He is closely connected to the EY's global RPA efforts and has supported numerous RPA projects.
• Craig TruempiDirector of IIoT ReliabilityATEK Companies
  Equipment Reliability Case Studies using Artificial Intelligence and IoT for Condition Monitoring

  Equipment Reliability Case Studies using Artificial Intelligence and IoT for Condition Monitoring

  ×

  This session will share case histories and lesson learned from deploying Artificial Intelligence and Internet of Things condition monitoring systems in chemical plants. The use of innovative ultrasonic and vibration sensors will be discussed. Pro’s and Con’s of connecting sensors using wireless cellular to a cloud based application will be reviewed. Case studies will include:  Fans, pumps, gearboxes and agitators  Other Case Studies including: o DeathWatch - temporary monitoring of a suspect or impaired rotating asset. o CrashCart –temporary monitoring to diagnose intermittent root issues with rotating equipment Three key takeaways: 1) Basic understanding of the Internet of Things as it applies to condition monitoring 2) Basic understanding of an Artificial Intelligence model used for establishing alerts 3) Awareness of new, cost effective tools for condition monitoring 4) Awareness of ultrasonic monitoring as deployed in continuous monitoring
  ×

  Craig Truempi

  Director of IIoT Reliability

  ATEK Companies

  Craig Truempi, CRL, CMRP is a Certified Reliability Leader and Certified Maintenance and Reliability Professional, board member of the Upper Midwest Chapter of the Vibration Institute, and Director of IIoT Reliability Solutions at Atek Companies. Craig has been involved in a diverse range of artificial intelligence, internet of things and digital transformation projects in the power, refining, mining, petrochemical, manufacturing, and food industries. Craig received his B.S. in Electrical and Electronic Engineering from North Dakota State University and a M.B.A. from the University of Minnesota
• Paul ManchesterOperations & Maintenance Manager-GenerationSilicon Valley Power
  When Silicon Valley Power Turned to Predictive Analytics to Power Up California

  When Silicon Valley Power Turned to Predictive Analytics to Power Up California

  ×

  In September, 2017 the heat wave in California was at its peak. PG&E reported several power outages in San Francisco Bay Area. The State Energy grid was grappling with an unprecedented challenge. If the demand exceeded supply, emergency measures including rolling blackouts would have had to be taken. The state had not seen that in years. At the same time, Silicon Valley Power had recently experienced a catastrophic failure on a generator fan which shut down their generator for six weeks and incurred costly repairs. The monthly vibration analysis program hadn\'t caught the failure, so SVP installed Petasense wireless motes on their balance of plant equipment. Petasense, an Industrial IoT startup, provides permanently installed wireless sensors and software that use advanced machine learning to accurately predict equipment problems. Each machine is assigned a machine health score and provides real-time management alerts when the score falls below an acceptable number. Within a few weeks of installing Petasense motes, SVP received a warning alert on the same generator fan, which was running at alarming limits (vibration levels were higher than the alarm limits). A generator fan cools off the excessive heat and if the generator fan supporting a turbine fails, the gas turbine needs to be shut down, or it runs the risk of collateral and structural damage, breakdown or even a catastrophic failure. In this particular case (besides unplanned downtime and huge repair costs), it would have meant less power supply to the energy grid at a time when the demand was swiftly climbing. Given the criticality of the situation, Petasense had an in-person meeting with the reliability team at SVP. After close analysis and examination of data, SVP decided to shut down the unit preemptively on Petasense’s recommendation to carry out repairs. The plant was up and running within a matter of hours. Bearings were greased and the faulty bearings were replaced. The generator fan was restored and vibration on the motor and fan were well within acceptable limits again, allowing SVP to continue to produce energy to meet peak industrial demand. Silicon Valley Power is always looking for innovative ways to improve their efficiency and deliver safe, reliable power to their customers. By using Petasense’s predictive maintenance technology, SVP is at the forefront of embracing the latest innovations in sensing, wireless and big data analytics. Following the success of the trial, SVP is now in the process of implementing Petasense across their entire plant.
  ×

  Paul Manchester

  Operations & Maintenance Manager-Generation

  Silicon Valley Power

  Paul (Chet) Manchester is an Electric Utility Professional with over 27 years of industry experience. He is currently the Acting Operations and Maintenance Manager for Silicon Valley Power (SVP) Generation Division located in Santa Clara, California. He is responsible for the safe and reliable operation of three gas turbine power plants and other SVP owned power generation assets. In the past he has worked at the Stanford Linear Accelerator Center, Hetch Hetchy Water and Power and General Electric. He is a veteran of the US Navy. A Nebraska native, he now lives in the San Francisco Bay Area with his wife and two dogs.

Digital transition offers data from many sources. This session will help show how to gain value from moving to a more-digital solution throughout your plant and utilizing that data to your benefit.

• Gregory AugspurgerTechnical Manager - M&D Performance CenterDuke Energy
  ×

  Gregory Augspurger

  Technical Manager - M&D Performance Center

  Duke Energy
• Kelly WhittenbergPerformance EngineerBlack & Veatch
  Case Study - Early Detection of Vertical Pit Pump Failure Using Advanced Pattern Recognition Modeling and Condition Monitoring Techniques

  Case Study - Early Detection of Vertical Pit Pump Failure Using Advanced Pattern Recognition Modeling and Condition Monitoring Techniques

  ×

  For more than 10 years, Weston Station has been leveraging on-line Advanced Pattern Recognition (APR) monitoring to help expose unit performance and equipment health issues. With minimal plant instrumentation, the APR software detected a small and sudden increase in motor vibrations on a critical river water supply pump. The change was relayed to the resident plant vibration specialist. Field analysis was performed which revealed evidence of an impending pump failure on this 2 year old pump. This presentation will cover details of the anomaly detection, plant instrumentation, field analysis and findings of the overhaul. Losing redundancy in critical plant equipment increases the risk of a forced unit outage. The effective use of technology, trained staff, processes, and communication enabled the plant to plan proactively and reduce the time of this exposed risk.
  ×

  Kelly Whittenberg

  Performance Engineer

  Black & Veatch

  Kelly Whittenberg is a Performance Engineer within Black & Veatch's global energy service business. His experience has focused on improving coal-fired power generation unit performance and reliability via the Monitoring & Diagnostics product powered by the ASSET360 data analytics platform. This experience includes understanding plant systems, plant controls and operation to identify and help solve problems through data analysis and applying performance metrics. With more than 20 years of experience in the power industry, his primary role has been directly interfacing with plants through Black & Veatch remote monitoring services to various utilities. In delivering this service, he acts as an extension of a plant engineering staff by remotely monitoring day-to-day operation to identify and analyze heat rate and capacity limiting issues, quantify impacts, and provide recommendations for corrective action. Whittenberg is actively involved in providing this service to clients and serves as a mentor to other engineers in the Black & Veatch remote monitoring center.
• Chris StrohmSales ManagerMetso Flow Control-Performance Solutions
  Increasing production without increasing energy

  Increasing production without increasing energy

  ×

  A modern control system at a power plant can gather gigabytes of data, every day. Savvy engineers have applied software tools and methodologies to extract immediate value. This presentation shows how users are applying Big Data Analytics to improve the performance of all aspects of control systems, from instrumentation to advanced controls supporting company KPIs. Manufacturing control systems have been a source for big data since the late 1980\'s. Digital control systems started to capture data, and feed it to process data historians. Initially, users were selective, and only captured the most important process measurements from machines, turbines, compressors, and other large equipment. As computer memory, storage, and network speeds increased capacity, users were quickly storing thousands of pieces of information about the process, at faster and faster sampling rates. Today, in a modern power plant, it is common to collect tens of thousands of process measurements, valve positions, and control system statuses, at sampling rates as fast as once per second. It is even possible to store all of that data without loss of information in data compression. Analyzing this flood of data to find the most important information has been a challenge to engineers for several decades. This presentation shows new tools and techniques that have demonstrated success in finding, prioritizing, and resolving major issues in process plants. Specific techniques and examples will be shown for: * Finding and quantifying mechanical issues with control valves * Measuring the effectiveness of the control system * Identifying turbine stiction to steam temperature improvement * Measuring the performance to provide company supported KPIs
  ×

  Chris Strohm

  Sales Manager

  Metso Flow Control-Performance Solutions

  A Business Development Manager and IT Administrator with over 10 years’ experience in the process industry helping customers achieve precision state tuning and performance monitoring software. Currently, Chris is working as globally-minded Sales Manager for Metso ExperTune based in Atlanta, Georgia USA. Previous positions Chris has held: • IT manager and Support specialist for ExperTune, Inc • Marketing and Sales Specialist for GE Medical Chris holds multiple IT Administration certificates and is a Microsoft Certified Solutions Expert (MCSE).
• Martin GasconSenior Data Scientist and Product ManagerIntertek
  Predicting Power Plant Remaining Useful Life using Machine Learning

  Predicting Power Plant Remaining Useful Life using Machine Learning

  ×

  There are new challenges for plant operators due to the increase share of renewable energy. Plant Operators must maintain high reliability and high profits while plants are being required to be more flexible to compensate for the variable generation addition of these renewables into the grid. Plant operators must deal with the thermal strain and the wear-and-tear of such operations. The aim of this study was to determine which machine learning algorithm gives the best estimation of remaining useful life using plant operations. The flexible operation for all units was estimated using the Intertek hourly MW analysis and damage modeling software Loads ModelTM. We used several plant features as predictors (such as equipment manufacturer, operating regime, ramp rates, etc. We tested five different Machine Learning techniques and found that Gradient Boost is the best approach to predict the reduction in Life Span of the plant with over 90% precision.
  ×

  Martin Gascon

  Senior Data Scientist and Product Manager

  Intertek

  Dr. Gascón manages the data science program at Intertek AIM. His expertise focuses on evaluating large data sets from fossil and renewable energy power plants. He uses statistical analysis, mathematical modeling and machine learning techniques to generate predictive models to improve plant efficiency. Dr. Gascón earned a Ph.D. in Nuclear Physics at USC (Spain) in 2010 developing detectors to study nuclear reactions. During a 2-year postdoc at Stanford and 3-year fellowship at Berkeley National Laboratory, he studied how to improve the functionality of radiation detectors by applying extreme conditions and using novel techniques and materials, with the objective of increasing the sensitivity of these detectors for national defense applications. Dr. Gascon leverages his broad experience, programming skills and quantitative analytical background to solve a wide variety of problems involving large data sets in the power industry. He is the principal architect of Intertek’s data analytics platform INGRID. His current responsibilities include the development of COSTCOM, a real-time cost analysis tool for power plant operations, WearMulti and Opcon, two multipurpose programs used in the nuclear industry. He also developed WINDLIFE, a fully customizable web-application for wind farms featuring anomaly detection and remaining life estimation. Dr. Gascon has also developed programs and algorithms to predict the impact of the variability in power generation of renewable energy on the wear and tear of fossil fuel plants.
• Rodney DurbanDigital LeadABB
  Creating a single version of operational truth- applying digital solutions to bridge generation and trading teams

  Creating a single version of operational truth- applying digital solutions to bridge generation and trading teams

  ×

  By 2030, IEA anticipates that renewables and small scale distributed generation will account for nearly a quarter of power generation. As a result, the revenues of conventional assets will move from base load generation revenues to capacity payments for grid firming and ramping. Many of the world’s conventional thermal assets, some with 30 years of operation or more, were designed for base load operation. For conventional power generation companies operating in increasingly volatile markets, the importance of accurately defining plant physical characteristics and cost profiles has never been more crucial. In addition to making better trading/dispatch decisions, conventional thermal plant operators are cycling more frequently and increasing ramp rates, all of which risk accelerating maintenance intervals and costs. While there is significant risk in operating conventional assets in new modes, the underlying business processes that drive dispatch/trading decisions have changed very little over the past twenty years. For day ahead markets, generation teams rely on highly static models to forecast their capacity, heat rate and start cost estimates for dispatch/trading teams’ day ahead decisions.
  ×

  Rodney Durban

  Digital Lead

  ABB

  Rodney Durban leads Enterprise Digital Sales for ABB’s Energy Business which includes all related energy vertical segments of Oil, Gas, Chemical, Power Generation, and Water. Since coming to ABB in 2006, he has held roles in account management, sales management, business development, and strategic marketing. Most recently, Rodney led global capture teams offering the complete ABB portfolio for mega projects in oil\gas\chemical and 3C manufacturing segments. Prior to joining ABB, Rodney worked inside the power generation industry as an I&C Supervisor, IT Analyst, and Control Systems Engineer. Rodney’s first exposure to IT\OT convergence was in the early 2000s where he was responsible for implementation of digital systems at thermal power plants as well as the deployment of cyber security controls in response to NERC CIP requirements.

Plant managers and engineers are constantly managing risks. Should a unit be taken offline to make an emergency repair or can it be safely operated until the next planned shutdown? How can data analytics help improve the decision-making process? In this session peers share case studies showing how analytics are used in their plants for risk assessment and management.

• Jason MakansiPresidentPearl Street
  ×

  Jason Makansi

  President

  Pearl Street

  Jason Makansi, a recent transplant to Tucson, is president of Pearl Street (www.pearlstreetinc.com), whose clients range from electric utilities, Fortune 50 global energy firms, growing technology firms, and garage shop inventors. He specializes in technology deployment for the electricity and energy industries. A life-long entrepreneur, he has raised venture capital for clients, assisted in creating partnerships and collaborations, launched and managed a hedge fund focused on the electricity supply and delivery value chain, started up innovative publications in energy and environmental affairs, helped tech-based companies grow in preparation for acquisitions and exits, started up and managed two policy groups to support the grid-scale energy storage business, and launched a publishing company (now managed by his wife and two partners). He is the author of four professional books, the most recent Painting By Numbers: How to Sharpen Your BS Detector and Smoke Out the Experts, winner of two prestigious national awards for independently published books, and including An Investor’s Guide to the Electricity Economy and Lights Out: The Electricity Crisis, The Global Economy, and What It Means To You, the last two published by John Wiley & Sons. He is co-author of Knowledge Management In the Digital Age, focused on how to get the most from people, devices, and data in the electric power industry. Makansi is a frequent keynote speaker, guest lecturer, workshop leader, trainer, panelist, and presenter on technology, business, market, and policy innovations for energy and electricity. In the area of service, he mentors students in innovation and entrepreneurship, delivers special lectures at the university level, leads tours at the Titan Missile Museum south of Tucson, interviews prospects for his college alma mater, plays viola and performs with inter-generational chamber groups and orchestras for seniors, juniors, and the general public. He earned his BS in Chemical Engineering from Columbia University, has taken graduate courses in technology management, and several graduate courses towards a higher degree in Sociology. He moonlights as a fiction writer, evaluates fiction submissions for two well-known literary journals, and has published more than a dozen short stories. He serves as Associate Editor for December Literary Journal and Contributing Editor for River Styx, both St. Louis-based literary journals with a national reputation for literary excellence. He judges short stories for publishing and writers associations, and recently delivered a workshop on the short story for the Arizona Authors Association. Makansi’s first novel, The Moment Before, came out in March 2018.
• Anup AggarwalDirector of Digital SolutionsStructural Integrity
  Real-Time Health Tracking of Headers and Piping

  Real-Time Health Tracking of Headers and Piping

  ×

  As power plants have expanded the use of online monitoring, their initial focus has been on either diagnosing instrument errors or failure, or improving efficiency. Significant benefits can also be gained by monitoring in real-time the health and rate of life consumption (aging) of key components that suffer progressive degradation and damage. As an example, equipment such as headers and piping suffer from creep due to operation at high temperature or fatigue due cyclic operation. Plant operators use the resulting life consumption estimates to guide asset management decisions, such as changes in operating procedures, targeted inspections, or off-line analysis of anomalous conditions. This paper discusses the technology available to add components such as headers and piping to the growing ‘Internet of Things’. An example will be provided of steam header damage tracking to summarize how process instrumentation (pressure, temperature and steam flow) and terminal tube thermocouples can be used in combination with the specific geometric configuration and material type of the header to track the following damage modes: - Oxide growth and creep damage accumulation in terminal tubes - Creep related, header side, stub tube damage - Fatigue related, tube side, stub tube damage - Creep and fatigue damage in axial and circumferential ligaments between tube boreholes - Creep damage in girth and seam welds Once implemented, better informed decisions are made regarding future inspection, maintenance or replacement plans for equipment experience flexible operation, rapid load changes or extensive cycling; as well as for aged headers with large steam temperature imbalances.
  ×

  Anup Aggarwal

  Director of Digital Solutions

  Structural Integrity

  Mr. Aggarwal has 20 years of experience designing, building and delivering advanced Asset Performance and Asset Integrity Management solutions to the power generation industry. These solutions have been applied to make equipment more reliable, improve availability, extend the life of equipment, meet regulatory compliance requirements, and most importantly, help keep personnel safe. Mr. Aggarwal is working with power industry experts at Structural Integrity to build the next generation of software products that will transform the way industrial equipment is operated and maintained. Structural Integrity has always been at the forefront of technology, and is now building advanced equipment models and digital twins to help partners go from preventive to predictive maintenance while improving the equipment efficiency and reducing the cost associated with managing the equipment.
• Manjari AsawaDirector, IoTWipro
  Prognostic Maintenance of Turbines and Assets

  Prognostic Maintenance of Turbines and Assets

  ×

  Predictive maintenance software requires large past data from each asset class in consideration. As there are many manufacturers with many models, the data while similar can come from variable sources. Further, various machines are of different age with different wear and tear and have been working in different environmental conditions. Existing approaches try to apply the same model to new sources or start fresh. This reduces the chances of accurate prognosis. Our proposed method decomposes the neural network structure, and by using both “supervised” and “un-supervised” learning at different steps creates a “continuously learning” model that can discover variations in the fault scenarios due to differences in the asset model, age and environmental conditions of the assets. We applied our approach to gas turbines with 25 base parameters that were captured continuously via sensors such as lever position, speed, compressor torque, output temperature, output pressure etc. to predict the propensity of failure of the gas turbine. Our trial runs show that while the current best in class solutions could achieve at most 79% prediction accuracy, our proposed solution achieved over 90% prediction accuracy across the considered installed base. This real-time tracking of the components\' health enables users to trigger gas turbine maintenance activities as necessitated by the condition of the specific target system and helps nip the problem even before it happens. This helps in decreased maintenance costs, reduced mean-time-to-repair and downtime of equipment and avoids operational shutdown and related revenue losses. We also created a \"Generative Learning\" model that simulates real-life data and enables the solution to perform as a digital twin. This helps to understand asset performance or potential degradation in performance that could be due to environmental factors and allows maintenance managers to digitally perform stress tests on the asset to see how the it would perform in different environments without actually physically deploying it. Our core algorithms are implemented using Google’s Tensor Flow libraries.
  ×

  Manjari Asawa

  Director, IoT

  Wipro

  Dr. Manjari Asawa is Director of IoT and 5G Solutions at Wipro, a leading solution integrator company. With over two decades of research, engineering and product management experience in cellular and networking technologies, she works with electric utilities and communication providers to address business and technical transformation challenges. She received her Ph.D. in Electrical Engineering from the University of Michigan at Ann Arbor.

This session will introduce the next step in the connected plant - how to increase efficiency through digitization, and reviewing the best solutions to asset optimization.

• Chris WelchRotational DevelopmentTennessee Valley Authority (TVA)
  ×

  Chris Welch

  Rotational Development

  Tennessee Valley Authority (TVA)

  I've been involved in some fantastic projects over the years. They tend to be bleeding edge technologies, which is a blast! Wired networks, wireless networks, servers, and everything in between are my modus operandi. My goals in life include solving problems with new technologies, and using technology to make a difference in the lives of those around me. Specialties: My specialties include engineering solutions, and translating between those who speak technology and those who need it. I love to build strong/passionate teams that can reach the moon and beyond. Come to us with your problems, and we will solve them. Recent Projects: Industrial sensors (IIoT), non-ip based wireless communications, machine learning, AI, robotics, hybrid manufacturing, and changing the world bits at a time. Putting the techy stuff aside for a bit, my other passion is developing leadership and making connections between amazing people. Taking advantage of my experiences from a broad range of fields, I have been able to leverage my time around some very different thinkers. I like to share those thinking styles to others over discussions and sessions while we develop ideas and solutions. I cannot think of anything that is more fun than that at work.
• Dr. Stephen ZitneyProcess System EngineerNational Energy Technology Laboratory
  Dynamic Model-Based Digital Twin, Optimization, and Control Technologies for Improving Flexible Power Plant Operations

  Dynamic Model-Based Digital Twin, Optimization, and Control Technologies for Improving Flexible Power Plant Operations

  ×

  Dynamic model-based digital twins, optimization, and control are key enabling technologies for improving the flexibility of fossil-fueled power plants. With the increasing penetration of intermittent renewable energy sources into the electrical grid, existing fossil power plants that were originally designed for base-load electricity generation are increasingly forced into cycling operations. Cycling includes on/off, load-following, part-load, and minimum load operations. These flexible operating modes lead to not only increased equipment degradation, plant downtime, operating and maintenance costs, and environmental emissions, but also decreased plant reliability, efficiency, and profitability. At the U.S. Department of Energy (DOE), the National Energy Technology Laboratory (NETL) and its research partners are developing and applying a wide range of dynamic modeling, simulation, optimization, and control technologies to address the flexible operation challenges facing fossil power plants connected to the modern grid. NETL researchers are developing plant-wide dynamic models with rigorous mechanistic equipment-level models to improve the reliability and efficiency of cycling operations. Using these digital twins, it is also possible to develop cycling concepts that lead to faster, and still safe, ramp rates, while minimizing equipment damage and emissions. In addition, NETL and its research partners are developing optimal sensor placement algorithms to enhance condition monitoring and fault diagnosis. As cycling operations are required to keep up with renewable energy, emissions from existing power plants will significantly increase. As a result, NETL is developing advanced process control strategies for maintaining emissions within environmental limits during cycling operations. Tools for generating fast data-driven digital twins from high-fidelity dynamic models have been also developed to decrease computational expense for use in real-time optimization and control. In addition, a 3D virtual dynamic simulation-based digital twin has been developed and deployed at NETL and at the Advanced Virtual Energy Simulation Training And Research (AVESTAR®) Center at West Virginia University. The 3D digital twin for an integrated gasification combined cycle (IGCC) power plant with CO2 capture combines high-fidelity, real-time dynamic simulators for “gasification-based chemical processing and cleanup” and “combined-cycle power generation” together with 3D virtual reality technology. The IGCC digital twin serves as a virtual test bed to help address the critical R&D, workforce training, and education challenges facing the power industry on its drive toward more flexible plant operations.
  ×

  Stephen Zitney

  Process System Engineer

  National Energy Technology Laboratory

  Dr. Zitney has nearly 30 years of R&D experience in process systems engineering for chemical and energy industry applications. Since 2004, he has directed energy research at the U.S. Department of Energy’s National Energy Technology Laboratory and launched the AVESTAR Center for Advanced Virtual Energy Simulation Training And Research at West Virginia University, where he also serves as an Adjunct Professor of Chemical Engineering. Before joining NETL, Dr. Zitney held senior consulting and R&D management positions at Fluent, Aspen Technology, and Cray Research. He received his Ph.D. degree in Chemical Engineering from the University of Illinois.
• Cynthia MasonCloud Applications Services ManagerSiemens PLM Software
  Transforming Operations with IoT

  Transforming Operations with IoT

  ×

  Siemens will share client case studies that include the Digital Twin and autonomous solutions. The presentation will include a client to support the discussion if possible, however, if timing or confidentiality concerns do not allow, the presentation will be delivered by Siemens business & technology representatives. The presentation can be summarized as: Point Cloud, Mesh Networks, Edge devices, Machine Learning, cloud computing, Digital Twins and other technologies all contribute to digitally transforming operations with gains in competiveness and productivity through these innovations and with the IoT. Siemens MindSphere provides an operating system that utilize “Big Data” technologies to gain operational insights from facilities production performance, conditions and status of assets and overall resource utilization. Integration of operations with digitalization solutions associated with mechanical, electrical, process and controls information allows transformations and optimization as not seen with past technologies. New data correlations, cross references, and analysis applied across the business lifecycle – Design, Operation and Performance in the market, all contribute to faster, more informed, strategic decision making. Machines can be “Self healing”, and certain operations can become autonomous through advances in digitalization and IoT which include translating the physical business world into digital models of feed stocks, facilities and workflows.
  ×

  Cynthia Mason

  Cloud Applications Services Manager

  Siemens PLM Software

  Cynthia Mason is part of Siemens Industry, Process Automation Digitalization team serving the US market. She engages opportunities with a focus on digital solutions for the process and water industries which may include SIWA Siemens Water Management Suite, MindSphere, which is Siemens open, cloud based Internet of Things, (IoT), Operating System, (OS) and digitalization consulting. Cynthia works with clients to identify steps to enable digital transformations, new business models and digital services within the process & water industry. Cynthia has over 25 years of experience with roles spanning from engineering design and process plant operations to sales and program development. She is a registered P.E. in Texas, with a BS in Chemical Engineering from Cornell University and a MBA from the University of Texas at Austin.
• Mark BeckerSenior Marketing Manager, Digital Solutions, Nalco WaterNalco Company
  Predict Performance, Prolong Asset Life and Optimize Total Cost of Operation with OMNI

  Predict Performance, Prolong Asset Life and Optimize Total Cost of Operation with OMNI

  ×
  ×

  Mark Becker

  Senior Marketing Manager, Digital Solutions, Nalco Water

  Nalco Company

  Mark Becker is the Marketing Manager for Digital Solutions for Nalco Water. Mark is responsible for driving the strategy and execution of Nalco Water’s Commercial Digital Solutions within the Heavy industries: Power Generation, Primary Metals, and Chemical Manufacturing. In addition, he leads the development of new automation and digital platforms, which leverage technology and analytics to solve our customer’s water challenges. He has been with Nalco for 4 years in a variety of Marketing roles, primarily supporting automation platforms and wastewater solutions. Prior to joining Nalco, Mark worked in Sales, Marketing and Corporate Account Management roles for ifm efector inc., a firm specializing in industrial automation solutions.
• Mike GreeneOperational Technology LeadNova Scotia Power
  Enabling Asset Management Through Strategic Use of Data

  Enabling Asset Management Through Strategic Use of Data

  ×

  This presentation will demonstrate how Nova Scotia Power uses a combination of data from MMS systems, Measurement systems, manual readings and third party NDT test results to create real-time Asset Management Dashboards and Health Indicators. It will also so how strategic use of software and data can deliver predictive alerts to subject matter experts on a timely basis to avoid costly forced outages.
  ×

  Mike Greene

  Operational Technology Lead

  Nova Scotia Power

  Mike has over 25 years of experience with Databases, Information Systems and Plant Automation. He is currently the Operational Technology Lead for the Enterprise Asset Management Office of Nova Scotia Power, the utility for the province of Nova Scotia. He is responsible for the successful integration of NSPs many Operational information systems into a platform that provides end users with a dynamic asset based Operational Risk across the NSP Thermal fleet.
• Rahul ChillarSenior Director – Product Management Asset Performance ManagementGE Power Digital Solutions
  Co-Presenter on Enabling Asset Management Through Strategic Use of Data

  Co-Presenter on Enabling Asset Management Through Strategic Use of Data

  ×

  Like many utilities, Nova Scotia Power faced a series of challenges that included aging generating assets (many approaching the 40-year mark), a growing knowledge gap, changing generation profile and utilization, increasing stakeholder scrutiny and an expectation of transparency by regulatory agencies. Key to meeting those challenges was the standardization of its work management processes throughout the steam fleet and hydro and combustion turbines group. Nova Scotia Power engineers applied real time analytics to their asset management program, following a layered strategy consisting of work management, reliability programs, advanced technology, information management, and asset planning. Nova Scotia Power uses APM Strategy to manage and extract value-added benefits from its flood of asset-related information. Data from a host of sources including: EAM, preventive and predictive systems, condition monitoring, operator rounds and pattern recognitions—along with performance data, are imported into APM. Once risk strategies have been defined and validated, APM provides a comprehensive tool to employ them through an implementation package. This allows multiple strategies to be combined into a common package but implemented into various work management and monitoring systems. Operators use performance monitoring and analysis to identify maintenance work and the potential losses determine urgency. The layered structure also uses predictive analytics to recommend operations and maintenance actions. The data rich root cause analysis helps the utility meet compliance reviews and conduct product and data management and ops surveillance; operator log reviews; history and losses reviews; rapid reviews for anomalies; vibration analysis. Time savings, early issue identification, deductive support, and a narrowed investigative focus of failures are also major benefits. These benefits have translated into more than $35M in cost savings over 5 years.
  ×

  Rahul Chillar

  Senior Director – Product Management Asset Performance Management

  GE Power Digital Solutions

  Rahul Chillar is the principal product manager for GE’s Digital Application – Asset Performance Management (APM) Rahul is responsible for product managing and developing the APM application for the power industry. This includes the development of product, service and solution roadmaps, and designing new customer experiences and business models. Rahul has worked for 16 years in the Power industry with over 55 + patents, Rahul has held multitude of roles from power design, product dev engineer, customer service engineering, new product development, business development to leading risk management in GE contractual service agreements and most recently digital transformation for GE in the Asset performance space. Rahul has Masters in Mechanical Engineering from University of Illinois Urbana Champaign and Bachelors in Mechanical Engr from Mumbai University.

Digitization can have many advantages but none more important than improving efficiency and enhancing maintenance capabilities. In this session, experts will share lessons learned from various projects that incorporated wireless solutions; optimized resources during shutdown, turnaround, and outage projects; and improved inspection techniques using drone technology.

• Greg TanckAsset Management Services GroupBlack & Veatch
  ×

  Greg Tanck

  Asset Management Services Group

  Black & Veatch

  Greg Tanck has more than 20 years of experience in the power industry and currently leads the Asset Management Services group at Black & Veatch. Greg’s business line is responsible for providing remote monitoring services and software to more than 100 power generation and industrial facilities around the world. Previously, Greg held the lead role in implementing integrated neural net based combustion optimization software to improve power plants emissions management. Greg also has extensive experience helping facilities implement continuous efficiency and reliability improvement programs using data analytics.
• James MurrayPlant ManagerOrlando CoGen
  Making Maintenance Decisions Using Wireless Remote Condition Monitoring System: A Case Study at Orlando CoGen

  Making Maintenance Decisions Using Wireless Remote Condition Monitoring System: A Case Study at Orlando CoGen

  ×

  Summary: In late 2016 OCL set out to trial a remote condition monitoring technology. The purpose of the trial was to see if one of the new wireless condition monitoring systems could help the plant affordably improve efficiency and/or effectiveness through better planning of operations and maintenance activities. After evaluating a few competing technologies, OCL purchased 4 wireless vibration & temperature sensors and a cellular gateway device from ProAxion for the QF plants pumps and motors to trial the technology. The pump and motors were originally installed in 1993 and never had any remote monitoring sensors installed on it. After successfully completing the first phase of the trial to test ease of installation and setup, OCL purchased 8 additional sensors for the 4 cell cooling tower In April of 2017. Each cell has a 100 HP motor with an extended shaft to a right-angle gearbox which drives the fan. The gearbox is located directly under the fan inside the cell and is a very wet environment. The plant’s Cooling Tower maintenance outage was scheduled for December 2017, and had planned on replacing one of the four motor and gearbox sets as part of a pre-planned maintenance schedule based on operational hours. However, the ProAxion system was showing that the motor and gearbox scheduled for replacement looked to be in good health, based on the vibration and temperature data collected. However, one of the other motors not scheduled for replacement was in an alarm condition before the outage and ProAxion had indicated that it was likely near end of life, with vibration patterns associated with bearing failure present. Using this information OCL staff replaced the motor in alarm, and did not replace the motor and gearbox set as originally planned. The result was a double benefit, having saved the expense of replacing an otherwise healthy motor and gearbox, (including the relatively difficult process of removing the gearbox from under the fan assembly), and replacing a motor that was nearing end of life and removing the potential threat of an unexpected failure causing a reduced capacity event. After this success OCL purchased 4 additional sensors for our Steam Turbine vacuum system In January 2018 . OCL currently has 16 vibration & temperature sensors in operation that are monitored 24/365. If a sensor goes into alarm the ProAxion system automatically sends email and text alerts to inform the OCL staff. Our trial of this particular technology has been a success. We’ve found that installation, setup, use and understanding of the system has been straightforward and easy, and the results have proven a positive cost/benefit for us.
  ×

  James Murray

  Plant Manager

  Orlando CoGen

  Twenty-eight years of experience in the independent power and international power generation industry with skills in plant management, Operation and maintenance, startup and commissioning, operations and maintenance training support, construction management support, project development support. Current responsibilities Plant Manager of Orlando Cogen 2007 – present. Provide technical support and project due diligence support throughout the (CAMS) network. My overall knowledge in the industry is founded on a strong operational and technical knowledge base, experience and continual growth through various management positions for Coastal Power, Enron, El Paso Merchant Energy, Northern Star Generation Services and CAMS.
• Patrick SaraccoVP Technology SolutionsCyberhawk
  Driving Efficiency Through Drone-Captured Data and Asset Visualization Software

  Driving Efficiency Through Drone-Captured Data and Asset Visualization Software

  ×

  Maintaining the integrity of power generating equipment is fundamental to ensuring plant performance. Over the last 10 years, drones have emerged as a viable solution for conducting asset inspections, bringing significant benefits to operators/owners; reduced costs, quicker inspection times, ability to inspect live assets, avoidance of shutdowns and minimised safety risk to personnel through avoiding scaffolding, rope access and working at height. Cyberhawk has completed thousands of projects over the last ten years in the oil and gas, transmission and distribution and nuclear industries and can present numerous case studies which highlight these benefits in real world projects. Even further efficiencies come into play, however, when operators begin to use the data captured to visualize their assets. Industry specific asset visualization software now exists which aids asset managers in the management of their portfolios. Data can be stored, hosted and analyzed with defects, areas of interest, engineering commentary and high-quality imagery available to view at a glance. Assets can be viewed using a map based interface with defects highlighted by severity and location. For the transmission and distribution sector, software has been further developed to incorporate tablet apps available to linespersons, so all visual data, work orders and asset status are stored in one central repository. As well as imagery captured by drone and linespersons, such software can also integrate with data captured from other sources, including helicopter, scaffolding or rope access, as well as other asset management systems. The benefits to the operator from this approach mean better organisation of data, the ability to view the escalation of defects and the enabling of temporal analysis. The frequency of site visits is reduced through an up-to-date asset record being available from your desk, and asset teams including contractors can better communicate using one single source of data hosted in the cloud.
  ×

  Patrick Saracco

  VP Technology Solutions

  Cyberhawk

  Patrick has extensive experience in the global oil and gas industry having worked for a number of technology focused service companies in this sector. His multilingual skills and extensive knowledge of the international energy sector put him in a strong position to understand the requirements of Cyberhawk’s global customers. Patrick is passionate about developing compelling inspection solutions that improve safety and deliver real value to our customers. He holds an MEng in Environmental Engineering, a Diploma in Surveying and has brought many of Cyberhawk’s world firsts projects to the wider market.
• Paul MuirPresident and Chief Revenue OfficerMobideo
  The Digital Outage - Lessons Learned and Critical Outcomes from 60+ Digital Outage projects

  The Digital Outage - Lessons Learned and Critical Outcomes from 60+ Digital Outage projects

  ×

  • How to increase real time visibility and drive better and faster decision-making for on-time and on-budget outages • Innovative means to reduce money spend on contractors and improve resource utilization • Respond immediately to evolving scope resulting in reduced cycle times • How to optimize shift-handover as well as work-transfer between crafts • Methods to improve outage planning with granular data capture and analysis • Explore new approaches to dramatically reduce outage costs and schedule
  ×

  Paul Muir

  President and Chief Revenue Officer

  Mobideo

  Following a short career as a Marine Engineer Paul graduated from Heriot-Watt University, Edinburgh with an honors degree in the then new field of Computer-Aided Engineering. After a short period as a practicing design engineer at Unisys, Paul entered the IT industry ultimately acquiring and growing the UK’s largest Computer Aided Design (CAD) system vendor. In 1997 Paul founded McLaren Software with a focus on Asset Intensive Industries including Oil & Gas, Utilities, and Construction. The business gained a market leadership position and international footprint with 60% of revenues in North America and significant businesses in Europe and Australia. Paul has helped many Fortune 2000 companies improve Capital Project and MRO performance and compliance by leveraging enterprise software and cloud solutions focused on Project Controls, Contract Management, Mobility, and Business Process Management. Today, as President of Mobideo, Paul continues to work with customers looking to leverage emerging Industrial Internet of Things (IIoT), Enterprise Mobility, Cloud, and Analytics technology to drive service process transformation in the Oil & Gas, Chemical, Aviation and Power Industries. Paul has lived in Houston Texas with his family since 2004.

With more information, more space is needed. This session will help you ensure that your information is secure even while using the cloud.

• James GoosbyI&C Support General ManagerSouthern Company
  ×

  James Goosby

  I&C Support General Manager

  Southern Company
• Mr. Allen KentNERC CIP SpecialistNAES
  BCSI storage on the Cloud: NERC CIP Compliance Concerns

  BCSI storage on the Cloud: NERC CIP Compliance Concerns

  ×

  The viability of Cloud use for storage of Bulk Electric System Cyber System Information (BCSI) is possible but requires both the Cloud Service Provider (CSP) and Registered Entity (RE) to take proactive security measures. This presentation will explore some recommendations to ensure NERC CIP Compliance.
  ×

  Allen Kent

  NERC CIP Specialist

  NAES

  Allen Kent has over 25 years of diverse work experience that includes managing, training, and continual professional development in information technology, specifically in cyber security. He has achieved IT certification in CISSP, CISA, MCSE:Sec, VCP, and several others. He taught information technology courses at the college-level for 7 years. Mr. Kent currently works as a NERC CIP Reliability Specialist for NAES Corporation with over 6 years of NERC CIP compliance experience. Prior to that, he worked in the banking industry as a Technical Support Manager and Senior Security Analyst.
• Brian RomanskyDirector, Business DevelopmentOwl Cyber Defense
  IIC Perspective on Secure Cloud Connections

  IIC Perspective on Secure Cloud Connections

  ×
  ×

  Brian Romansky

  Director, Business Development

  Owl Cyber Defense

  Brian Romansky has over 25 years’ experience in security technology and innovation in payment systems, healthcare, and logistics. He is currently leading the Industrial IoT security program at Owl Cyber Defense. Formerly he served as a technical expert for the US Department of Transportation SCMS V2X initiative and as Director of Corporate Innovation at Pitney Bowes. Mr. Romansky holds a Master of Science degrees in Electrical Engineering and R&D management from Rensselaer Polytechnic Institute and is an inventor on 25 patents.
• Dr. James HuntCEOaicas GmbH
  Edge Computing and Security: It is not just Data

  Edge Computing and Security: It is not just Data

  ×
  ×

  James Hunt

  CEO

  aicas GmbH

  Dr. James J. Hunt is a cofounder and CEO. He has a BS in Computer Science and Physics from Yale University, an MA in Computer Science from Boston University, and a Doctorate of Engineering in Computer Science from the University of Karlsruhe. He spent several years as a researcher at the Massachusetts Institute of Technology Lincoln Laboratory developing CAD software for restructurable wafer scale integrated circuits (RVLSI) and a parallel Lisp system for signal processing. Dr. Hunt led the IST project HIDOORS and was the technical lead for the Artemis CHARTER project, which developed tools and technology for Java-based safety-critical systems. He is a contributing member of the Java Community Process (JCP), particularly for Safety-Critical Java (JSR-302) and the Real-Time Specification for Java (JSR-282), where he is the specification lead. He was European Cochair for the subgroup on object-oriented technology of the SC-205 Plenary for updating software safety standard for avionics as well. The result of which is now published as DO-332 and ED-217.