Where is your company in its digital journey? Whether you are just creating a foundation or actively deploying digital technologies, we have a workshop for you. The Connected Plant Conference will kick off the event with two simultaneous one-hour digitalization workshops with CPC Advisory Committee members. One workshop will focus on the foundational discussions needed to initiate the process of taking advantage of available digitalization technologies. The other workshop is for companies that are further along in the digital journey. In it, committee members and attendees will share lessons learned to enable progress in the digital journey.

In this session, industry leaders will share the challenges and lessons learned from their experiences with digitalization initiatives. By discussing real-world projects with others with direct experience, attendees can gain perspective on how to take the next steps in their own digitalization journey. The objective of the workshop is to help attendees strategize how to move past the testing and piloting phase to deploying digitalization tools at scales that achieve real business results. Speakers will discuss strategy versus tactical integration planning. Also addressed: How to make sound decisions about which projects to scale up and which to abandon. The distinction between “products” and “projects” will be explored.

Most chemical process industries (CPI) and power-generation companies have by now embarked upon a journey toward realizing a vision for the artful use of large structured and unstructured data sets for real-time plant operation and problem solving. But in many cases, the story of this journey is one of technology, salesmanship and the difficult work of convincing companies that have been in love with Excel spreadsheets and after-the-fact problem solving that significant value lies in already collected, but often under-utilized, data sets. Part of the story involves the application of artificial intelligence (AI), but AI is not the solution – it is a solution. Through the use of volunteer audience participation and a few artful stories, this talk will discuss real-time analytics success stories that are actually simpler than “AI”-infused solutions. It will also discuss where AI fits in the CPI and power sectors, and highlight some experiences from 30 years of engagement in chemical manufacturing that can bring tremendous bottom-line value to your operations.

• Lloyd Colegrove, Writer/Retired Data Services Director at Dow Chemical, Independent Consultant
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  Lloyd Colegrove

  Writer/Retired Data Services Director at Dow Chemical

  Independent Consultant

  

  Lloyd F. Colegrove retired after a 29-year career at Dow, as the Director of Data Services and the Director of Fundamental Problem Solving within Manufacturing and Engineering. He was also the Analytics Platform Director for Dow’s Manufacturing and Engineering’s Industry 4.0 program. Lloyd’s background is in Chemical Physics where he obtained a B.S. and Ph.D. from Texas A&M University. He spent 7 years in Polymer Research in Dow (R&D and TS&D) before moving into Manufacturing in an analytical measurement improvement role for plant quality assurance activities and then as a global quality leader for five Dow businesses. He then moved on to establish a new capability called Fundamental Problem Solving, where top chemists and engineers work on complex, often multi-effect, plant problems. With this established, he refocused his efforts working to establish the Data Services group that has developed and deployed novel analytics applications to improve data-based decision making in chemical operations. These efforts have been recognized with many industry awards. Lloyd consults on real time analytics, artificial intelligence in the chemical industry, and process improvement strategies He is married with two daughters and is an avid cyclist, wilderness backpacker and landscaper.

Digitalization and Industry 4.0 are as much about humans as they are about digital technologies. Understanding how humans interact and use technology are just as important as the technology’s attributes. This session will explore how equipment experts can integrate capabilities with data scientists; where wearable technologies are best suited; and how digital professionals can garner support and buy-in for digital initiatives. It will also explore how job profiles could shift in the future.

• Steven Ford, Compliance and Support Mgr., Southern Company
  Next steps in a Digital Journey

  Next steps in a Digital Journey

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  Steven Ford

  Compliance and Support Mgr.

  Southern Company

  
• Walter Curley, *Invited, Senior Engineer - Continuous Improvement, Nova Scotia Power
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  Walter Curley

  Senior Engineer - Continuous Improvement

  Nova Scotia Power

  
• Stephen Kwan, Director of Product Management for Power Generation/Grid Management, Beyond Limits
  The Nexus of AI and Human Knowledge in Plant Operations

  The Nexus of AI and Human Knowledge in Plant Operations

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  Stephen Kwan

  Director of Product Management for Power Generation/Grid Management

  Beyond Limits

  

  Stephen Kwan is Director of Product Management for Power Generation/Grid Management at Beyond Limits, an industrial-grade AI company that builds advanced software solutions for the most demanding sectors, including energy, utilities and healthcare. Stephen has over 25 years of experience in product management and engineering, holding previous positions in the product organizations at OSIsoft and GE Energy. Stephen holds a BS in Engineering Chemistry from SUNY Stony Brook and a Ph.D. in Materials from Pennsylvania State University.
• Matt Buck, Senior Technical Leader, EPRI Electric Power Research Institute
  The Role of the Digital Worker in a Transitioning Energy Industry

  The Role of the Digital Worker in a Transitioning Energy Industry

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  - This would provide an update of work in this area and how utilities are leveraging DW tools. DW’s role and new ways to carry out work with COVID was an interesting thing that was worked on in 2020.
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  Matt Buck

  Senior Technical Leader

  EPRI Electric Power Research Institute

  

  Matt Buck joined EPRI in 2015 after more than 16 years of operational experience in the power industry. Matt is a currently a Senior Technical Leader in EPRI’s Operations and Maintenance (O&M) research group in the Generation sector. He oversees R&D that supports plant O&M excellence. Matt’s work include O&M performance assessments, work planning, equipment reliability, and changing mission profiles for plants; but his passion is the Digital Worker concept. This work focuses on the use of advanced mobile technology to make the O&M field worker smarter, safer, and more effective.

Viewing “data as an asset” is becoming the new industry norm and companies are beginning to invest heavily in digital tools and technologies to help them leverage their data to generate valuable business insights. However, the value generated from that data is often limited by poor data quality, thereby greatly reducing the return on investment to the business. With the growing need to integrate data across different systems—both internal and external—and the expanding enterprise value chain, the need for trusted high-quality data is greater than ever before. This session will explore real-world pitfalls in data collection and review data management best practices.

• Naimi Mattos Nassiffe Souza, *Invited, Braskem
  PIMs Connectivity Where We Connected All Industrial Data To The Cloud Using Osi Solution

  PIMs Connectivity Where We Connected All Industrial Data To The Cloud Using Osi Solution

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  Naimi Mattos Nassiffe Souza

  Braskem

  
• Chris Welch, *Invited, Enterprise Improvement, Tennessee Valley Authority (TVA)
  • Sensors That Provide Real Quality Information and Proper Connectivity

  • Sensors That Provide Real Quality Information and Proper Connectivity

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  Chris Welch

  Enterprise Improvement

  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.
• Jonas Hellgren, CEO, APERIO Systems
  Data Integrity and Sensor Health for OT and IoT

  Data Integrity and Sensor Health for OT and IoT

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  Turn industrial data into a strategic asset. Increased revenue. Reduced cost. Less downtime.
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  Jonas Hellgren

  CEO

  APERIO Systems

  

  Jonas Hellgren is CEO of Aperio Systems. Jonas has a computer science background with over 30 years of experience in software development. Aperio is the leader in industrial data integrity solutions. We help customers drive profitability and sustainability goals while mitigating risk in their industrial operations. Aperio AI automatically validates operational data at scale to improve data accuracy, security, and value, allowing for better, smarter business decisions based on real-time, trusted, superior data
• Jane Arnold, VP, Global SPS Strategy & Industry 4.0 Execution, Stanley Black & Decker
  Data Integrity and Sensor Health for OT and IoT

  Data Integrity and Sensor Health for OT and IoT

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  Turn industrial data into a strategic asset. Increased revenue. Reduced cost. Less downtime.
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  Jane Arnold

  VP, Global SPS Strategy & Industry 4.0 Execution

  Stanley Black & Decker

  

  Jane Arnold is the Vice President of Global SPS Strategy and Industry 4.0 Execution at Stanley Black and Decker. Her focus is to improve Digitalization in Manufacturing with a special emphasis on emerging technologies including Additive Manufacturing. Arnold is a graduate of Oxford University’s Business Economic Program and has a degree in Mathematics from the University of Houston. Arnold has over 30 years of experience in manufacturing technology and operations. She joined Stanley Black and Decker in February of 2021. FOR THOSE WHO MAKE THE WORLD™ Stanley Black & Decker became the global leader in tools by building innovative solutions and tools for more than 175 years.
• Dr. Francois Laborie, President, Cognite
  IT/OT Data Contextualization: The Key to Delivering the Connected Plant

  IT/OT Data Contextualization: The Key to Delivering the Connected Plant

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  With the rise of data engineers, citizen data scientists, and other industrial data consumer roles, why is it still that only a fraction of all industrial data created actually gets used to its full potential? The bottleneck lies in not having automation in the high-overhead process of finding meaningful data relationships across big data. This is difficult enough for homogenous data sets with thousands of variables but is significantly more challenging when mapping relationships between disparate data sources and formats across IT (ERP, work orders), OT (asset time series, events), and ET (P&ID, single line diagram) domains. Done well, contextualization can reduce data & analytics workloads by 50-90% by approximating a significant amount of industrial subject-matter expertise, empowering data consumers and application development. Done poorly, and data continues to go unused, consumers lose trust in the data they have to work with, and new applications struggle to get momentum and adoption. The good news is that emerging technology and automation is improving the process of contextualizing data from around the organization and setting up dashboarding, analytics, and machine learning applications for operationalized deployment and scale. This Connected Plant session will define how data contextualization works across IT/OT/ET data sets and explores the variety of nontraditional data sources like 3D visualizations that can be contextualized as a result. Then we’ll examine a customer case study and lessons learned from around the industry that showcase the power and scalability of a contextualized data model.
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  Dr. Francois Laborie

  President

  Cognite

  

  Francois runs Cognite's operations in North America, including offices in Houston and Austin, Texas. Previously, he managed Cognite’s overall marketing activities as Chief Marketing Officer. He spent the preceding 11 years with software company Vizrt, where he served as COO and CCO, responsible for all commercial and operational activities. Laborie has a Ph.D. from the Toulouse Computer Science Research Institute.

Digitalization’s most prominent value in the power sector is perhaps its ability to boost an entity’s competitiveness while keeping it in step with customer and stakeholder expectations. This session will break down how different technologies and their implementation can help achieve real business value.

• Dr. Yves Gorat Stommel, Head of Cognitive Solutions, Evonik Digital
  Tools Alone Do Not Create Value: Technology Acceptance at Evonik

  Tools Alone Do Not Create Value: Technology Acceptance at Evonik

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  Yves Gorat Stommel

  Head of Cognitive Solutions

  Evonik Digital

  

  Yves Gorat Stommel is currently working as the head of the Cognitive Solutions team, with a strong focus on setting-up and implementing a comprehensive, company-wide Artificial Intelligence and data analysis strategy. In his current function, Yves’ motivation stems from bringing together people, technology and positive change. By continuously questioning, improving and re-imagining operational and business processes – amongst others by looking at the company through the new “lens” of enabling and disruptive Artificial Intelligence – value is created for both the company and its employees. This does not only require identifying, benchmarking and implementing best practices, but more importantly targeted communication, linking knowledge and an emphasis on global instead of local mindsets and approaches.
• James Martin, Global Operational Excellence Manager, Allnex USA
  Operational Excellence with Advanced Analytics

  Operational Excellence with Advanced Analytics

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  Many manufacturers list sustainability as one of their main corporate objectives. These companies that optimizing their processes to reduce energy consumption and waste gives them a significant competitive advantage. However, identifying opportunities for continuous improvement in this area is difficult, as process engineers often do not have the advanced analytics applications required to investigate their process in a rapid, in-depth way. It is extremely difficult for plant engineers to eliminate waste and improve efficiency when they spend most of their analytics time sifting through time series data in spreadsheets. One analytical technique that engineers can use to improve energy efficiency is through energy modeling. In a process plant, there are hundreds of items of equipment that contribute to the total energy use. Sensor data from this equipment are stored in time series data historians. Subject matter experts (SMEs) can use this data to build predictive models of total energy consumption using each individual item’s sensor data as inputs. With this method, they can determine if each piece of equipment is operating efficiently based on the coefficients in the model. Using traditional analytics tools like spreadsheets, energy models are difficult to build and maintain, and they quickly become outdated and obsolete. Companies like Allnex, however, use Seeq Workbench’s Prediction Tool to construct energy models that they use to decrease their total energy consumption with minimal capital expense. In addition, Allnex engineers operationalize these models by building monitoring dashboards in Seeq Organizer Topic to detect, in near real time, if energy use is deviates from the model. Compared to the hundreds of hours required to build energy models with spreadsheets, the engineers at Allnex have used the Seeq to build and monitor energy models in significantly less time.
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  James Martin

  Global Operational Excellence Manager

  Allnex USA

  
• Nicholas Gigliotti, Analytics Engineer, Seeq Corporation
  Operational Excellence with Advanced Analytics

  Operational Excellence with Advanced Analytics

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  Many manufacturers list sustainability as one of their main corporate objectives. These companies that optimizing their processes to reduce energy consumption and waste gives them a significant competitive advantage. However, identifying opportunities for continuous improvement in this area is difficult, as process engineers often do not have the advanced analytics applications required to investigate their process in a rapid, in-depth way. It is extremely difficult for plant engineers to eliminate waste and improve efficiency when they spend most of their analytics time sifting through time series data in spreadsheets. One analytical technique that engineers can use to improve energy efficiency is through energy modeling. In a process plant, there are hundreds of items of equipment that contribute to the total energy use. Sensor data from this equipment are stored in time series data historians. Subject matter experts (SMEs) can use this data to build predictive models of total energy consumption using each individual item’s sensor data as inputs. With this method, they can determine if each piece of equipment is operating efficiently based on the coefficients in the model. Using traditional analytics tools like spreadsheets, energy models are difficult to build and maintain, and they quickly become outdated and obsolete. Companies like Allnex, however, use Seeq Workbench’s Prediction Tool to construct energy models that they use to decrease their total energy consumption with minimal capital expense. In addition, Allnex engineers operationalize these models by building monitoring dashboards in Seeq Organizer Topic to detect, in near real time, if energy use is deviates from the model. Compared to the hundreds of hours required to build energy models with spreadsheets, the engineers at Allnex have used the Seeq to build and monitor energy models in significantly less time.
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  Nicholas Gigliotti

  Analytics Engineer

  Seeq Corporation

  

  Nicholas Gigliotti is a Customer Success Manager at Seeq. He supports Seeq customers on global deployments of Seeq to ensure that they are successful in meeting their Digital Transformation goals. He holds a bachelor’s degree in Chemical Engineering from Virginia Tech and worked at Eastman Chemical Company prior to joining Seeq in 2020.
• Linda Salinas, Texmark
  Digital Asset & IIOT- Realizing the Value

  Digital Asset & IIOT- Realizing the Value

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  The industrial internet of things (IIoT) has transformed the landscape of Asset Integrity Management (AIM), enabling plant operators to extract more value from their maintenance investments than ever before. AIM platforms communicating with IIoT devices and systems provide increased visibility into maintenance planning and efficiency. Digital Twins provide visibility to insights from very large amounts of data, transforming this data mountain into actionable information. Indeed, this “new normal” can be leveraged by operators to optimize output and thrive. With the right strategies and tools, it is more efficient and cost-effective than one might first suspect. Today’s forward-thinking operators understand that leveraging AIM and IIoT technology to drive effective and meaningful improvement at all levels of operations is imperative to competing successfully in a data-driven industry. From daily maintenance activities to enterprise-level of operational metrics, data has no value unless the right strategy is applied to divulge hidden opportunities for optimization. In this paper, we examine:How facilities can take control of their data to optimize their digital transformation journey:How the Power and Chemical industries can leverage digital AIM with IIoT solutions to address today’s unique challenges while still focusing on maintenance cost and asset lifecycle;How companies can gain insights from integrated platforms, process and equipment data, and visualizations through Digital Twin to drive intelligent operations and set the stage for Artificial Intelligence and Machine Learning. Technology is changing the game for AIM in the Power and Chem industries, and more companies are adopting it. Yet, while many organizations have ideas on how this technology can benefit them, many are unsure how to begin. There are so many variables and options spanning large numbers of assets, geographical locations, and technologies. The potential value is visible, but there is not a clear one-size-fits-all method of determining the path to success.
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  Linda Salinas

  Texmark

  
• Floyd Baker, Vice President, Antea USA
  Digital Asset & IIOT- Realizing the Value

  Digital Asset & IIOT- Realizing the Value

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  The industrial internet of things (IIoT) has transformed the landscape of Asset Integrity Management (AIM), enabling plant operators to extract more value from their maintenance investments than ever before. AIM platforms communicating with IIoT devices and systems provide increased visibility into maintenance planning and efficiency. Digital Twins provide visibility to insights from very large amounts of data, transforming this data mountain into actionable information. Indeed, this “new normal” can be leveraged by operators to optimize output and thrive. With the right strategies and tools, it is more efficient and cost-effective than one might first suspect. Today’s forward-thinking operators understand that leveraging AIM and IIoT technology to drive effective and meaningful improvement at all levels of operations is imperative to competing successfully in a data-driven industry. From daily maintenance activities to enterprise-level of operational metrics, data has no value unless the right strategy is applied to divulge hidden opportunities for optimization. In this paper, we examine:How facilities can take control of their data to optimize their digital transformation journey:How the Power and Chemical industries can leverage digital AIM with IIoT solutions to address today’s unique challenges while still focusing on maintenance cost and asset lifecycle;How companies can gain insights from integrated platforms, process and equipment data, and visualizations through Digital Twin to drive intelligent operations and set the stage for Artificial Intelligence and Machine Learning. Technology is changing the game for AIM in the Power and Chem industries, and more companies are adopting it. Yet, while many organizations have ideas on how this technology can benefit them, many are unsure how to begin. There are so many variables and options spanning large numbers of assets, geographical locations, and technologies. The potential value is visible, but there is not a clear one-size-fits-all method of determining the path to success.
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  Floyd Baker

  Vice President

  Antea USA

  

  Floyd Baker is the Vice President for Antea USA. With over 39 years of experience, Floyd provides valuable insight and perspective on digital transformation and the industrial internet of things (IIoT). He is an asset integrity and digital strategies advisor in global communities. He is a revered global keynote speaker on topics of asset integrity management (AIM), risk based inspection (RBI), digital twin, IIoT, and digitalization. Prior to joining Antea, Floyd served at senior levels for end users of AIM platforms. He has also served about half of his career at executive levels as a strategist/technology driver at companies providing AIM/RBI/Technology services. His background and education are focused on emerging technologies in inspection, mechanical engineering, and materials science.

• Jose Valls, *Invited, IoT Strategist, Microsoft Corporation
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  Jose Valls

  IoT Strategist

  Microsoft Corporation

  

• Alex West, *Invited, Senior Principal Analyst, OMDIA
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  Alex West

  Senior Principal Analyst

  OMDIA

  
• David DeBari, Process Control Engineer, ExxonMobil Corporation
  The Journey to the Future of Automation

  The Journey to the Future of Automation

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  • Global growth, competition, and technology trends have elevated the requirements of automation • A standards-based, open, secure, and interoperable architecture addresses these new necessities • The change in automation technology is taking place and industry needs to engage and capture benefit
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  David DeBari

  Process Control Engineer

  ExxonMobil Corporation

  

  David DeBari is an Innovator and a Process Control Engineer for the ExxonMobil Research and Engineering Company. He has many years of automation experience in both continuous and batch processes and has built, migrated, or replaced control systems on numerous projects. David is the Lead Engineer and one of the original members of ExxonMobil’s Open Process Automation Program Team. He has provided technical leadership for the proof of concept and pilot unit prototype projects, and leads the current Test Bed and Field Trial activities for the ExxonMobil Program.

Learning how to select the right automation technologies and software applications is crucial to any operations and maintenance project. This session will review data visibility software, automation, flexibility, and safety solutions through case studies.

• David Bonislawski, Project Engineer, Lonza AG/Ltd
  Operations Data Visibility fuels Productivity at Lonza

  Operations Data Visibility fuels Productivity at Lonza

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  Chemical processing companies do not have to wait for advanced analytics to benefit from digitalization. Simply increasing operations visibility provides immediate and significant value. This was Lonza’s experience when implementing its own digitalization initiative, PIVOT (Productivity Improvements via Operations Technology). As a global company, Lonza had to adopt a strategy to support small, diverse production sites. One example was a chemical plant with less than 100 employees, facing a growing demand for its product. Lonza implemented a global data infrastructure using OSIsoft’s PI System, and put in place tools for data visualization and analysis such as SEEQ and Microsoft Power BI. The PI System allowed raw data from disparate systems (e.g. control systems, analytical instruments) to be combined and contextualized. Power BI presented an aggregated view of the data that could be filtered by batch, production line or production period of interest. SEEQ provided engineers with a powerful analytics tool to drill into raw data signals and uncover insights. With these tools, it became possible to track key performance indicators and anticipate production issues before they occurred, helping the site adopt a proactive approach to operations. Remote support groups were able to access the same data in real-time, and provide recommendations for process changes. These improvements yielded significant improvements in cost benefit and production volume for the plant. Increased operations visibility also enables advanced digitalization projects. The combination of analytical results with process data provided a clear picture of in-process samples for each batch. This allowed the team to identify a strong use case for on-line analytical instruments and model predictive control. The strong foundation of operations visibility at Lonza will fuel its digitalization roadmap for years to come.
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  David Bonislawski

  Project Engineer

  Lonza AG/Ltd

  

  David is a Project Engineer with Lonza and is a member of the Manufacturing Process Optimization (MPO) group which supports Lonza Specialty Ingredients (LSI). Prior to joining Lonza, David spent time in multiple positions with companies such as W.R. Grace and Akzo Nobel. Over his career he has worked with automation of printed circuit board imaging, automatic control of NOx emissions from mixed acid pickling and automated online analysis of hydrogen peroxide in bleaching systems. He holds a Bachelor’s degree in Chemical Engineering from Northeastern University in Boston, Massachusetts.
• Fatih Abdel Kariem, Asset Management, Marubeni Corp.
  Reducing maintenance costs, reducing risk and increasing reliability by combining engineering and digital solutions on a CCGT

  Reducing maintenance costs, reducing risk and increasing reliability by combining engineering and digital solutions on a CCGT

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  Fatih Abdel Kariem

  Asset Management

  Marubeni Corp.

  

Technology innovation and software development are continuing at a bewildering pace. Emerging technologies hold much promise, but separating the hope from the hype can be challenging. In this session, attendees will hear from leading technology developers and their end-users about how they identified and implemented new technologies or how developed novel applications for “tried and true” digitalization technologies.

• Misbah Syed, Data Scientist, Evonik Corporation
  Forecasting the concentration of chemical components in a production plant through AutoML

  Forecasting the concentration of chemical components in a production plant through AutoML

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  Misbah Syed

  Data Scientist

  Evonik Corporation

  

  Misbah has worked on applications of Advanced Process Controls and Machine Learning based predictive models in the chemical manufacturing industry. With background in Chemical Engineering, he augments theoretical process engineering with modern data driven approaches to optimize production processes.
• Michael Menzel, Machine Learning (ML) Specialist in the Customer Engineering division, GOOGLE LLC
  Forecasting the concentration of chemical components in a production plant through AutoML

  Forecasting the concentration of chemical components in a production plant through AutoML

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  Michael Menzel

  Machine Learning (ML) Specialist in the Customer Engineering division

  GOOGLE LLC

  

  In his role as Machine Learning (ML) Specialist in the Customer Engineering division at Google, Michael supports cloud customers in innovative projects with the goal to build novel software solutions within months or even weeks. In his projects, Michael focuses on challenges in the space of big data and machine learning. Before becoming a Googler, Michael worked in similar roles at Amazon Web Services and in a Predictive Maintenance team at SAP. After his studies in computer science, Michael spent several years in academia to focus on research in cloud compute services, distributed databases & systems at the Karlsruhe Institute of Technology (KIT) and obtained a PhD degree in engineering from the Technical University (TU) Berlin.
• Gupreet Kainth, Principal Design Engineer, Cyient, Inc.
  Multi-Objective Design Optimization of Hydro Generators using Genetic Algorithm

  Multi-Objective Design Optimization of Hydro Generators using Genetic Algorithm

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  Hydro generator design optimization poses an engineering problem constituting conflicting multi-objectives and constraints. Finding a techno-commercially competitive value proposition for the client by arriving at a design uniquely defined by a set of independent design variables (e.g. parameters related to magnetic core, rotor pole, stator & rotor winding etc.) is the challenge. The conventional design approaches have limitations of not adequately exploring the vast design/solution space and missing potential good solutions. An intelligent application to adequately address exploration in the multi-dimensional solution space has been developed by utilizing Genetic Algorithm (GA) in the design process. GAs are a branch of Artificial Intelligence and derivative-free equivalent of Reinforcement Learning. These are nature inspired evolutionary computations relying on Darwin’s principle of survival of the fittest and mimic the operations of natural selection, crossover, and mutation to evolve a population of solutions over generations. Being derivative free, GAs can operate on discontinuous and non-differentiable objective functions. This paper presents, the experience of software application development employing the advanced version of GA namely Non-Dominated Sorting Genetic Algorithm with elitist strategy (NSGA-II) for generator design optimization. The objectives of optimization include cost minimization and efficiency maximization while respecting associated constraints (e.g. temperature rises, transportation constraints, Inertia requirement etc.) The optimization application developed, within the purview of established synchronous generator design principles, caters to optimization tasks corresponding to a vast range of generator specifications. The optimization run delivers not one but a set of Pareto-Optimal solutions enabling stakeholders to make a choice. The choice empowers OEMs and power plant developers with enhanced flexibility in techno-commercial decision making. This application is being used by Hitachi Mitsubishi Hydro, Japan. A typical optimization run intelligently scans approximately 100,000 solutions in 6-7 hours. The methodology has potential for new insights and solutions for widespread applications in machinery and system design.
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  Gupreet Kainth

  Principal Design Engineer

  Cyient, Inc.

  

  Is Principal Design Engineer at Cyient Limited, Hyderabad in Turbo Machinery Group. He received a Bachelor of Technology Degree in Mechanical Engineering from IIT Delhi in the year 2001. He has around 18 years of engineering design & management experience in Green Field & Brown Field projects while working with OEMs like Hitachi Mitsubishi Hydro, Japan; Voith Hydro, Austria; Alstom Power, India & BHEL, India with particular focus on Hydro Generators. Apart from core mechanical engineering experiences, he is an Optimization, Artificial Intelligence & Machine Learning enthusiast.
• Leslie Rittenberg, Refinery & Process Manufacturing Product Manager, Beyond Limits
  Machine Learning and AutoML

  Machine Learning and AutoML

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  Leslie Rittenberg

  Refinery & Process Manufacturing Product Manager

  Beyond Limits

  

  Leslie Rittenberg is the Refinery & Process Manufacturing Product Manager for Beyond Limits, an industrial-grade artificial intelligence technology company built for demanding sectors including energy and utilities. Leslie has over 30-years of experience in the Power and Oil & Gas industries. Prior to joining Beyond Limits, Leslie held leadership positions with a major O&G company for multiple refining assets as well as their Corporate Downstream Technology Center, where she led the development of their downstream manufacturing digital strategy. Leslie recently joined the Beyond Limits team that brings in a unique brand of Cognitive AI to solve complex business and operational problems in manufacturing settings such as refineries. Beyond Limits’ use cases go beyond conventional AI by incorporating and encoding human business reasoning in combination with live plant data to impact operational frontline decision making. Leslie holds an MBA and a degree in Chemical Engineering.
• Martin Gonzalez, Discipline Leader, Intelligent Operations, BP Oil Co
  Machine Learning and AutoML

  Machine Learning and AutoML

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  Martin Gonzalez

  Discipline Leader, Intelligent Operations

  BP Oil Co

  

  As the Connected Worker Team Leader within bp’s Intelligent Operations team, Marty Gonzalez leads Digital Twin strategic solution development in support of bp’s Production and Operations organization. In his 25-year career with Amoco and bp, Marty has held positions in R&D, refinery operations, process design, capital projects, and refinery commercial optimization. He has been developing data analytics, machine learning, and AI-based applications for bp’s downstream operations since 2016. Marty holds a Bachelor of Science degree in Chemical Engineering from the University of Illinois, and a PhD in Chemical Engineering from the University of Wisconsin.
• Mike Berryman, Director, Technical Services and Solutions, Advantech
  Open Nature of Edge Connectivity & Compute

  Open Nature of Edge Connectivity & Compute

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  Mike Berryman

  Director, Technical Services and Solutions

  Advantech

  

  Mike Berryman is the Director of Technical Services for Advantech IIoT Group in North America. Based in Cincinnati, Ohio, USA, his major responsibility is to promote, consult, architect, and deliver on IIoT and AI technologies for clients from various industries. In prior roles over his 20 years with Advantech IIoT, Mike directed the Smart Manufacturing sales sector, Product Management department, and Application Engineering team. He holds a degree in Chemical Engineering from the University of Cincinnati and has over 30 years of experience in the fields of process control, batch automation, discrete manufacturing, and IoT solutions.