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At the end of this course you will learn how to:
Mentor others and propagate Aspen DMC3 competence throughout the company
This is the “Online” version of our popular AEI101 Aspen Enterprise Insights Foundations class. This Online version is delivered through a combination of self-paced eLearning that you can complete within 2 weeks with live, online expert-led sessions, hands-on workshops and interactive online discussion boards. Students usually spend 8 to 12 hours per week to complete this online course.Upon successful completion of this course, you will be able to:
Dynamic Optimization is a new area of economic opportunity in Production Optimization. GDOT improves overall site operating margins by closing the loop between planning/economics objectives and actual operations of units through real time coordination of several APC applications across a broad envelope
In this class, learn how to define applications and estimate benefits, align with planning and scheduling work processes, online monitoring capabilities, and best practices for sustaining benefits. Class includes hands-on exercises on deploying GDOT templates.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Process industries around the world are losing billions of dollars due to unplanned downtime and off-spec production.Distillation column failures are one of the most common causes for these issues.In this course, students will learn to predict and prevent column failures like flooding to avoid situations like processunit shutdown.
Despite spending millions on preventive maintenance and risk-based inspection techniques, unplanned process disruptions and asset downtime continue to plague every company’s process optimization efforts.Industry studies have shown that up to 82% of all failures cannot be avoid using these techniques since they are considered “random”, meaning they don’t follow a traditional time-based wear or usage failure pattern. The result is that these “random” failures are costing the global process industry $20 Billion annually.What makes solving this unplanned disruptions and downtime problem so challenging is the dynamic nature of production process. With thousands of variations occurring simultaneously within the process it is nearly impossible to predict exactly what patterns or trends will lead to unplanned events, impacting asset performance.Combining deep process design expertise with real-time big data and machine learning Aspen APM continually monitors and optimizes asset performance at the equipment, process and plant levels. It uses a suite of integrated risk, process and performance analytics solutions to predict when and why future disruptions and downtimes will occur and to prescribe responses to proactively avoid issue from occurring.
Billions of dollars are lost each year to equipment failure, unplanned downtime, and production disruption. The challenge is to maintain optimal reliability and mitigate risks while achieving target production levels. Understanding the costs and benefits of making such changes drives the decision-making process.Would you be interested in knowing about a software tool that can help you predict the reliability, availability and maintainability of your units and help you maximize overall production or determine economic impact of a potential failure? In this course, you will learn how you can use Aspen Fidelis Reliability to generate accurate predictions of future performance and monitor key indicators such as availability, utilization, system shutdowns, logistics, and resource usage.
This course will help you prepare for the certification exam and the exam fee is waived with this course.The Aspen Mtell application is a condition monitoring solution that uses automated Machine Learning to stop machines breaking down, makes them last longer, reduces maintenance costs, and increases the net product output of any process.In this training, learn the Aspen Mtell easy implementation methodology so you can automatically create many Machine Learning Agents for a single asset with one-click. Practice automatic Agent tuning for the best accuracy and earliest detection of excursions from normal behavior, and the precise patterns that match degrading conditions that lead to failure
This is the “Online” version of our popular APR101 Aspen Mtell Foundations class. This Online version is delivered through a combination of self-paced eLearning that you can complete over 3 weeks with live, online expert-led sessions, hands-on workshops and interactive online discussion boards. Students usually spend 8 to 12 hours per week to complete this online course. Upon successful completion of this course, you will be able to:
This course will focus on using Mtell Agent Builder to create Machine Learning models (ML “Agents”) to capture early warning signatures from the time series sensor data and then easily deploy these Agents in a production manner to provide prescriptive guidance for preventing unplanned downtime. You will learn how to leverage your existing investments in Enterprise Asset Management (EAM) and Historian (e.g. Aspen InfoPlus.21) software by combining and analyzing data from these systems to derive additional value and generate improved business outcomes.
This course will help you prepare for the certification exam and the exam fee is waived with this course.Manufacturers are collecting more data than ever but face the challenge of deriving useful information from these large data sets. Part of the challenge is understanding the relationships amongst all the available variables (raw materials, process conditions etc.) and how these relationships impact process performance (yield, final quality etc.). Aspen ProMV finds the independent driving forces affecting process performance and effectively extracts actionable insights from available historical data. Learn how to use this desktop software on your historical process data to improve understanding of key process relationships.
This is the “Online” version of our popular PMV101 Aspen ProMV Foundations class. This Online version is delivered through a combination of self-paced eLearning that you can complete over 2 weeks with live, online expert-led sessions, hands-on workshops and interactive online discussion boards. Students usually spend 8 to 12 hours per week to complete this online course.Upon successful completion of this course, you will be able to:
This course will help you prepare for the certification exam and the exam fee is waived with this course.This course will teach you how to get actionable insights from your industrial batch data and to use that information for process optimization and troubleshooting. You will learn how to relate time-varying process data, raw material properties and initial conditions to final product quality and productivity. Using this model you can then troubleshoot and optimize your batch processes.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Learn how to extract the most information from your spectral data with the Unscrambler application
Learn the basics of multivariate analysis while getting familiar with the Unscrambler application.
Learn about data analysis strategies for batch process monitoring and control.
Learn how to build models to predict groups or categories while getting familiar with Unscrambler.
Learn about key aspects of compliance and quality assurance in regulated MVA applications in process analytical technology (PAT) for analyzing and monitoring manufacturing processes.
Learn how to perform explorative, multivariate analysis of HSI data in the spectral domain, including calibration model development and its application for quality control.
Get an introduction to strategies for ensuring quality and safety with real-time process analysis.
Reduce capital and energy costs while ensuring consistent particle size distribution by using rigorous solids-handling equipment models integrated with fluid simulation. In this course we will explain the fundamentals of batch process modeling including simulation of batch reactors, crystallization equipment and distillation.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Reduce capital and energy costs while ensuring consistent equipment design and optimize network considering energy savings and capital cost trade off. This course will highlight examples of how Concurrent Conceptual Engineering can help achieve better design while reducing capital and operating costs. Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Drive down costs through project design reuse improving project agility. In this course will explain how to integrate FEED management with design, safety and costing, summarize how to have Automated Change Management and create Optimal Diagrams and Datasheets.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
A systematic model-based estimating process that produces consistent and fast estimates. In this course you will identify and develop best practices for the engineering and procurement strategy with the goal of optimizing the project cost while keeping the project on schedule.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Realize Aspen Capital Cost Estimator (ACCE) benefits by taking training and working with an AspenTech expert who will guide you through the best-practice workflows to:
Drive refinery profit through the optimization of specific applications using rigorous models of distillation columns, heat exchangers and reactor models can assist in troubleshooting and performing engineering studies. In this course we will explain how to navigate and optimize process simulations using Aspen HYSYS and Aspen HYSYS Petroleum Refining.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
The course will demonstrate how to use some of the latest features in Aspen HYSYS and Aspen HYSYS Upstream to optimize the flow from the wellhead all the way through to a gas processing facility including subsea equipment modeling such as wet gas compression.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
Use the capabilities of Aspen HYSYS Upstream to enable shared workflow for Liquefied Natural Gas plant modeling. Build a composition based flowsheet. Utilize several analysis tools for improving overall performance. Implement different unit used to customize flowsheet
Build, navigate and optimize a steady state simulation of an Air Separation Unit (ASU) using Aspen HYSYS. Utilize a wide variety of unit operation models and calculation tools to model process equipment. Use templates and sub-flowsheets to streamline and organize the different sections of the plant.
该课程为 Aspen Plus 的基础课程,面向参加 “2020年大学生化工竞赛” 的在校学生和指导教师。课程的目的是使学生了解 Aspen Plus 基本建模和流程优化的方法。该课程将以网络虚拟课堂的形式进行。课程时间表
Catering to both new and experienced users of Aspen Multi-Blend Optimizer, this course will review the importance of blending in refinery operations and the "day in the life" of the blender. Moving to advanced topics, the course will explain strategies for working with infeasibilities, how to connect with the blend controller interface (BCI), and how to leverage non-linear blending with the Aspen Blend Model Library (ABML).Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
As the world's demand for fuels shifts and slows due to various factors such as more fuel efficient and electric vehicles, refiners globally are moving toward a more diversified product mix with focus on chemicals. The operation of integrated refinery-chemical complexes brings significant synergies but also adds complexity due to the extended scope and multiple interactions. This training session will focus on capturing the integration benefits from the planning perspective, covering subjects such as connection streams, joint decision variables and the integrated model structure. Key topics include approaches for building accurate petrochemicals models and their impact on the characterization of the refinery streams exchanged and the modelling of crude distillation and refinery reactors.Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access PassTo register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true
This course will help you prepare for the certification exam and the exam fee is waived with this course.To provide an intensive problem-solving workshop designed for the experienced Aspen PIMS user. Detailed emphasis is placed on reviewing Aspen PIMS modeling techniques and troubleshooting errors and warnings in models.