Posts Tagged ‘model predictive control’

Ethylene Furnace Automation, Control & Optimization

Posted in Chemical, Services, Consulting & Training on Thursday, January 23rd, 2020. No comments yet
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Cracking furnaces are one of the most crucial parts of an ethylene complex. They set the production and yield for the entire plant. For furnace-limited plants, when a furnace is down, whether for maintenance or decoking, production is lost.

Case Study in Carbon Black Process Optimization

Posted in Event, Production, Services, Consulting & Training on Wednesday, September 25th, 2019. No comments yet
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Emerson’s Dr. Tiffany Tang presented Novel Idea of Model Predictive Control on Fast Process! First Application at the 2019 Emerson Exchange conference. Here is Tiffany’s abstract: Carbon black formation reaction is a fast process in the order of seconds. An application of model predictive control (MPC) on such a fast process was recommended. MPC was […]

Advancing Digital Twin Technology

Posted in Digital Transformation, Simulation on Tuesday, August 27th, 2019. No comments yet
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Digital twins provide a simulation of the manufacturing or production process as well as the automation systems which control it. Depending on the level of fidelity of the process models, this digital twin can have many uses. I caught up with Emerson’s Zuwei Jin who shared some of his thoughts around these digital twins. He […]

Improved Steam Temperature Control Strategies

Posted in Control & Safety Systems, Power Generation on Tuesday, May 28th, 2019. No comments yet
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As electrical power producers and cogeneration facilities must handle the load swings produced on the supply and demand side. Optimizing steam temperature control is one key way to improve the reliability of on boilers and heat recovery steam generators (HRSGs) caused by load swings. I caught up with Emerson’s Jeffery Williams who shared the details […]

Applying Embedded Model Predictive Control

Posted in Control & Safety Systems, Services, Consulting & Training on Thursday, April 18th, 2019. No comments yet
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The history of model predictive control (MPC) dates back to the early 1970s invented at Shell Oil and was known as Dynamic Matrix Control. MPC was designed at that time to solve largescale control challenges. As technology advanced, this technology could be more widely applied on smaller-scale challenges. At this past 2019 AIChE Spring Meeting, […]