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| ------------ Basics ------------ |
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Fuzzy logic and process control |
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FLOU |
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> Know the basic principles of fuzzy logic.
> Show the control benefits of this tool. |
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| ------------ Fundamentals ------------ |
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Advanced control through practice |
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CA |
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> Acquire a synthetical and comparative vision of advanced control.
> Understand the technical and economical benefit of advanced control over PID control.
> Know the principle of the most used advanced control tools in the industry. |
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Predictive control for chemical reactors |
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REACT |
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> Evaluate the economical impact: quality, quantity, energy consumption, security for reactor's operation.
> Learn different techniques of predictive control adapted to heat exchangers: non linear commands, enthalpic, parametric, exothermicity estimation. |
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Digital control: concept, tools, development |
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RN |
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> Design, with a PC or control system, a performant digital corrector.
This digital corrector can be on a PID base: simple loops, multi-loops or model based controller.
> Implement these control loops on a control system.
> Use the basics of digital control to implement digital identification tools. |
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| ------------ Mastery ------------ |
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Fuzzy logic and process control - Diagnosis and modeling by neural networks |
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FLOU + NEURONE |
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> Know the basic principles of fuzzy logic.
> Show the control benefits of this tool.
> Show the benefits of neural networks for modeling and diagnosis of non-linear processes.
> Present, on industrial application examples, the advantages of this approach and the implementation strategies.
> Demystify techniques which appear complex in industrial environments. |
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Diagnosis and modeling by neural networks |
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NEURONES |
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> Show the benefits of neural networks for modeling and diagnosis of non-linear processes.
> Present, on industrial application examples, the advantages of this approach and the implementation strategies.
> Demystify techniques which appear complex in industrial environments. |
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Modeling and Predictive Control |
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PCR |
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> Apprehend the benefit and techniques of industrial processes modeling.
> To be able to implement test procedures and process identifications.
> Understand the fundamental principles of predictive control and its implementation.
> Know the pros and cons of predictive control compared to classic control. |
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How to boost PID control by modeling processes physically |
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PID++ |
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> Extend the PID control domain to non linear processes or large deadtimes by integrating process measurements and by capitalizing on physical laws that rule the process behaviours.
> Learn to setup the tuning parameters of obtained multi-loop controls. |
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Multivariable process control |
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RPM |
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> Acquire a methodology to analyse interactive control loops problems.
> Learn and measure the benefits of model predictive control applied to multivariable processes. |
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Robust digital command by pole assignment |
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RST |
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> Show the benefits of the RST corrector compared to the PID controller:
- Independent management of the pursuit of the setpoint and disturbance corrections.
- Adaptation to high-order or deadtime processes.
- Robust control following variations of process operating conditions.
> Propose a practical setup method on digital control systems. |
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