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    Dami Somoye

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    dami
    This document discusses applications of model predictive control (MPC) in ethylene oxidation processes. It provides background on ethylene oxidation, different types of controllers, and an introduction to MPC. The document presents two case studies applying nonlinear and linear MPC to control an ethylene oxidation plant. It compares the results and costs/benefits of nonlinear versus linear MPC, finding that linear MPC can achieve similar results if the process is sufficiently linear. Computation is identified as a potential bottleneck to real-time MPC implementation. The document concludes that MPC uses repeated optimization of a system model to determine an optimal trajectory while ensuring feasibility, stability, and robustness.

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    Dami Somoye

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    56 views26 pages
    This document discusses applications of model predictive control (MPC) in ethylene oxidation processes. It provides background on ethylene oxidation, different types of controllers, and an introduction to MPC. The document presents two case studies applying nonlinear and linear MPC to control an ethylene oxidation plant. It compares the results and costs/benefits of nonlinear versus linear MPC, finding that linear MPC can achieve similar results if the process is sufficiently linear. Computation is identified as a potential bottleneck to real-time MPC implementation. The document concludes that MPC uses repeated optimization of a system model to determine an optimal trajectory while ensuring feasibility, stability, and robustness.

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    This document discusses applications of model predictive control (MPC) in ethylene oxidation processes. It provides background on ethylene oxidation, different types of controllers, and an introduction to MPC. The document presents two case studies applying nonlinear and linear MPC to control an ethylene oxidation plant. It compares the results and costs/benefits of nonlinear versus linear MPC, finding that linear MPC can achieve similar results if the process is sufficiently linear. Computation is identified as a potential bottleneck to real-time MPC implementation. The document concludes that MPC uses repeated optimization of a system model to determine an optimal trajectory while ensuring feasibility, stability, and robustness.

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    56 views26 pages

    Dami Somoye

    Uploaded by

    dami
    This document discusses applications of model predictive control (MPC) in ethylene oxidation processes. It provides background on ethylene oxidation, different types of controllers, and an introduction to MPC. The document presents two case studies applying nonlinear and linear MPC to control an ethylene oxidation plant. It compares the results and costs/benefits of nonlinear versus linear MPC, finding that linear MPC can achieve similar results if the process is sufficiently linear. Computation is identified as a potential bottleneck to real-time MPC implementation. The document concludes that MPC uses repeated optimization of a system model to determine an optimal trajectory while ensuring feasibility, stability, and robustness.

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 26

    MPC

    application
    in
    Ethylene
    Oxidation
    DAMI SOMOYE
    Table of contents
    •Introduction to Ethylene Oxidation

    •Background/crash course on controllers

    •Case studies: MPC applications in Ethylene oxidation

    •Compare linear vs nonlinear MPC

    •Conclusion
    INTRODUCTION
    Ethylene Oxidation
    • Ethylene reacts with partial oxygen

    • Produces Ethylene oxide and side products

    Figure 1: Ethylene Oxidation process


    Source:

    https://pubs.acs.org/doi/full/10.1021/acscatal.9
    b03443
    Applications of Ethylene Oxide
    • Used to manufacture
    (i) Antifreeze
    (ii) Pharmaceuticals
    (iii) Detergents
    (iv) Plastics

    • Low temperature sterilizer

    Figure 2: Ethylene oxidation plant


    Source:
    https://www.phxequip.com/plant.82/et
    hylene-plant-36-000-tpy.aspx
    BACKGROUND
    Feedback/closed loop controller
    Types of controllers:

    • Classical

    • Predictive

    • Repetitive

    Figure 3: Diagram of controller


    Source: [1]
    PID vs Predictive vs Repetitive

    PID controller Predictive controller Repetitive controller


    • React based on past and • Use system model to predict • Use previous cycle to predict
    current behaviour future behaviour next cycle

    Source:

    Figure 4: PID vs Predictive vs Repetitive https://en.wikipedia.org/wiki/PID_controller

    https://www.researchgate.net/figure/Model-Predictive-control-structure-of-the-measured-or-estimated-from-the-available_fig1_305670113

    https://www.researchgate.net/figure/The-basic-scheme-of-the-repetitive-control_fig3_228994917
    Introduction to MPC
    MPC features:

    Model
    +
    Optimization

    = > Future
    behaviour
    Figure 5: MPC based control loop Advantage: Disadvantage:
    Source: [1] No need to develop explicit control law Computationally expensive
    Theory

    Figure 6: Function principle of a model-based predictive with horizons N1, N2, Nu


    Source: [1]
    Feasibility
    &
    Stability
    &
    Robustness
    Case Studies
    Case study:
    Nonlinear and Gain-Scheduled MPC
    Control of an Ethylene Oxidation
    Plant
    Objective
    •Analyze the effect of using Nonlinear MPC in controlling the plant

    •Analyze the effect of using linear MPC in controlling the plant

    •Compare results of both linear and nonlinear MPC to arrive at conclusion


    Achieving objective
    using NMPC
    Methodology-NMPC
    Figure 7: Equations describing reaction
    Source: [1]
    Methodology contd

    Figure 8: Important parameters and considerations


    Source: [2]
    Results-NMPC

    Fig 9: The Nonlinear MPC Controller block in the model


    Source: [2]
    Achieving objective
    using linear MPC
    Methodology-MPC
    Results-MPC

    Fig 10: The linear MPC Controller block in the model


    Source: [2]
    “Don’t use a NMPC if you can
    achieve the same results with a
    linear MPC.”
    NMPC vs linear MPC
    costs vs benefits
    •Most industrial processes are nonlinear but still manage to use linear MPC

    •This is possible using Linear or Quadratic programming

    •However this can sometimes not give us the results we desire

    •Hence we might have to result in using NMPC


    Is the computation of the
    controller in real-time a
    bottleneck to implementation
    ?
    QUESTION
    Conclusion
    •Controller types:
    • (i) Classical
    • (ii) Predictive
    • (iii) Repetitive

    •MPC use repeated real-time optimization of the system model to find optimal trajectory

    •Important factors:
    • (i) Feasibility
    • (ii) Stability
    • (iii) Robustness

    •We can use linear MPC if the results closely mirror the results of nonlinear MPC
    References
    [1] M. Schwenzer, M. Ay, T. Bergs, and D. Abel, “Review on Model predictive control: An engineering
    perspective,” The International Journal of Advanced Manufacturing Technology, vol. 117, no. 5-6, pp.
    1327–1349, 2021.

    [2] Mathworks, “Nonlinear and Gain-Scheduled MPC Control of an Ethylene Oxidation Plant,”
    Computers and Chemical Engineering, vol. 92. pp. 18–36, 2016.

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