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    Feedback control for reducing flaring process smoke and noise
    3.
    发明授权
    Feedback control for reducing flaring process smoke and noise 有权
    反馈控制可减少火焰烟雾和噪音

    公开(公告)号：US09594359B2

    公开(公告)日：2017-03-14

    申请号：US14252156

    申请日：2014-04-14

    Applicant: HONEYWELL INTERNATIONAL INC.

    Inventor： Mohammed Ibrahim Mohideen ,  Madhukar Madhavamurthy Gundappa ,  Mahesh Gellaboina ,  Viswanath Talasila

    IPC: G05D3/12 ,  G05B13/04 ,  G05B17/02 ,  F23G5/50 ,  F23G7/08

    CPC classification number: G05B13/048 ,  F23G5/50 ,  F23G7/08 ,  F23J2900/11002 ,  F23N2023/40 ,  F23N2023/44 ,  F23N2029/20 ,  G05B17/02

    Abstract: A method of reducing plant emissions includes providing a MPC model for a flaring process including one-to-one models between controlled variables (CVs) including a smoke count and/or a flare count (CV1) and a noise level (CV2), and flow of assist gas as a manipulated variable (MV) and another process gas flow as a disturbance variable (DV). The MPC model receives sensed flare-related parameters during the flaring process including a measure of CV1 (CV1*) and CV2 (CV2*). Provided CV1* is above a minimum setpoint for CV1 (CV1 setpoint) and CV2* is above a setpoint for CV2 (CV2 setpoint), the flaring process is automatically controlled using the MPC model which determines an updated flow setpoint for MV from CV1* and CV2*, the CV1 and CV2 error, and the identified one-to-one models.

    Abstract translation: 一种减少工厂排放的方法包括为包括烟数和/或火炬计数（CV1）和噪声水平（CV2）在内的受控变量（CV）之间的一对一模型提供扩口过程的MPC模型，以及 作为操纵变量（MV）的辅助气体流和作为干扰变量（DV）的另一过程气体流。 MPC模型在燃烧过程中接收到感测到的火炬相关参数，包括CV1（CV1 *）和CV2（CV2 *）的测量。 如果CV1 *高于CV1（CV1设定值）的最小设定值，CV2 *高于CV2（CV2设定值）的设定值，则使用MPC型号自动控制燃烧过程，MPC型号将确定CV的更新流量设定值CV1 * CV2 *，CV1和CV2错误，以及识别的一对一模型。

    APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING FUGITIVE LEAKS
    4.
    发明公开
    APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING FUGITIVE LEAKS 审中-公开

    公开(公告)号：US20240202404A1

    公开(公告)日：2024-06-20

    申请号：US18176780

    申请日：2023-03-01

    Applicant: Honeywell International Inc.

    Inventor： Shahrul Azman Bin Zainal Abidin ,  Madhukar Madhavamurthy Gundappa ,  Ramesh Murugan Natarajan

    IPC: G06F30/27

    CPC classification number: G06F30/27

    Abstract: Methods, apparatuses, and computer program products for predicting fugitive leaks are provided. For example, a computer-implemented method may include receiving current operating conditions data associated with current operation of one or more operational systems and generating, using a fugitive leak prediction model, fugitive leak predictions corresponding to the current operation of the one or more operational systems. The fugitive leak prediction model may be a machine learning model trained based at least in part on historical operating conditions data associated with past operation of the one or more operational systems and historical fugitive emissions data associated with the past operation of the one or more operational systems, and the fugitive leak prediction model may be configured to generate the fugitive leak predictions based at least in part on the current operating conditions data

    APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING METHANE EMISSIONS INTENSITY
    5.
    发明公开
    APPARATUSES, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR PREDICTING METHANE EMISSIONS INTENSITY 审中-公开

    公开(公告)号：US20240202403A1

    公开(公告)日：2024-06-20

    申请号：US18176763

    申请日：2023-03-01

    Applicant: Honeywell International Inc.

    Inventor： Shahrul Azman Bin Zainal Abidin ,  Madhukar Madhavamurthy Gundappa ,  Ramesh Murugan Natarajan

    IPC: G06F30/27

    CPC classification number: G06F30/27

    Abstract: Methods, apparatuses, and computer program products for predicting methane emissions intensity are provided. For example, a computer-implemented method may include receiving projected production parameters and emissions reduction strategy information associated with one or more operational systems for a period of time and generating, using a methane emissions intensity prediction model, methane emissions intensity predictions based on the projected production parameters and the emissions reduction strategy information along with historical operational data and historical emissions data for the one or more operational systems. The methane emissions intensity prediction model may be a machine learning model trained on the historical operational data and historical emissions data and possibly simulated emissions data.

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