Methods for methanol-to-gasoline conversion with post-processing of heavy gasoline hydrocarbons

    公开(公告)号:US11603340B2

    公开(公告)日:2023-03-14

    申请号:US17014991

    申请日:2020-09-08

    Abstract: Methanol-to-gasoline conversion may be performed using a heavy gasoline treatment, followed by a separation operation. Methanol may be converted into a first product mixture comprising dimethyl ether (DME) under DME formation conditions. In a methanol-to-gasoline (MTG) reactor, the first product mixture may be converted under MTG conversion conditions to produce a second product mixture comprising light gasoline hydrocarbons and untreated heavy gasoline hydrocarbons. The untreated heavy gasoline hydrocarbons may be separated from the light gasoline hydrocarbons and transferred to a heavy gasoline treatment (HGT) reactor. The untreated heavy gasoline hydrocarbons may be catalytically reacted in the HGT reactor to form a third product mixture. A heavy hydrocarbon fraction may be separated from the third product mixture. The heavy hydrocarbon fraction includes heavy gasoline hydrocarbons having a lower boiling endpoint than does the untreated heavy gasoline hydrocarbons.

    Intelligent system for identifying sensor drift

    公开(公告)号:US11415438B2

    公开(公告)日:2022-08-16

    申请号:US16930786

    申请日:2020-07-16

    Abstract: A method for identifying sensor drift can include: setting an autocorrelation threshold for a sensor in a long-short term memory (LSTM) model developed based on historical process measurements from an analogous sensor to a sensor; collecting measured data from the sensor; applying the LSTM model to the measured data from the sensor, wherein applying the LSTM model comprises: applying the LSTM model to the measured data from the sensor to yield LSTM predicted data; calculating key performance indicators (KPIs) of the LSTM data based on an accumulated slow drift error (ASDE) model, wherein the KPIs comprise an error, an accumulated prediction error, an accumulated slow-drift error, and an estimated autocorrelation; and identifying sensor drift when the estimated autocorrelation violates the autocorrelation threshold.

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