A method of screening and selecting data automatically for model identification and model adaptation in a multivariable predictive controller (MPC) , the method comprising: given an online controller having an existing model, in a processor: loading process data from a subject process and storing said process data in a database accessible by said model; characterized by ; using a rule-based data selector, automatically detecting and excluding data segments of the stored process data that are unsuitable for model quality estimation and for model identification; validating the excluded data segments to minimize the data loss from bad data segments being excluded; repairing certain data segments of said stored process data to maximize usage of data in a MPC application; and updating said existing model using resulting process data as stored in the database, wherein said rule-based selector is configured to, screen a given time series variable as a dependent process variable or an independent process variable and apply a basic data screening filter to detect and mark data segments of the time series as good data or bad data according to given data quality measurement parameters; group time series variables according to their associated proportional-integral-derivative PID loop; compare process variables PV against their corresponding set points SP in each PID loop and applying data screening filters; generate predictions for dependent variables without a PID association using available independent variable measurements; evaluate said generated predictions for dependent variables without a PID association against corresponding available dependent variable measurements and applying data screening methods; and identify and generate bad data slices in the given time series variable using the data segments marked as bad data and a data slice generator to exclude said bad data segments from the time series. loading process data from a subject process and storing said process data in a database accessible by said model; characterized by ; using a rule-based data selector, automatically detecting and excluding data segments of the stored process data that are unsuitable for model quality estimation and for model identification; validating the excluded data segments to minimize the data loss from bad data segments being excluded; repairing certain data segments of said stored process data to maximize usage of data in a MPC application; and updating said existing model using resulting process data as stored in the database, wherein said rule-based selector is configured to, screen a given time series variable as a dependent process variable or an independent process variable and apply a basic data screening filter to detect and mark data segments of the time series as good data or bad data according to given data quality measurement parameters; group time series variables according to their associated proportional-integral-derivative PID loop; compare process variables PV against their corresponding set points SP in each PID loop and applying data screening filters; generate predictions for dependent variables without a PID association using available independent variable measurements; evaluate said generated predictions for dependent variables without a PID association against corresponding available dependent variable measurements and applying data screening methods; and identify and generate bad data slices in the given time series variable using the data segments marked as bad data and a data slice generator to exclude said bad data segments from the time series. <EOS>