Abstract: A method is disclosed for collecting and processing raw process data. The method includes processing the raw data through a process model to obtain a prediction of the process quality; processing this prediction through two dynamic transfer functions thus creating two intermediate signals; storing the two intermediate signals as a function of time; retrieving at the time of a real and validated measurement of the process quality from the history the absolute minimum value and maximum value of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, in which the absolute minimum value and maximum values define the minimum and maximum prediction possible; calculating the deviation as being the difference between the real and validated measurement and the uncertainty area encompassed between the minimum and maximum prediction possible; incorporating the deviation into the process model to calibrate the process model; and, repeating.

Abstract: A process to continuously prepare a hydrocarbon product having a specified desired sulphur content lower than 0.05 wt %, starting from at least two or more high-sulphur hydrocarbon feedstocks having a sulphur content of above 0.

Abstract: A process to continuously prepare a hydrocarbon product having a specified desired sulphur content lower than 0.05 wt %, starting from at least two or more high-sulphur hydrocarbon feedstocks having a sulphur content of above 0.

Abstract: Method for automatic on-line calibration of process models for real-time prediction of process quality from raw process measurements by collecting raw process data, processing data collected through a mathematical model to obtain a prediction of the quality, processing this prediction through two independent dynamic transfers functions thus creating two intermediate signals, storing the two intermediate signals obtained as a function of time in history, retrieving, at the time of a real and validated measurement of the quality, from the history the absolute minimum and maximum values of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, which values define the minimum and maximum prediction possible, calculating the deviation as being the difference between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained.

Abstract: Method for automatic on-line calibration of process models for real-time prediction of process quality from raw process measurements by collecting raw process data, processing data collected through a mathematical model to obtain a prediction of the quality, processing this prediction through two independent dynamic transfer functions thus creating two intermediate signals, storing the two intermediate signals obtained as a function of time in history, retrieving, at the time of a real and validated measurement of the quality, from the history the absolute minimum and maximum values of the two intermediate signals in the time period corresponding to a minimum and maximum specified deadtime, which values define the minimum and maximum prediction possible, calculating the deviation as being the difference between the real and validated measurement and the area encompassed between the minimum and maximum prediction possible as obtained, and repeating these steps if the absolute value of the deviation obtained is