System and method for improving model product property estimates

A computer-based system for processing dynamic model generated stream composition and/or product property estimates, comprising:

• (a) two or more sensors for producing raw pressure and temperature signals from two or more streams in a process comprising one or more units and two or more streams;
• (b) one or more computer-based process dynamic models connected to receive limit checked pressure, temperature, and level signals, reconciled flows, feed composition signals, and model alignment factors, to produce predicted pressure and temperature signals and predicted stream composition and/or product property signals;
• (c) a process variable comparison module connected to use said raw pressure and temperature signals and said predicted pressure and temperature signals to determine an error between said raw signals and said predicted signals;
• (d) a statistical filter module connected to take said error and determine a new moving average error from the statistical history of said error;
• (e) an alignment module connected to take said new moving average error and produce one or more model alignment factors and connected to adjust said one or more computer-based process dynamic models based on said one or more model alignment factors;
• (f) a stream composition and/or product property controllers, connected to directly or indirectly use said predicted stream composition and/or product property signals as input, for producing a controller output signal in accordance with said input and a control objective;
• and
• (g) an actuator connected to change one or more states of said process in accordance with said controller output signal.

A computer-based system for processing model generated stream composition and/or product property estimates, comprising:

• two or more sensors for producing raw stream composition and/or product property signals from two or more streams in a process comprising one or more units and two or more streams;
• one or more computer-based process models connected to receive reconciled flows and pressure, temperature, and level signals, said raw stream composition and/or product property signals, and model calibration factors, and produce predicted stream composition and/or product property signals;
• a product property transform module connected to use said reconciled flows, said raw stream composition and/or product property signals, and said predicted stream composition and/or product property signals to produce artificial stream composition and/or product property signals for streams lacking raw stream composition and/or product property signals;
• a product property comparison module connected to use said raw stream composition and/or product property signals, said artificial stream composition and/or product property signals, and said predicted stream composition and/or product property signals to determine an error between said raw signals and said predicted signals;
• a statistical filter module connected to take the said error and determine a new moving average error from the statistical history of said error;
• a calibration module connected to take the said new moving average error and produce model calibration factors for adjusting said process models;
• and
• a product property corrector module connected to take said new moving average error and said predicted stream composition and/or product property signals and apply said new moving average error to said predicted stream composition and/or product property signals to produce a corrected stream composition and/or product property signal.

A computer-based method for processing dynamic model generated stream composition and/or product property estimates, comprising the steps of:

• (1) measuring, in a process comprising one or more units and two or more streams, stream composition and/or product property signals of said streams to produce raw stream composition and/or product property signals;
• (2) comparing said raw stream composition and/or product property signals with previously predicted stream composition and/or product property signals to determine an error measure;
• (3) using said error measure to adjust dynamic process models to reduce future estimates of said error measure;
• (4) predicting, using said dynamic process models, future process stream composition and/or product property signals for one or more of said streams;
• (5) using said predicted future stream composition and/or product property values to directly or indirectly change the state of a control element(s) of said process;
• and
• (6) operating said process using said control element(s) to produce a product.