Hybrid linear-neural network process control

• US 5,877,954 A
• Filed: 05/03/1996
• Issued: 03/02/1999
• Est. Priority Date: 05/03/1996
• Status: Expired due to Fees

• Current Assignee: Aspentech Corporation
• Original Assignee: Aspen Technology (Telefonaktiebolaget LM Ericsson)
• Inventors: Guiver, John P., Klimasauskas, Casimir C.
• Primary Examiner(s): Hafiz, Tariq R.
• Assistant Examiner(s): Rhodes, Jason W.
• Application Number:
  US08/642,775
• Time in Patent Office: 1,033 Days
• Field of Search: 395/23, 395/22, 395/21, 364/148, 364/149, 364/148.01, 364/148.02, 364/148.03, 364/150, 364/151, 706/15, 706/16, 706/23, 706/25, 706/26, 706/17
• US Class Current: 700/29
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence

Abstract

A hybrid analyzer having a data derived primary analyzer and an error correction analyzer connected in parallel is disclosed. The primary analyzer, preferably a data derived linear model such as a partial least squares model, is trained using training data to generate major predictions of defined output variables. The error correction analyzer, preferably a neural network model is trained to capture the residuals between the primary analyzer outputs and the target process variables. The residuals generated by the error correction analyzer is summed with the output of the primary analyzer to compensate for the error residuals of the primary analyzer to arrive at a more accurate overall model of the target process. Additionally, an adaptive filter can be applied to the output of the primary analyzer to further capture the process dynamics. The data derived hybrid analyzer provides a readily adaptable framework to build the process model without requiring up-front knowledge. Additionally, the primary analyzer, which incorporates the PLS model, is well accepted by process control engineers. Further, the hybrid analyzer also addresses the reliability of the process model output over the operating range since the primary analyzer can extrapolate data in a predictable way beyond the data used to train the model. Together, the primary and the error correction analyzers provide a more accurate hybrid process analyzer which mitigates the disadvantages, and enhances the advantages, of each modeling methodology when used alone.

Claims

1. An apparatus for modeling a process, said process having one or more disturbance variables as process input conditions, one or more corresponding manipulated variables as process control conditions, and one or more corresponding controlled variables as process output conditions, said apparatus comprising:

a data derived primary analyzer adapted to sample an input vector spanning one or more of said disturbance variables and manipulated variables, said data derived primary analyzer generating an output based on said input vector;

a derivative calculator for computing a derivative of the output of said primary analyzer;

an integrator coupled to the output of said derivative calculator for generating a predicted value;

an error correction analyzer adapted to sample said input vector, said error correction analyzer estimating a residual between said data derived primary analyzer output and said controlled variables; and

an adder coupled to the output of said data derived primary analyzer and said error correction analyzer, said adder summing the output of said primary and error correction analyzers to estimate said controlled variables.

2. The apparatus of claim 1, wherein said data derived primary analyzer and said error correction analyzer sample said input vector continuously.

3. The apparatus of claim 1, wherein said data derived primary analyzer and said error correction analyzer sample said input vector using predetermined delay periods.

4. The apparatus of claim 3, wherein said delay period is determined using an adaptive process.

5. The apparatus of claim 3, wherein said delay period is user selectable.

6. The apparatus of claim 1, wherein said disturbance and manipulated variables are latent variables.

7. The apparatus of claim 1, wherein said data derived primary analyzer is a linear model.

8. The apparatus of claim 7, wherein said linear model is a Partial Least Squares (PLS) model.

9. The apparatus of claim 8, further comprising a filter coupled to the output of said data derived primary analyzer, said filter receiving said output vector and providing a filtered vector as an output.

10. The apparatus of claim 9, wherein said filter is adaptive.

11. The apparatus of claim 9, wherein said filter is a Kalman filter adapted to receive said controlled variables.

12. The apparatus of claim 8, wherein said PLS model further comprises a spline generator for mapping said input vector to said primary analyzer output.

13. The apparatus of claim 8, wherein said error correction analyzer is a neural network.

14. The apparatus of claim 13, wherein said neural network further comprises:

a derivative calculator for computing a derivative of the output of said primary analyzer; and

an integrator coupled to the output of said derivative calculator for generating a predicted value suitable for correcting the output of said data derived primary analyzer.

15. The apparatus of claim 13, further comprising a filter coupled to the input of said data derived primary analyzer, said filter receiving said input vector and providing a filtered vector for capturing the dynamics of the process to the input of said neural network.

16. The apparatus of claim 8, wherein said error correction analyzer is a neural network partial least squares model.

17. The apparatus of claim 1, further comprising:

a distributed control system coupled to the output of said adder; and

a run-time delay and variable selector coupled to the output of said distributed control system, said run-time delay and variable selector generating said input vector.

18. The apparatus of claim 17, wherein said run-time delay and variable selector are adapted to receive delay and variable settings, wherein said data derived primary analyzer and said error correction analyzer are adapted to receive model parameters, said apparatus further comprising:

a data repository for storing historical values of said disturbance variables, said manipulated variables and said controlled variables;

a development delay and variable selector coupled to said data repository for selecting and time-shifting one or more of said disturbance variables, said manipulated variables and said controlled variables, said development delay and variable selector generating said delay and variable settings;

a hybrid development analyzer coupled to said development delay and variable selector, said hybrid development analyzer generating said model parameters.

19. The apparatus of claim 17, wherein said hybrid development analyzer further comprises:

a development primary analyzer coupled to said data repository, said development primary analyzer adapted to sample a development input vector spanning one or more of said disturbance variables and manipulated variables, said development primary analyzer adapted to sample one or more controlled variables, said development primary analyzer generating an output based on said input vector;

a subtractor coupled to said data repository and to said development primary analyzer, said subtractor adapted to receive one or more controlled variables from said data repository, said subtractor generating a primary model error output;

a development error correction analyzer coupled to said data repository and said development primary analyzer error output, said development error correction analyzer adapted to sample said development input vector, said development error correction analyzer estimating a residual between said development primary analyzer output and said controlled variables; and

an adder coupled to the output of said development primary analyzer and said development error correction analyzer, said adder summing the output of said primary and error correction analyzers to estimate said controlled variables.

20. An apparatus for modeling a process, said process having one or more disturbance variables as process input conditions, one or more corresponding manipulated variables as process control conditions, and one or more corresponding controlled variables as process output conditions, said apparatus comprising:

a Partial Least Squares model data derived primary analyzer adapted to sample an input vector spanning one or more of said disturbance variables and manipulated variables, said data derived primary analyzer generating an output based on said input vector, said PLS model comprising a spline generator for mapping said input vector to said primary analyzer output;

an error correction analyzer adapted to sample said input vector, said error correction analyzer estimating a residual between said data derived primary analyzer output and said controlled variables; and

an adder coupled to the output of said data derived primary analyzer and said error correction analyzer, said adder summing the output of said primary and error correction analyzers to estimate said controlled variables.