CA2404968A1

CA2404968A1 - Method and system for monitoring and analyzing a paper manufacturing process

Info

Publication number: CA2404968A1
Application number: CA002404968A
Authority: CA
Inventors: Sakari Laitinen-Vellonen
Original assignee: Individual
Current assignee: Liqum Paper Oy
Priority date: 2000-04-05
Filing date: 2001-04-04
Publication date: 2001-10-11
Anticipated expiration: 2021-04-04
Also published as: WO2001075222A2; AU2001252298A1; ES2228845T3; DE60105421D1; DE60105421T2; EP1290507B1; CA2404968C; ATE275740T1; PT1290507E; US6792388B2; EP1290507A2; WO2001075222A3; US20030139904A1

Abstract

The invention concerns a method for monitoring and analyzing a paper manufacturing process, in which method a large number of quantities (xi, t) are measured from the process, the measured quantities (xi, t) are entered as an input vector (X, t) into a neural network, which, in response to the input vector, produces an output vector (Y, t) as a continuous quantity, at least one fingerprint consistent with a good process situation in regard of runnability, i.e. an optimal output vector (Yo, t) is determined and stored in memory, the stored fingerprints and fingerprints or output vectors obtained in a normal process situation are compared substantially in real time, based on the comparison, a difference to be presented in a graphic form to the user is determined. According to the invention, a continuous time-dependent scalar quantity k=k(t) to be presented as a result to the user is determined as a geometric distance between the instantaneous measured fingerprint (Y, t) and the taught fingerprint (Yo), and the scalar quantity k=k(t) is displayed via a display device to the user.

Claims

1. Method for monitoring and analyzing a paper manufacturing process, in which method:

a large number of quantities (xi,t) are measured from the process, the measured quantities (xi,t) are entered as an input vector (X, t) into a neural network, which, in response to the input vector, produces an output vector (Y,t) as a continuous quantity, at least one fingerprint consistent with a good process situation in regard of runnability, i.e. an optimal output vector (Yo,t) is determined and stored in memory, the stored fingerprints and the fingerprints or output vectors obtained in a normal process situation are compared substantially in real time, based on the comparison, a difference to be pre-sented in a graphic form to the user is determined, characterized in that:

a continuous time-dependent quantity k=k(t) to be presented as a result to the user is determined as a geomet-ric distance between the instantaneous measured fingerprint (Y,t) and the taught fingerprint (Yo), and the aforesaid quantity k=k(t) for the period of a selected trend T is displayed via a display device to the user.

2. Method as defined in claim 1, characterized in that a multi-layer perception network (MLP) is selected as the neural network to be used.

3. Method as defined in claim 1 or 2, characterized in that a polar conversion k(~,t) to be presented in a graphic form is generated from the said quantity k=k(t) as follows:
cp(t)= t/T * 2n, where T is the selected trend r (t)=k.

4. Method as defined in claim 3, characterized in that a set of history values is determined from the measured quantities (xi, tn) over a period of time corresponding to a multiple of the trend, by periodically saving the dimensionless value (yi,tn) of each input quantity, which is stored and coded so as to produce brightness/color information, said values being displayed graphically in succession as a bar (13') in which each value (yi, tn) is visible as a transverse line or similar area of corresponding brightness/color.

5. Method as defined in claim 4, characterized in that both a graph of the polar conversion k(cp,t) and the history bars (13) are presented to the user at the same time, especially via the same display.

6. Method as defined in any one of the preceding claims 3 -5, characterized in that the information presented to the user on the same display additionally comprises the measured quantities and the good fingerprint as sets of values con-verted into a dimensionless and graphic form, e.g. into sequences of bars (11, 12).

7. Method as defined in any one of the preceding claims 2 6, characterized in that a set of difference quantities (Yo Y) together with a sequence of times is presented to the user in a graphic form.

8. Method as defined in any one of the preceding claims 1 -7, characterized in that the dimension of the output vector of the neural network is in the range of 3 - 10, preferably equaling the number of categories to be distinguished.

9. System for monitoring and analyzing the paper production process in a paper machine (1), said system comprising a neural network (2) having inputs for the entering of a large number of quantities (xi,t) measured from the process and outputs for the delivery of a response, and computing means (3) for the processing of the response, and storage means (4) for the storage of a fingerprint consistent with a good process situation, i.e. an optimal output vector (Yo,t), means for comparing substantially in real time the stored fingerprints and the fingerprints or output vectors obtained in a normal process situation and for determining on the basis of the comparison a difference to be presented to the user, a display device (5) for displaying the difference in a graphic form, characterized in that said neural network (2) has been adapted to implement a multi-layer perception network (MLP), and the computing means (3) have been adapted to deter-mine from the output vector a continuous time-dependent scalar quantity k=k(t) as a geometric distance between the instantaneous measured fingerprint (Y, t) and the taught fingerprint (Yo), and the display device (5) has been adapted to display the said scalar quantity k=k(t).