An example of a data mining project

Problem Detect and explain faults of a continuous pulp digester Faults: drops in the output quality of the digester.

Solution A report which consists of –description of analyzed data, –analysis methods, –results, –conclusions, and –process improvement recommendations.

Problem understanding Several sources of information: –description of process instrumentation, –documentation of digester control system, –ISO 9000 documents, –interviews of operation personnel, process engineers, researchers, and automation system vendor engineers.

Data acquisition About 200 on-line measurements Sampling rate 1 sample/10 minutes Data stored in SQL-database at the mill

Data acquisition Data acquisition procedure –a shell script run in SQL host twice a month –ftp-transfer of the data to HUT through firewall by a mill computer operator –addition of the new data files after the existing ones at HUT using shell scripts

Data acquisition Data file format: value1 checkbits1 timelabel1 value2 checkbits2 timelabel2... valueN checkbitsN timelabelN

Basic data preparation For each measurement channel: –check that the measurements are valid using checkbits –check using timelabel s if some samples are missing; if this is the case, fill in the empty gaps with NaNs

Data survey Visual data inspection (time series plots) revealed some problems: –some measurements didn’t work at all, –some measurements worked properly, but not all the time, –changes in production speed could be seen in most measurements, and –process tuning altered the behavior of some measurements.

Data survey Computation of material balances provides a way to roughly estimate reliability of some sensors Process delay from input to output of the digester about three hours  Delay between different measurements in different parts of the process had to be compensated

Data survey In order to get reliable results, only periods with constant production speed should be analyzed

Data modeling First, only temperature measurements in the digester sides were used Basic idea: to estimate the movement of chips using correlations between neighboring measurements  Failed

Data modeling Next, all available measurements were used The measurements were reduced to the ones best depicting the state of the digester The reduction was carried out using –process knowledge, –data visualization, and –correlation analysis.

Data modeling During the project, a digester modeling expert was consulted A model depicting the fault sensitivity of digester was created