Research

Research is directed towards process modelling and control as well as industrial applications of artificial intelligence and advanced data analysis covering a large area from paper and metallurgical industries to biotechnology and also mineral processing and pharmaceutical manufacturing. Applications consist of:

  • soft sensors
  • advanced control, diagnostics and condition monitoring (lime kiln, pulp cooking, bleaching, TMP-refiner, paper machine, blast furnace, converter plant, continuous casting, solar power plant, waste water processes, bioprocesses, fluidised bed granulator)
  • wireless systems
  • modelling and control in energy production of different scales (bioenergy, hydrogen energy, solar energy, small-scale boilers and furnaces)

Publications starting from year 2000.

Most recent publications: 

Lindblad, J., Routa, J., Ruotsalainen, J., Kolströn, M., Isokangas, A. & Sikanen, L. (2018) Weather based moisture content modelling of harvesting residues in the stand. Silva Fennica, Vol. 52(2), 16 p. doi: 10.14214/sf.7830

Juuso, E. (2018). An advanced teaching scheme for integrating problem-based learning in control education. Open Engineering, 8(1), pp. 41-49. doi:10.1515/eng-2018-0006

Sorsa, A., Santa-aho, S., Wartiainen, J., Suominen, L., Vippola, M. & Leiviskä, K. (2018) Effect of Shot Peening Parameters to Residual Stress Profiles and Barkhausen Noise. Journal of Nondestructive Evaluation, Vol. 37(10), 11 p. doi: 10.1007/s10921-018-0463-7

Tomperi, J. & Leiviskä, K. (2018) Comparison of modelling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality. Environmental Technology (United Kingdom), Vol.39(11), pp. 1442-1449. doi: 10.1080/09593330.2017.1331267

Tomperi, J. & Leiviskä, K. (2018) Monitoring a municipal wastewater treatment process using a trend analysis. Environmental Technology (United Kingdom), (in press). doi: 10.1080/09593330.2017.1375026

Tomperi, J., Koivuranta, E., Kuokkanen, A. & Leiviskä, K. (2017) Modelling effluent quality based on a real-time optical monitoring of the wastewater treatment process. Environmental Technology (United Kingdom), 38(1), p. 1-13. doi: 10.1080/09593330.2016.1181674

Tomperi, J., Koivuranta, E. & Leiviskä, K. (2017) Predicting the effluent quality of an industrial wastewater treatment plant by way of optical monitoring. Journal of Water Process Engineering, 16, p. 283-289. doi: 10.1016/j.jwpe.2017.02.004

Juuso, E. (2017) Intelligent performance analysis with a natural language interface. Management Systems in Production Engineering, 25(3), p. 168-175- doi: 10.1515/mspe-2017-0025.

Tomperi, J., Piippo, S., Aikio, O., Luoma, T., Leiviskä, K. & Pongrácz, E. (2017) Sustainable waste management in Northern rural areas: Local utilisation of bio-wastes. International Journal of Energy and Environment (IJEE), 8(5), p. 365-374.

Nikula, R., Karioja, K., Leiviskä, K. & Juuso, E. (2017) Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties. Journal of Intelligent Manufacturing, (in press), doi: 10.1007/s10845-017-1341-3

 

We have also two older report series (which are currently on hold): Series A is in English and Series B in Finnish.

 

 

Last updated: 20.6.2018