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A model-based control strategy for the compensation of combustion power fluctuations in small-scale biomass fired boilers is presented. Delay-free heat output model is obtained by utilising combustion temperature measurements. Local principal component regression models are constructed to cope with changing combustion conditions and redundant input variables found by data analysis. The modelling approach was validated using data from 30 kW and 300 kW boilers. Usability of the discussed monitoring method for compensating fuel power disturbances by the primary air control was tested in practice. The results imply that modelling information can be utilised as a part of the control structure, enabling the cost-effective way to reduce emissions and maintain optimal combustion conditions in the distributed energy production with solid biomass fuels.

A novel method for real-time moisture level monitoring of the solid biomass fuel in grate combustion is presented. The measurement principle is based on temperature sensor information from both flame and a fuel bed. Based on the combustion theory and data analysis, selected features have been extracted from the fused sensor information and estimate of the fuel quality is then made continuously with calculated features. The monitoring approach has been tested in a 300 kW stoker combustion unit intended for decentralized heat production, over a wide range of different process conditions and wood fuel moistures, giving satisfactory accuracy for control purposes. The availability of moisture information made possible to adjust primary/secondary air ratio, leading to reduction of emissions and excess air. Based on the results, the method has capability to give new possibilities for cost effective control and more energy efficient use of solid biomass as a fuel in small-scale energy production.

A procedure for surveying process data sets is presented. For this, linear models constructed in varying length, sliding data windows to determine the usefulness of data segments for process identification are utilised. The discussed approach has been applied to an industrial wood debarking plant and a biomass boiler analysis, enabling the preliminary study of process variables and conditions affecting the non-optimal process conditions. In addition, main process interactions and delays were easily discovered from the structures of the interpretable linear model candidates. It is concluded that the analysis can provide valuable information also for modelling and control of continuous processes.

A soft computing monitoring approach for automated screw insertions is presented. A model based monitoring method is developed with systematically collected experimental data and fundamental process knowledge to verify the quality of assemblies. The model for quality monitoring is based on Linguistic Equations (LE)-a non-linear scaling framework for model variables. Fuzzy reasoning and basic statistical methods are combined to interpret the model residuals and faults. Preliminary tests indicate that the proposed method could successfully cope with changes in manufacturing parameters. Based on the results, the method seems to provide valuable information for quality control of the screw insertion task.