## 3 Results  
The designed filter with the Gauss membership function was implemented into a free programmable industrial process control system of biomass combustion which was monitored on-line by a Supervisory Control and Data Acquisition (SCADA) system [11]. On-line monitoring allows  
Figure 11: Filtering of the noisy measured variable for different sensitivity of the membership function.  
Figure 10: Filtering of the noisy measured variable for different number of samples.  
Figure 9: Filter time responses to unit step for different number of samples.  
visualization of the technological process (graphical schemes, diagrams, trends and reports), to evaluate the quality of combustion process and to change the control parameters with access via the internet [1]. The designed control and monitoring system was installed for the control of two medium-scale biomass-fired boilers for woodchip combustion (with different powers and different manufactures). Grate furnace combustion technology was used in both boilers, and the sketch of one type of boiler is in Figure 12. The reversible shifting grate is situated in the firebox, the primary combustion air is fed by means of a fan under the grate, the secondary one above the grate. The amount of primary and secondary air is controlled by means of corresponding fan rpm [3].  
In Figure 13 there is an example of the process variables' remote monitoring. As can be seen, some of them are interfered with strongly. In particular the quality of O\({}_{2}\) concentration sensing (yellow course in Figure 13) was unable to be used for effective control of the combustion process. The correct values of O\({}_{2}\) concentration in the flue gas are very important for the combustion process control because efficiency of the biomass combustion depends on the excess air ratio \(\lambda\), which can be obtained from the measured O\({}_{2}\) concentration as follows [23]:  
\[\lambda=\frac{21}{21-\text{O}_{2\%}} \tag{8}\]  
where O\({}_{2\%}\) is oxygen concentration in the flue gas in percentage.  
That is why, firstly, the new filter with a membership function was applied for filtering O\({}_{2}\) concentration measured data and the result of filtration is shown in Figure 14.  
The optimal range of excess air ratio \(\lambda\) for biomass combustion is usually in the interval (1.4-2) and its optimal value depends on the type of wood, the moisture content in the wood, combustion chamber construction, and so on. However, the most optimal biomass combustion operating conditions are when the compromise between maximal combustion efficiency and minimal CO emissions is achieved. One of the control algorithm's tasks is to find such a value of excess air ratio \(\lambda\) so that CO emissions would be minimal, even with the change in fuel parameters. To fulfill this task, it is necessary to continuously monitor a trend between CO emission and excess air ratio and, consequently, to change the set point of O\({}_{2}\) concentration in the flue gas [11; 24]. That is why this filter was also applied for filtering CO emissions' measured data, and the result of filtration is shown in Figure 15.  
Figure 12: The sketch of 600 kW biomass-fired boiler.  
The quality of filtration has been confirmed by comparison of the membership function filter with the other types of filters. Figure 16, for example, shows a comparison with a moving average filter. As such, it can be seen that the moving average filter does not exclude values that are extremely  
Figure 14: Filtering of O\({}_{2}\) concentration.  
Figure 13: Remote monitoring of biomass combustion process variables.  
Figure 15: Filtering of CO concentration.  
different. On the other hand, the membership function filter monitors the trend of data, but there is a significant time delay. However, this time delay does not have an important impact on O\({}_{2}\) or CO sensing in the biomass combustion process because the trend of their concentration is important not their current values.