Investigation in control of small-scale biomass boilers

Quantities measured during biomass combustion experiments are heavily burdened with a considerable noise. Usage of common linear low-pass filters is able to smooth measured time rows but it also introduces typical dynamic delay of filtered data. The article presents comparison of three commonly used linear filters-Butterworth, Bessel and Chebishev. An effort to smooth measured data without introducing dynamic delay led us to use some of less common non-linear filters. The article further presents usage of Threshold and Gaussian weighted average non-linear filters and compares them with the linear ones. Abstrakt Veličiny sledované při experimentech se spalováním biomasy jsou zpravidla významně zatížené silným šumem. Použití běžných lineárních filtrů s dolní propustí sice dokáže vyhladit průběhy měřených veličin, ale zavádí do filtrovaného signálu typické dynamické zpoždění. Tento článek porovnává použití tří běžně používaných lineární filtrů-Butterworthův, Besselův a Čebiševův. Další snaha o vyhlazení měřených dat bez zatížení dynamickým zpožděním vedla autory k použití méně běžných nelineárních filtrů. Článek tedy dále srovnává použití prahového filtru a filtru založeném na Gaussovo křivkou váženém průměru.

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“…1). Further information about the boiler, its configuration and control, can be found in [PLAČEK et. al., 2011], [HAAPA-AHO et.…”

Section: Common Signal Filtersmentioning

confidence: 99%

Usability of Low-Pass Filters on Biomass Combustion Quantities

Placier-Zavala¹,

Plaček²

2012

TransVSBms

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“…[n this research project we investigated not only control problems but also many other problems such as software testing of sensor credibility, achieving the compromise in CO2 and NOx shares optimizing power and combustion ratio interventions in boiler design and some other tasks resulting from the fact, that this boiler is a prototype model. None of other limiting factors linked with operation of the boiler are not explicitly mentioned here but they can be found in [3], [5].…”

Section: Introductionmentioning

confidence: 99%

Support of biomass boiler control design by modeling

Plaček

Šulc

2015

Proceedings of the 2015 16th International Carpathian Control Conference (ICCC)

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Most of small-scale combustion devices firing biomass use control algorithms designed on the basis of an empirical experience. Very often, the on-off strategy is applied for heat output control linked with repetitive starts of the burning process which may be linked with some undesirable effects especially as far as emissions and efficiency are concerned. There are more reasons why such control solutions are preferred in practice, but one of the most evident is the difficulty of modeling biomass combustion processes which are strongly dependent on the fuel properties and its variability. Appraisal of both model based and model free methods in the controller design was the object of investigations we carried out on an experimental small scale water heating boiler firing wood chips of various quality. Results from testing suitability of a model intended for both simulation and control design are reported in this paper completed with some other pieces of knowledge from ecological operation of biomass boilers. Usage of the model will be demonstrated on the model-based control algorithm.

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“…An effective control of biomass combustion and biomass-fired boilers in general in regard to meeting ever stricter requirements on emission levels remains a challenging task [1], [2], [3]. In works [2] and [3] the aspects of biomass-fired boiler operation in terms of carbon monoxide and oxygen concentration relationship are analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…In works [2] and [3] the aspects of biomass-fired boiler operation in terms of carbon monoxide and oxygen concentration relationship are analyzed. Despite the complexities of processes associated with biomass combustion, the commonest controller type applied to this task is still a conventional PID controller [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Biomass-fired Boiler Control Using Simulated Annealing Optimized Improved Varela Immune Controller

Hosŏvský

2014

APH

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Investigation in control of small-scale biomass boilers

Cited by 16 publications

References 1 publication

Usability of Low-Pass Filters on Biomass Combustion Quantities

Usability of Low-Pass Filters on Biomass Combustion Quantities

Support of biomass boiler control design by modeling

Biomass-fired Boiler Control Using Simulated Annealing Optimized Improved Varela Immune Controller

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