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This paper presents experimental data concerning combustion characteristics of full-scale biomass-fired bubbling fluidized bed (BFB) steam boiler with a thermal output of 31 MW. The purpose of the experimental measurements is to show how the values of selected combustion parameters vary in reality depending on measurement position. Experimentation involves specifically a determination of combustion gas temperature and concentration of gas species i.e. O₂, CO₂, CO and NO_X at different positions in the furnace and the flue gas trains. Character of results from the furnace indicates the intermediate stage of thermochemical reactions. Increased levels of CO close to the wall have been found, this may be indicating reducing atmosphere and thereby increased corrosion risk. Results from flue gas trains demonstrate that behavior there is related to the fluid dynamics and heat transfer, the temperature is too low for further combustion reactions. Results show great variations among measured values of all measurands depending on a distance along the line from the wall to the center of the boiler. The measurements from permanently installed fixed sensors are not giving value representing average conditions, but overall profiles can be correlated to online measurements from fixed sensors.

The mechanism of biomass gasification has been studied for decades. However, for circulating fluidized bed (CFB) gasifiers, the impacts of different parameters on the gas quality and gasifiers performance have still not been fully investigated. In this paper, different CFB gasifiers have been analyzed by multivariate analysis statistical tools to identify the hidden interrelation between operating parameters and product gas quality, the most influencing input parameters and the optimum points for operation. The results show that equivalence ratio (ER), bed material, temperature, particle size and carbon content of the biomass are the input parameters influencing the output of the gasifier the most. Investigating among the input parameters with opposite impact on product gas quality, cases with optimal gas quality can result in high tar yield and low carbon conversion while low tar yield and high carbon conversion can result in product gas with low quality. However using Olivine as the bed material and setting ER value around 0.3, steam to biomass ratio to 0.7 and using biomass with 3 mm particle size and 9 wt% moisture content can result in optimal product gas with low tar yield.

Biomass used in energy conversion processes is typically characterized by high variability, making its utilization challenging. Therefore, there is a need for a fast and non-destructive method to determine feedstock/product properties and directly monitor process reactors. The near-infrared spectroscopy (NIRS) technique together with advanced data analysis methods offers a possible solution. This review focuses on the introduction of the NIRS method and its recent applications to physical, thermochemical, biochemical and physiochemical biomass conversion processes represented mainly by pelleting, combustion, gasification, pyrolysis, as well as biogas, bioethanol, and biodiesel production. NIRS has been proven to be a reliable and inexpensive method with a great potential for use in process optimization, advanced control, or product quality assurance.

This paper presents the results of experimental measurements and multivariate statistical modeling concerning detection of soda-lime glass using near infrared (NIR) spectrometry technique. The purpose is to test if the glass is quantitatively detectable in a waste-based material and to assess what method of spectral data pretreatment is the most suitable in order to develop prediction models. The experiments were performed on six test samples containing a specific amount of glass distributed in background material. Pretreatment methods such as normalization and first and second derivatives were applied on the acquired absorbance spectral data. Principal component analysis (PCA) was employed in order to describe the relationship between pretreated data and the amount of glass in the test samples. Subsequently, principal component regression (PCR) was utilized for the development of prediction models. The results from the models show strong correlation between the pretreated data and the glass content. The most promising results were obtained from the model based on 1st derivative pretreatment when only absorbance spectral data from selected wavelengths are included. 

This paper focuses on the characterization of highly variable biofuel properties such as moisture content, ash content and higher heating value by near-infrared (NIR) spectroscopy. Experiments were performed on different biofuel sample mixtures consisting of stem wood chips, forest residue chips, bark, sawdust, and peat. NIR scans were performed using a Fourier transform NIR instrument, and reference values were obtained according to standardized laboratory methods. Spectral data were pre-processed by Multiplicative scatter correction correcting light scattering and change in a path length for each sample. Multivariate calibration was carried out employing Partial least squares regression while absorbance values from full NIR spectral range (12,000–4000 cm^-1), and reference values were used as inputs. It was demonstrated that different solid biofuel properties can be measured by means of NIR spectroscopy. The accuracy of the models is satisfactory for industrial implementation towards improved process control.