Behavior of black liquor nitrogen in combustion : formation of cyanate

The Kraft pulping process is the dominant chemical pulping process in the world. Roughly 195 million metric tons of black liquor are produced annually as a by-product from the Kraft pulping process. Black liquor consists of spent cooking chemicals and dissolved organics from the wood and can contain up to 0.15 wt% nitrogen on dry solids basis. The cooking chemicals from black liquor are recovered in a chemical recovery cycle. Water is evaporated in the first stage of the chemical recovery cycle, so the black liquor has a dry solids content of 65-85% prior to combustion. During combustion of black liquor, a portion of the black liquor nitrogen is volatilized, finally forming N2 or NO. The rest of the nitrogen remains in the char as char nitrogen. During char conversion, fixed carbon is burned off leaving the pulping chemicals as smelt, and the char nitrogen forms mostly smelt nitrogen (cyanate, OCN-). Smelt exits the recovery boiler and is dissolved in water. The cyanate from smelt decomposes in the presence of water, forming NH3, which causes nitrogen emissions from the rest of the chemical recovery cycle. This thesis had two focuses: firstly, to determine how the nitrogen chemistry in the recovery boiler is affected by modification of black liquor; and secondly, to find out what causes cyanate formation during thermal conversion, and which parameters affect cyanate formation and decomposition during thermal conversion of black liquor. The fate of added biosludge nitrogen in chemical recovery was determined in Paper I. The added biosludge increased the nitrogen content of black liquor. At the pulp mill, the added biosludge did not increase the NO formation in the recovery boiler, but instead increased the amount of cyanate in green liquor. The increased cyanate caused more NH3 formation, which increased the NCG boiler’s NO emissions. Laboratory-scale experiments showed an increase in both NO and cyanate formation after biosludge addition. Black liquor can be modified, for example by addition of a solid biomass to increase the energy density of black liquor, or by separation of lignin from black liquor by precipitation. The precipitated lignin can be utilized in the production of green chemicals or as a fuel. In Papers II and III, laboratory-scale experiments were conducted to determine the impact of black liquor modification on NO and cyanate formation. Removal of lignin from black liquor reduced the nitrogen content of the black liquor. In most cases NO and cyanate formation decreased with increasing lignin removal; the exception was NO formation from lignin lean soda liquors. The addition of biomass to black liquor resulted in a higher nitrogen content fuel mixture, due to the higher nitrogen content of biomass compared to black liquor. More NO and cyanate were formed from the fuel mixtures than from pure black liquor. The increased amount of formed cyanate led to the hypothesis that black liquor is catalytically active and converts a portion of the nitrogen in the mixed fuel to cyanate. The mechanism behind cyanate formation during thermal conversion of black liquor was not clear before this thesis. Paper IV studies the cyanate formation of alkali metal loaded fuels during gasification in a CO2 atmosphere. The salts K2CO3, Na2CO3, and K2SO4 all promoted char nitrogen to cyanate conversion during gasification, while KCl and CaCO3 did not. It is now assumed that cyanate is formed when alkali metal carbonate or an active intermediate of alkali metal carbonate (e.g. -CO2K) reacts with the char nitrogen forming cyanate. By testing different fuels (bark, peat, and coal), each of which had a different form of organic nitrogen, it was concluded that the form of organic nitrogen in char also has an impact on cyanate formation. Cyanate can be formed during pyrolysis of black liquor, but at temperatures 900°C or above, the formed cyanate will decompose. Cyanate formation in gasifying conditions with different levels of CO2 in the atmosphere was also studied. Most of the char nitrogen was converted to cyanate during gasification at 800-900°C in 13-50% CO2 in N2, and only 5% of the initial fuel nitrogen was converted to NO during char conversion. The formed smelt cyanate was stable at 800°C 13% CO2, while it decomposed at 900°C 13% CO2. The cyanate decomposition was faster at higher temperatures and in oxygen-containing atmospheres than in an inert atmosphere. The presence of CO2 in oxygencontaining atmospheres slowed down the decomposition of cyanate. This work will provide new information on how modification of black liquor affects the nitrogen chemistry during thermal conversion of black liquor and what causes cyanate formation during thermal conversion of black liquor. The formation and decomposition of cyanate was studied in order to provide new data, which would be useful in modeling of nitrogen chemistry in the recovery boiler.

Leaf morphological and physiological responses of Quercus aquifolioides along an altitudinal gradient

Abstract

Lean Duplex -terästen hitsaus

Viime vuosien aikana tapahtunut nikkelin hinnan nouseminen on vaikuttanut austeniittis-ferriittisten ruostumattomien terästen, ns. duplex -terästen kehittämiseen. Niukkaseosteisemmissa lean duplex -teräksissä seostetun nikkelin määrää on vähennetty ja sitä on korvattu typellä ja mangaanilla. Nämä muutokset ko. terästen seostuksessa aiheuttavat haasteita hitsaukselle, erityisesti austeniitti-ferriitti -suhteen säilyttämisessä, sekä sitä kautta iskusitkeyden ja korroosio-ominaisuuksien säilyttämiselle. Suurempi typen osuus myös lisää teräksen hitsisulan viskositeettia, mikä heikentää juuripalkojen hitsauksessa tunkeumaa. Tässsä diplomityössä on tutkittu keinoja helpottaa paksujen (yli 20 mm) lean duplex -teräslevyjen hitsausta käytännön näkökulmasta, sekä parantaa hitsattujen levyjen iskusitkeyttä. Hitsauskokeilla löydettiin hitsausta helpottavia menetelmiä ja kokeista saatiin karsimalla valikoitua hitsausarvot, joilla pystytään hitsaamaan painelaitedirektiivin mukaisesti hyväksyttäviä hitsejä lean duplex -laatuihin LDX2101 ja UR2202.

Effect of dietary crude protein and energy content on nitrogen utilisation, water intake and urinary output in growing pigs

Selostus: Rehun valkuais- ja energiapitoisuuden vaikutus sikojen typen hyväksikäyttöön, veden kulutukseen ja virtsan eritykseen

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.