This paper presents the development of an emissions-controlling technique for oil burners aimed especially to reduce oxides of nitrogen (NOx). Another emission of interest is carbon monoxide (CO). In this research, a liquid fuel burner is used. In the first part, five different radial air swirler blade angles, 30 degrees , 40 degrees , 45 degrees , 50 degrees , and 60 degrees , respectively, have been investigated using a combustor with 163 mm inside diameter and 280 mm length. Tests were conducted using kerosene as fuel. Fuel was injected at the back plate of the swirler outlet. The swirler blade angles and equivalence ratios were varied. A NOx reduction of more than 28% and CO emissions reduction of more than 40% were achieved for blade angle of 60 degrees compared to the 30 degrees blade angle. The second part of this paper presents the insertion of an orifice plate at the exit plane of the air swirler outlet. Three different orifice plate diameters of 35, 40, and 45 mm were used with a 45 degrees radial air swirler vane angle. The fuel flow rates and orifice plate's sizes were varied. NOx reduction of more than 30% and CO emissions reduction of more than 25% were obtained using the 25 mm diameter orifice plate compared to the test configuration without the orifice plate. The last part of this paper presents tests conducted using the air-staging method. An industrial oil burner system was investigated using the air staging method in order to reduce emission, especially NOx. Emissions reduction of 30% and 16.7% were obtained for NOx and CO emissions, respectively, when using air staging compared to the non-air-staging tests.
The problems of global warming and the unstable price of petroleum oils have led to a race to develop environmentally friendly biofuels, such as palm oil or ethanol derived from corn and sugar cane. Biofuels are a potential replacement for fossil fuel, since they are renewable and environmentally friendly. This paper evaluates the combustion performance and emission characteristics of Refined, Bleached, and Deodorized Palm Oil (RBDPO)/diesel blends B5, B10, B15, B20, and B25 by volume, using an industrial oil burner with and without secondary air. Wall temperature profiles along the combustion chamber axis were measured using a series of thermocouples fitted axially on the combustion chamber wall, and emissions released were measured using a gas analyzer. The results show that RBDPO blend B25 produced the maximum emission reduction of 56.9% of CO, 74.7% of NOx, 68.5% of SO(2), and 77.5% of UHC compared to petroleum diesel, while air staging (secondary air) in most cases reduces the emissions further. However, increasing concentrations of RBDPO in the blends also reduced the energy released from the combustion. The maximum wall temperature reduction was 62.7% for B25 at the exit of the combustion chamber.
Selective Non-Catalytic Reduction (SNCR) of nitric oxide has been studied experimentally by injecting aqueous urea solution with and without additive in a pilot-scale diesel fired tunnel furnace at 3.4% excess oxygen level and with low ppm of baseline NO(x) ranging from 65 to 75 ppm within the investigated temperature range. The tests have been carried out using commercial grade urea as NO(x) reducing agent and commercial grade sodium carbonate as additive. The furnace simulated the small-scale combustion systems, where the operating temperatures are usually in the range of about 973 to 1323 K and NO(x) emission level remains below 100 ppm. With 5% plain urea solution, at Normalized Stoichiometric Ratio (NSR) of 4 as much as 54% reduction was achieved at 1128 K, whilst in the additive case the NO(x) reduction was improved to as much as 69% at 1093 K. Apart from this improvement, in the additive case, the effective temperature window as well as peak temperature of NO(x) reduction shifted towards lower temperatures. The result is quite significant, especially for this investigated level of baseline NO(x). The ammonia slip measurements showed that in both cases the slip was below 16 ppm at NSR of 4 and optimum temperature of NO(x) reduction. Finally, the investigations demonstrated that urea based SNCR is quite applicable to small-scale combustion applications and commercial grade sodium carbonate is a potential additive.
In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
In this overview, the authors have discussed the potential advantages of the association between mycorrhizae and plants, their mutual accelerated growth under favorable conditions and their role in nutrient supply. In addition, methods for isolating mycorrhizae are described and spore morphologies and their adaptation to various conditions are outlined. Further, the significant participation of controlled greenhouses and other supported physiological environments in propagating mycorrhizae is detailed. The reviewed information supports the lack of host- and niche-specificity by arbuscular mycorrhizae, indicating that these fungi are suitable for use in a wide range of ecological conditions and with propagules for direct reintroduction. Regarding their prospective uses, the extensive growth of endomycorrhizal fungi suggests it is suited for poor-quality and low-fertility soils.
The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.