A Comparative Study of Fouling and Bottom Ash from Woody Biomass Combustion in a Fixed-Bed Small-Scale Boiler and Evaluation of the Analytical Techniques Used

Abstract

Abstract:In this work, fouling and bottom ash were collected from a low-power boiler after wood pellet combustion and studied using several analytical techniques to characterize and compare samples from different areas and determine the suitability of the analysis techniques employed. TGA results indicated that the fouling contained a high amount of organic matter (70%). The XRF and SEM-EDS measurements revealed that Ca and K are the main inorganic elements and exhibit clear tendency in the content of Cl that is negligible in the bottom ash and increased as it penetrated into the innermost layers of the fouling. Calcite, magnesia and silica appeared as the major crystalline phases in all the samples. However, the bottom ash was primarily comprised of calcium silicates. The KCl behaved identically to the Cl, preferably appeared in the adhered fouling samples. This salt, which has a low melting point, condenses upon contact with the low temperature tube and played a crucial role in the early stages of fouling formation. XRD was the most useful technique applied, which provided a semi-quantitative determination of the crystalline phases. FTIR was proven to be inadequate for this type of sample. The XRF and SEM-EDS, techniques yield similar results despite being entirely different.

Experimental Study of the Viability of Low-Grade Biofuels in Small-Scale Appliances

“…Sustainability 2017, 9, 1823 4 of 16 agreement with established standards for the shear testing of bulk materials (according to ASTM-6773) [15] .The flowability was determined by the dimensionless flow index ffc. This dimensionless index is defined as the ratio between the consolidation stress (σ1) and the unconfined yield strength (fc or σc), and then, the material was classified according to the rules developed by Jenike in regards to the powder flowability group, as shown in Table 2 [15].…”

“…The automatic computer-controlled RST-01 (Ring Shear Tester) instrument was designed by the German inventor Dr. Dietmar Schulz in agreement with established standards for the shear testing of bulk materials (according to ASTM-6773) [15]. The flowability was determined by the dimensionless flow index ffc.…”

“…The flowability was determined by the dimensionless flow index ffc. This dimensionless index is defined as the ratio between the consolidation stress (σ 1 ) and the unconfined yield strength (f c or σ c ), and then, the material was classified according to the rules developed by Jenike in regards to the powder flowability group, as shown in Table 2 [15]. The measurement of the angle wall friction between a stainless-steel surface and the tested raw bulk materials was carried out using procedures similar to the measurement of the angle of internal friction.…”

“…Barley (green) was evaluated as a trouble-free material due to its flowability. Of course, flowability is a consequence of a combination of the material physical properties, such as moisture, particle-size distribution and so on, that influence the material flow and the equipment used for handling, storing or processing these materials [15]. The abovementioned conclusions are applicable only for the tested materials.…”

Prediction Control of Biomass Combustion Boiler based on Multilayer Perceptron Neural Network

“…The neural network prediction model of multilayer perceptron is a multilayer feed forward network model of one-way communication, which is one of the most basic network model in current research and application [4]. Under normal circumstances, the generalization ability of the model is improving with the training capacity of the network improving to some extent, considering the contradictory between generalization ability and training capacity of the network [5]. However, there is a limit about this trend, the generalization ability declines with the training ability improving when the model reaches this limit; the internal reason of this is the learning principle of model, when too much details of the sample is studied, the law will not be reflected by the network model.…”