The mechanistic modeling of the sulfation reaction between fly ash-based sorbent and SO2 is a challenging task due to a variety reasons including the complexity of the reaction itself and the inability to measure some of the key parameters of the reaction. In this work, the possibility of modeling the sulfation reaction kinetics using a purely data-driven neural network was investigated. Experiments on SO2 removal by a sorbent prepared from coal fly ash/CaO/CaSO4 were conducted using a fixed bed reactor to generate a database to train and validate the neural network model. Extensive SO2 removal data points were obtained by varying three process variables, namely, SO2 inlet concentration (500-2000 mg/L), reaction temperature (60-80 degreesC), and relative humidity (50-70%), as a function of reaction time (0-60 min). Modeling results show that the neural network can provide excellent fits to the SO2 removal data after considerable training and can be successfully used to predict the extent of SO2 removal as a function of time even when the process variables are outside the training domain. From a modeling standpoint, the suitably trained and validated neural network with excellent interpolation and extrapolation properties could have immediate practical benefits in the absence of a theoretical model.
High performance sorbents for flue gas desulfurization can be synthesized by hydration of coal fly ash, calcium sulfate, and calcium oxide. In general, higher desulfurization activity correlates with higher sorbent surface area. Consequently, a major aim in sorbent synthesis is to maximize the sorbent surface area by optimizing the hydration conditions. This work presents an integrated modeling and optimization approach to sorbent synthesis based on statistical experimental design and two artificial intelligence techniques: neural network and genetic algorithm. In the first step of the approach, the main and interactive effects of three hydration variables on sorbent surface area were evaluated using a full factorial design. The hydration variables of interest to this study were hydration time, amount of coal fly ash, and amount of calcium sulfate and the levels investigated were 4-32 h, 5-15 g, and 0-12 g, respectively. In the second step, a neural network was used to model the relationship between the three hydration variables and the sorbent surface area. A genetic algorithm was used in the last step to optimize the input space of the resulting neural network model. According to this integrated modeling and optimization approach, an optimum sorbent surface area of 62.2m(2)g(-1) could be obtained by mixing 13.1g of coal fly ash and 5.5 g of calcium sulfate in a hydration process containing 100ml of water and 5 g of calcium oxide for a fixed hydration time of 10 h.
In this present experimental study, geopolymer cement is developed using high calcium fly ash and used in the production of one-part alkali-activated binders. At 8-16 percent of the total precursor materials, the HCFA was activated with anhydrous sodium metasilicate powder and cured in ambient condition. Five mixtures of one-part geopolymer paste were intended at a steady w/b proportion. Density, flowability, setting time, compressive strength, splitting tensile strength and molar ratio impact were envisaged. It was observed that the setting time of the designed one-part geopolymer paste decreases with higher activator content. The experimental findings showed that the resistance of one-part geopolymer cement paste increases with comparatively greater activator content. However, raising the granular activator beyond 12 percent by fly ash weight decreases the strength and workability of the established one-part geopolymer cement. The optimum mix by weight of the fly ash was discovered to be 12 percent (i.e. 6 percent Na2O). At 28 days of curing, one-part alkali-activated paste recorded the greatest compressive strength of almost 50 MPa. The density of the one-part geopolymer paste is nearly the same regardless of the mixes. Microstructural assessment by FESEM, FTIR and XRD has shown that the established geopolymer paste includes quartz, pyrrhotite, aluminosilicate sodium and hydrate gels of calcium aluminosilicate. Based on the experimental information acquired, it can be deduced that the strength growth of one-part geopolymer cement is similar to that of Portland cement.
Coal combustion and the disposal of combustion wastes emit enormous quantities of nano-sized particles that pose significant health concerns on exposure, particularly in unindustrialized countries. Samples of fresh and weathered class F fly ash were analysed through various techniques including X-ray fluorescence (XRF), X-ray diffraction (XRD), focused ion beam scanning electron microscopy (FIB-SEM), field-emission gun scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM) coupled with energy dispersive x-ray spectroscopy (EDS), and Raman Spectroscopy. The imaging techniques showed that the fresh and weathered coal fly ash nanoparticles (CFA-NPs) are mostly spherical shaped. The crystalline phases detected were quartz, mullite, ettringite, calcite, maghemite, hematite, gypsum, magnetite, clay residues, and sulphides. The most abundant crystalline phases were quartz mixed with Al-Fe-Si-K-Ti-O-amorphous phases whereas mullite was detected in several amorphous phases of Al, Fe, Ca, Si, O, K, Mg, Mn, and P. The analyses revealed that CFA-NPs are 5-500 nm in diameter and encapsulate several potentially hazardous elements (PHEs). The carbon species were detected as 5-50 nm carbon nanoballs of graphitic layers and massive fullerenes. Lastly, the aspects of health risks related to exposure to some detected ambient nanoparticles are also discussed.