This work examines the impregnated carbon-based sorbents for simultaneous removal of SO(2) and NOx from simulated flue gas. The carbon-based sorbents were prepared using palm shell activated carbon (PSAC) impregnated with several metal oxides (Ni, V, Fe and Ce). The removal of SO(2) and NOx from the simulated flue gas was investigated in a fixed-bed reactor. The results showed that PSAC impregnated with CeO(2) (PSAC-Ce) reported the highest sorption capacity among other impregnated metal oxides for the simultaneous removal of SO(2) and NOx. PSAC-Ce showed the longest breakthrough time of 165 and 115 min for SO(2) and NOx, respectively. The properties of the pure and impregnated PSAC were analyzed by BET, FTIR and XRF. The physical-chemical features of the PSAC-Ce sorbent indicated a catalytic activity in both the sorption of SO(2) and NOx. The formation of both sulfate (SO(4)(2-)) and nitrate (NO(3-)) species on spent PSAC-Ce further prove the catalytic role played by CeO(2).
In order to reduce the negative impact of coal utilization for energy generation, the pollutants present in the flue gas of coal combustion such as sulfur dioxide (SO(2)) and nitrogen oxide (NO) must be effectively removed before releasing to the atmosphere. Thus in this study, sorbent prepared from rice husk ash that is impregnated with copper is tested for simultaneous removal of SO(2) and NO from simulated flue gas. The effect of various sorbent preparation parameters; copper loading, RHA/CaO ratio, hydration period and NaOH concentration on the sorbent desulfurization/denitrification capacity was studied using Design-Expert Version 6.0.6 software. Specifically, Central Composite Design (CCD) coupled with Response Surface Method (RSM) was used. Significant individual parameters that affect the sorbent capacity are copper loading and NaOH concentration. Apart from that, interaction between the following parameters was also found to have significant effect; copper loading, RHA/CaO ratio and NaOH concentration. The optimum sorbent preparation condition for this study was found to be 3.06% CuO loading, RHA/CaO ratio of 1.41, 8.05 h of hydration period and NaOH concentration of 0.80 M. Sorbent characterization using SEM, XRD and surface area analysis were used to describe the effect of sorbent preparation parameters on the desulfurization/denitrification activity.
Siliceous materials such as rice husk ash (RHA) have potential to be utilized as high performance sorbents for the flue gas desulfurization process in small-scale industrial boilers. This study presents findings on identifying the key factorfor high desulfurization activity in sorbents prepared from RHA. Initially, a systematic approach using central composite rotatable design was used to develop a mathematical model that correlates the sorbent preparation variables to the desulfurization activity of the sorbent. The sorbent preparation variables studied are hydration period, x1 (6-16 h), amount of RHA, x2 (5-15 g), amount of CaO, x3 (2-6 g), amount of water, x4 (90-110 mL), and hydration temperature, x5 (150-250 degrees C). The mathematical model developed was subjected to statistical tests and the model is adequate for predicting the SO2 desulfurization activity of the sorbent within the range of the sorbent preparation variables studied. Based on the model, the amount of RHA, amount of CaO, and hydration period used in the preparation step significantly influenced the desulfurization activity of the sorbent. The ratio of RHA and CaO used in the preparation mixture was also a significant factor that influenced the desulfurization activity of the sorbent. A RHA to CaO ratio of 2.5 leads to the formation of specific reactive species in the sorbent that are believed to be the key factor responsible for high desulfurization activity in the sorbent. Other physical properties of the sorbent such as pore size distribution and surface morphology were found to have insignificant influence on the desulfurization activity of the sorbent.
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.
In this work, the removal of SO(2) and NO from simulated flue gas from combustion process was investigated in a fixed-bed reactor using rice husk ash (RHA)/CaO-based sorbent. Various metal precursors were used in order to select the best metal impregnated over RHA/CaO sorbents. The results showed that RHA/CaO sorbents impregnated with CeO(2) had the highest sorption capacity among other impregnated metal oxides for the simultaneous removal of SO(2) and NO. Infrared spectroscopic results indicated the formation of both sulfate (SO(4)(2-)) and nitrate (NO(3)(-)) species due to the catalytic role played by CeO(2). Apart from that, the catalytic activity of the RHA/CaO/CeO(2) sorbent was found to be closely related to its physical properties (specific surface area, total pore volume and average pore diameter).