The emission of sulphur dioxide (SO2) gas from power plants and factories to the atmosphere has been an environmental challenge globally. Thus, there is a great interest to control the SO2 gas emission economically and effectively. This study aims to use and convert abundantly available oil palm fiber (OPF) biomass into an adsorbent to adsorb SO2 gas. The preparation of OPF biochar and activated biochar was optimised using the Response Surface Methodology (RSM) based on selected parameters (i.e., pyrolysis temperature, heating rate, holding time, activation temperature, activation time and CO2 flowrate). The best adsorbent was found to be the OPF activated biochar (OPFAB) compared to OPF biochar. OPFAB prepared at 753 °C for 73 min of activation time with 497 ml/min of CO2 flow yields the best adsorption capacity (33.09 mg/g) of SO2. Meanwhile, OPF pyrolysed at 450 °C of heating temperature, 12 °C/min of heating rate and 98 min of holding time yield adsorption capacity at 18.62 mg/g. Various characterisations were performed to investigate the properties and mechanism of the SO2 adsorption process. Thermal regeneration shows the possibilities for the spent adsorbent to be recycled. The findings imply OPFAB as a promising adsorbent for SO2 adsorption.
Palm oil sludge (POS) is an organic waste generated from the palm oil industry. POS causes environmental pollution if it is improperly disposed. In this study, the potential of activated POS biochar, as an adsorbent for the removal of SO2 gas was tested. POS biochar was physically activated using CO2 gas. The effects of activation preparation variables i.e. activation temperature (300-700 °C), activation time (30-150 min) and CO2 flow rate (100-500 ml/min) were investigated using design expert version 8.0.7.1 software. Central Composite Design (CCD) was used to develop a quadratic model to correlate the operating variables with the activated biochar adsorption capacity. Analysis of variance (ANOVA) was performed to identify the significant factors on the experimental design response. The optimum preparation conditions of activated POS biochar were found to be at activation temperature of 442 °C, activation time of 63 min and CO2 flow rate of 397 ml/min. The maximum adsorption capacity at the optimum conditions was recorded as 16.65 mg/g. The adsorption capacity increased significantly after the activation process. Characteristics of the activated POS biochar proposed that SO2 was physically adsorbed. Furthermore, it was found that the adsorption capacity can be further enhanced by increasing the reaction temperature to 100 °C or with 15% of relative humidity in the inlet gas. The prepared adsorbents can be regenerated by thermal treatment.
Numerous mitigation techniques have been incorporated to capture or remove SO2 with flue gas desulfurization (FGD) being the most common method. Regenerative FGD method is advantageous over other methods due to high desulfurization efficiency, sorbent regenerability, and reduction in waste handling. The capital costs of regenerative methods are higher than those of commonly used once-through methods simply due to the inclusion of sorbent regeneration while operational and management costs depend on the operating hours and fuel composition. Regenerable sorbents like ionic liquids, deep eutectic solvents, ammonium halide solutions, alkyl-aniline solutions, amino acid solutions, activated carbons, mesoporous silica, zeolite, and metal-organic frameworks have been reported to successfully achieve high SO2 removal. The presence of other gases in flue gas, e.g., O2, CO2, NOx, and water vapor, and the reaction temperature critically affect the sorption capacity and sorbent regenerability. To obtain optimal SO2 removal performance, other parameters such as pH, inlet SO2 concentration, and additives need to be adequately governed. Due to its high removal capacity, easy preparation, non-toxicity, and low regeneration temperature, the use of deep eutectic solvents is highly feasible for upscale utilization. Metal-organic frameworks demonstrated highest reported SO2 removal capacity; however, it is not yet applicable at industrial level due to its high price, weak stability, and robust formulation.
In this study, the kinetic parameters of rice husk ash (RHA)/CaO/CeO(2) sorbent for SO(2) and NO sorptions were investigated in a laboratory-scale stainless steel fixed-bed reactor. Data experiments were obtained from our previous results and additional independent experiments were carried out at different conditions. The initial sorption rate constant (k(0)) and deactivation rate constant (k(d)) for SO(2)/NO sorptions were obtained from the nonlinear regression analysis of the experimental breakthrough data using deactivation kinetic model. Both the initial sorption rate constants and deactivation rate constants increased with increasing temperature, except at operating temperature of 170 °C. The activation energy and frequency factor for the SO(2) sorption were found to be 18.0 kJ/mol and 7.37 × 10(5)cm(3)/(g min), respectively. Whereas the activation energy and frequency factor for the NO sorption, were estimated to be 5.64 kJ/mol and 2.19 × 10(4)cm(3)/(g min), respectively. The deactivation kinetic model was found to give a very good agreement with the experimental data of the SO(2)/NO sorptions.
The SO2 sorption capacity (SSC) of sorbents prepared from rice husk ash (RHA) with NaOH as additive was studied in a fixed-bed reactor. The sorbents were prepared using a water hydration method by slurrying RHA, CaO, and NaOH. Response surface methodology (RSM) based on four-variable central composite face centered design (CCFCD) was employed in the synthesis of the sorbents. The correlation between the sorbent SSC (as response) with four independent sorbent preparation variables, i.e. hydration period, RHA/CaO ratio, NaOH amount, and drying temperature, were presented as empirical mathematical models. Among all the variables studied, the amount of NaOH used was found to be the most significant variable affecting the SSC of the sorbents prepared. The SSC for sorbent prepared with the addition of NaOH was found to be significantly higher than sorbents prepared without NaOH. This is probably because NaOH is a deliquescent material, and its existence increases the amount of water collected on the surface of the sorbent, a condition required for sorbent-SO2 reaction to occur at low temperature. The effect of further treatment of RHA at 600 degrees C was also investigated. Although pretreated RHA sorbents demonstrated higher SSC as compared to untreated RHA sorbents, nevertheless, at optimum conditions, sorbents prepared from untreated RHA was found to be more favorable due to practical and economic concerns.
In this study, palm oil mill sludge was used as a precursor to prepare biochar using conventional pyrolysis. Palm oil mill sludge biochar (POSB) was prepared at different preparation variables, i.e., heating temperature (300-800 °C), heating rate (10-20 °C/min) and holding time (60-120 min). The prepared biochars were tested for sulfur dioxide (SO2) adsorption in a fixed bed reactor using 300 ppm of SO2 gas at 300 ml/min (with N2 gas as balance). Response surface central composite experimental design was used to optimize the production of biochar versus SO2 removal. A quadratic model was developed in order to correlate the effect of variable parameters on the optimum adsorption capacity of SO2 gas. The experimental values and the predicted results of the model were found to show satisfactory agreement. The optimum conditions for biochar preparation to yield the best SO2 removal was found to be at 405 °C of heating temperature, 20 °C/min of heating rate and 88 min of holding time. At these conditions, the average yield of biochar and adsorption capacity for SO2 gas was reported as 54.25 g and 9.75 mg/g, respectively. The structure of biochar and their roles in SO2 adsorption were investigated by surface area, morphology images, infrared spectra, and proximate analysis, respectively. The characterization findings suggested that POSB adsorbs SO2 mainly by the functional groups.
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).
Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.
In July 1997 at 7 pm , 49 members 0f the public complained of dizziness, sore throat, eyes irritation and tightness of chest after exposed to sulphur dioxide while traveling on the road about 100 meter 90m the sulphuric acid factory in Teluk Kalong Kemaman. The incident was caused by leaking sulfur dioxide from return pipe of scrubber circulation. tank in the factory. The problem had occurred due to carelessness of the operator to inspect the level of water in the scrubber circulation tank during the operation.
An electron beam (EB) flue gas test rig and a dielectric barrier discharge (DBD) reactor were tested for the removal of nitric oxide (NO) from gas stream in separate experiments. In both systems, energised electrons were used to produce radicals that reacted with the pollutants. The EB system was a laboratory scale test rig used to treat emission from a diesel run generator. At 1.0 MeV and 10 mA more than 90% NO removal from flue gases flowing at 120 Nm3/h can be achieved. For higher removal percentage, higher beam current was required. In a related effort, a table top, two tubes DBD reactor was used to process bottled gases containing 106 ppm NO. Total removal (>99%) was achieved when the inlet gas contained only NO and N2. Additional SO2 in the in let gas stream lowered the removal rate but was overcame by scaling up the system to 10 DBD tubes. The system was operated with input AC voltage of 35 kV peak to peak. In the EB treatment system, the amount of NO2 increased at high beam current, showing that the NO was also oxidised in the process. Whereas in the DBD reactor, the amount of NO2 remained insignificant throughout the process. This leads to the conclusion that the DBD reactor is capable of producing total removal of NO. This is highly desirable as post treatment will not be necessary.
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.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
PM2.5 mass concentration and associated water-soluble ionic species in a sub-urban coastal area of Kuala Terengganu, Malaysia were investigated intermittently from year 2006 to 2009. A total of 78 weekly PM2.5 samples were analyzed. The mass concentration of PM2.5 exhibited annual, seasonal and diurnal variations. Temporal distributions of rainfall, sporadic haze episodes and local air flow (sea breeze circulation) were factors controlling PM2.5 mass variations in the study area. Although the PM2.5 concentrations were increased during haze episodes in 2006 (August and October) and2007 (October), their concentrations however, were still within the international guidelines. The average concentration of individual ions was in decreasing trend; SO42-> NH4+> K+> Na+> NO3-> Cl-> Ca2+. The concentrations of SO42- and NH4+ accounted for > 70% of the water-soluble aerosol mass. More than 80% of ionic species associated with PM2.5 are from non-marine sources. Major processes affecting the ionic composition of PM2.5 are biomass burning, crustal loading and sea spray. Air quality mitigation strategies should focus on anthropogenic activities emitting SO2, which promotes aerosol SO42- formation.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management.
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.
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.
In this work, the application of response surface and neural network models in predicting and optimizing the preparation variables of RHA/CaO/CeO(2) sorbent towards SO(2)/NO sorption capacity was investigated. The sorbents were prepared according to central composite design (CCD) with four independent variables (i.e. hydration period, RHA/CaO ratio, CeO(2) loading and the use of RHA(raw) or pretreated RHA(600 degrees C) as the starting material). Among all the variables studied, the amount of CeO(2) loading had the largest effect. The response surface models developed from CCD was effective in providing a highly accurate prediction for SO(2) and NO sorption capacities within the range of the sorbent preparation variables studied. The prediction of CCD experiment was verified by neural network models which gave almost similar results to those determined by response surface models. The response surface models together with neural network models were then successfully used to locate and validate the optimum hydration process variables for maximizing the SO(2)/NO sorption capacities. Through this optimization process, it was found that maximum SO(2) and NO sorption capacities of 44.34 and 3.51 mg/g, respectively could be obtained by using RHA/CaO/CeO(2) sorbents prepared from RHA(raw) with hydration period of 12h, RHA/CaO ratio of 2.33 and CeO(2) loading of 8.95%.
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.