Environmental Engineering
J. Oliver Paul Nayagam; K. Prasanna
Abstract
BACKGROUND AND OBJECTIVES: The prediction models, response surface methodology and adaptive neuro-fuzzy inference system are utilized in this study. This study delves into the removal efficiency of reactive orange 16 using hydrochar derived from the Prosopis juliflora roots. Hydrochar dose, pH, temperature, ...
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BACKGROUND AND OBJECTIVES: The prediction models, response surface methodology and adaptive neuro-fuzzy inference system are utilized in this study. This study delves into the removal efficiency of reactive orange 16 using hydrochar derived from the Prosopis juliflora roots. Hydrochar dose, pH, temperature, and initial reactive orange 16 concentration were studied in batch processes. The correlation coefficients for the batch processes were found to be 0.978 and 0.9999. The results denote that the adaptive neuro-fuzzy inference system predicted the reactive orange 16 removal efficiency more accurately than the response surface methodology model.METHODS: Prosopis juliflora roots roots are converted into hydrochar to remove azo dye from textile waste water. Prosopis juliflora roots roots were collected from Ramanad District, Southern Tamil Nadu, India. The moisture content was lowered by drying for 24 hours at 103 degree celcius in an oven with hot air. This biomass was thermally destroyed at 300 degree celcius for 15 minutes without oxygen in an autoclave in a muffle furnace (heating rate: 5 degree celcius per minute). As soon as it reaches room temperature, the hydrochar residue of this biomass was used for adsorption investigations. The batch adsorption process was conducted for 6 hours in a 250 milliliter Erlenmeyer conical flask with a 100 milliliter working volume using an orbital shaker. The pH, dosage, concentration, and temperature are the four parameters chosen for this study to find the maximum removal efficiency of the dye from aqueous solutions. This study validated adaptive neuro-fuzzy inference system, an artificial neural network with a fuzzy inference system, using response surface methodology projected experimental run with Box–Behnken method.FINDINGS: The adaptive neuro-fuzzy inference system model is created alongside the response surface methodology model to compare experimental outcomes. Experimental data was evaluated using a hybrid least square and gradient technique. Statistical and residual errors assessed experimental and mathematical model correctness. Experimental data matched the adaptive neuro-fuzzy inference system results. Statistical error analysis verified the model’s accuracy and precision against experimental data.CONCLUSION: Response surface methodology and adaptive neuro-fuzzy inference system optimized process conditions. At pH 2, 2 gram per litre hydrochar dosage, 35 degree celcius , and a reactive orange 16 starting concentration of 250 milligram per liter, removal effectiveness reached 86.1 percent. Adaptive neuro-fuzzy inference system predicted higher values than response surface methodology, with batch correlation coefficients of 0.9999 and 0.9997, respectively. Mathematical techniques can accurately estimate dye removal efficiency from aqueous solutions.
Environmental Science
R. Mostafaloo; M. Asadi-Ghalhari; H. Izanloo; A. Zayadi
Abstract
Ciprofloxacin antibiotic that is used to cure several kinds of bacterial infections have a high solubility capacity in water. The influent of ciprofloxacin to water resources in a low concentration affect the photosynthesis of plants, transforms the morphological structure of the algae, and then disrupts ...
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Ciprofloxacin antibiotic that is used to cure several kinds of bacterial infections have a high solubility capacity in water. The influent of ciprofloxacin to water resources in a low concentration affect the photosynthesis of plants, transforms the morphological structure of the algae, and then disrupts the aquatic ecosystem. 75% of this compound is excreted from the body down to the wastewater which should be removed. BiFeO3, a bismuth-based semiconductor photocatalyst that is responsive to visible light, has been recently used to remove organic pollutants from water. In this study, the optimal conditions for removing ciprofloxacin from aqueous solutions by the BiFeO3 process were investigated. Effective parameters namely pH, reaction time, ciprofloxacin initial concentration, BiFeO3 dose, and temperature on ciprofloxacin removal were studied by using response surface methodology. The validity and adequacy of the proposed model was confirmed by the corresponding statistics (i.e. F-values of 14.79 and 1.67 and p-values of 2 = 0.9107, R2adjusted = 0.8492, R2 predicted = 0.70, AP = 16.761). Hence the Ciprofloxacin removal efficiency reached 100% in the best condition (pH 6, initial concentration of 1 mg/L, BiFeO3 dosage of 2.5 g/L, reaction temperature of 30° C, and process time of 46 min).
A.C. Affam
Abstract
Conventional steam activation pyrolysis of waste materials such as oil palm kernel shell for production of biochar was investigated using central composite design. Conventional steam activation was carried out via an initial carbonization of oil palm kernel shell to obtain biochar and thereafter steam ...
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Conventional steam activation pyrolysis of waste materials such as oil palm kernel shell for production of biochar was investigated using central composite design. Conventional steam activation was carried out via an initial carbonization of oil palm kernel shell to obtain biochar and thereafter steam activation of the biochar using the conventional heating to produce activated carbon. Additionally, removal of chemical oxygen demand and colour was studied alongside the production. Optimum yield was obtained at about 90 min and 725oC. Out of the time duration, 80 min was for carbonation and 10 min was for steam activation. Further extension of time was not significant whereas increasing temperature was able to increase the pores found on the biochar. Under the optimum condition, fixed carbon was 19.39%, chemical oxygen demand and colour removal were 32.02 and 61.15%, respectively at 90 min adsorption time. However, when time was extended to 120 min, chemical oxygen demand (48.2%) and colour (94.19%) removal were achieved. The Brunauer–Emmett–Teller surface area and micropore area of the oil palm kernel shell based activated carbon was 620.45 m2/g and 550.4 m2/g, respectively. The conventional steam activation is an effective method that can be employed in production of activated carbon from waste oil palm kernel shell.
M. Samimi; M. Shahriari Moghadam
Abstract
High concentrations of nitrogen compounds, such as ammonia observed in the petrochemical industry, are the major environmental pollutants. Therefore, effective and inexpensive methods are needed for its treatment. Biological treatment of various pollutants is a low cost and biocompatible replacement ...
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High concentrations of nitrogen compounds, such as ammonia observed in the petrochemical industry, are the major environmental pollutants. Therefore, effective and inexpensive methods are needed for its treatment. Biological treatment of various pollutants is a low cost and biocompatible replacement for current physico-chemical systems. The use of aquatic plants is an effective way to absorb the nutrient pollutants. In this study, the optimal operating conditions in the biological removal of ammonia from the urea-ammonia wastewater of Kermanshah Petrochemical Company by Lemna gibba were determined using the response surface methodology. Lemna gibba was collected from the ponds around Kermanshah and maintained in a nutrient medium. Effect of the main operational variables such as ammonia concentration, residence time and Lemna gibba to surface ratio on optimal conditions of ammonia removal from wastewater has been analyzed using the Box-Behnken model design of experiments. Model numerical optimization was performed to achieve the maximum amount of ammonia removal from wastewater. The ammonia removal percentage varied from 13% to 88%, but the maximum amount of ammonia removal was determined at ammonia concentration of 5 ppm and Lemna gibba residence time of 11 days in wastewater based on the quadratic model. Lemna gibba to surface ratio of 2:5 was measured at 96.449%. After optimization, validation of ammonia removal was performed under optimum conditions and measured at 92.07%. Based on the experimental design and the predicted under model conditions, the maximum amounts of ammonia removal percentage in the experiments were 82.84% and 88.33% respectively, indicating the high accuracy of the model to determine the optimum conditions for the ammonia removal from wastewater.
B. Te; B. Wichitsathian; C. Yossapol; W. Wonglertarak
Abstract
Mesoporous pellet adsorbent developed from mixing at an appropriate ratio of natural clay, iron oxide, iron powder, and rice bran was used to investigate the optimization process of batch adsorption parameters for treating aqueous solution coexisting with arsenate and arsenite. Central composite design ...
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Mesoporous pellet adsorbent developed from mixing at an appropriate ratio of natural clay, iron oxide, iron powder, and rice bran was used to investigate the optimization process of batch adsorption parameters for treating aqueous solution coexisting with arsenate and arsenite. Central composite design under response surface methodology was applied for optimizing and observing both individual and interactive effects of four main influential adsorption factors such as contact time (24-72 h), initial solution pH (3-11), adsorbent dosage (0-20 g/L) and initial adsorbate concentration (0.25-4.25 mg/L). Analysis of variance suggested that experimental data were better fitted by the quadratic model with the values of regression coefficient and adjusted regression coefficient higher than 95%. The model accuracy was supported by the correlation plot of actual and predicted adsorption efficiency data and the residual plots. The Pareto analysis suggested that initial solution pH, initial adsorbate concentration, and adsorbent dosage had greater cumulative effects on the removal system by contributing the percentage effect of 47.69%, 37.07% and 14.26%, respectively. The optimum values of contact time, initial solution pH, adsorbent dosage and initial adsorbate concentration were 52 h, 7, 10 g/L and 0.5 mg/L, respectively. The adsorption efficiency of coexisting arsenate and arsenite solution onto the new developed adsorbent was over 99% under the optimized experimental condition.