دانلود رایگان مقاله انگلیسی مقایسه رگرسیون خطی چندگانه و روش گروهی مدل رسیدگی به اطلاعات چوب درخت بلوط - الزویر 2018

Abstract

 Activated carbon from oak tree is used as adsorbent for the removal of noxious anionic dye sunset yellow. The prepared adsorbent is characterized using X-ray diffraction, Scanning  Electron microscopy equipped with Energy-Dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy. In addition to this, parameters like initial concentration,  adsorbent dosage, contact time, pH, and particle size on the uptake of SY dye from wastewater is  well investigated and optimized. For maximum adsorption, the initial concentration of 10 mg/L; adsorbent dose of 0.25 g; pH =1; contact time= 35 min and particle size=150-250 µm is found to be optimal value. The adsorption isotherm data at different adsorbent dosage of 0.05- 0.25 g is in agreement with the Langmuir isotherm having Qmax = 5.8377- 30.1205 mg/g. On the other hand, models like, Group Method of Data Handling and multiple linear regression were used to forecast of the removal efficiency of noxious anionic dye sunset yellow and from results, it is specified that the GMDH model possess a high performance than MLR model for forecasting removal percentage of SY dye. Hence, activated carbon from oak tree can be efficiently used as adsorbent for the removal of SY dye from wastewater.

4. Conclusions

 Biomaterial of Oak Tree is used for the preparation of AC and the prepared inexpensive adsorbent is characterized using SEM, XRD and FTIR. The prepared adsorbent is 26% crystalline in nature and 74 % amorphous in nature. Effective parameters were investigated and optimized and it was observed that for maximum adsorption, the initial concentration of 10 mg/L; adsorbent dose of 0.25 g; pH =1; contact time= 35 min and particle size=150-250 µm is found to be optimized. Comparison of the MLR and GMDH models showed the good predictability of GMDH model for forecasting removal percent of SY dye. The optimal parameters for GMDH model were found to be 100 maximum number of layers, 6 maximum number of neurons in a layer, 0.1-selection pressure and 0.7 train ratio. For training points, the values of R2 of 0.9785 and MSE of 2.8362 were obtained using the optimal GMDH model. The results presented that the soft computing model is a good tool for predicting adsorption process.