To examine the effects of different functionalization methods on adsorption behavior, anionic-exchange MIL-101(Cr) metal-organic frameworks (MOFs) were synthesized using preassembled modification (PAM) and postsynthetic modification (PSM) methods. Perfluorooctanoic acid (PFOA) adsorption results indicated that the maximum PFOA adsorption capacity was 1.19 and 1.89 mmol g(-1) for anionic-exchange MIL-101(Cr) prepared by PAM and PSM, respectively. The sorption equilibrium was rapidly reached within 60 min. Our results indicated that PSM is a better modification technique for introducing functional groups onto MOFs for adsorptive removal because PAM places functional groups onto the aperture of the nanopore, which hinders the entrance of organic contaminants. Our experimental results and the results of complementary density functional theory calculations revealed that in addition to the anion-exchange mechanism, the major PFOA adsorption mechanism is a combination of Lewis acid/base complexation between PFOA and Cr(III) and electrostatic interaction between PFOA and the protonated carboxyl groups of the bdc (terephthalic acid) linker.
The sinter structure and its characteristics mainly depend on the raw material chemistry, size, size distribution and the sintering process parameters. In sintering process heat is supplied by coke breeze in the sinter mix to raise the bed temperature to achieve partial fusion and diffusion bonding. Airflow rate and flame front speed in sintering process has been found to guide the performance of the sinter plant and these parameters mainly depends on the sinter bed permeability. The flame front speed (bed permeability) has been considered as one of the important operating parameter and it depends on several factors; the feed size of the sinter being one of the most important parameter among them. Since iron ore proportion is at higher side in the sinter mix, its size fraction is very important. JSW Steel sinter plant receives iron ore fines of -10 mm size from Bellary -Hospet region which consist of 3 to 9% bigger than 10 mm and 30 to 35% smaller than 0.15 mm size fraction. It is well known that larger particles favour diffusion bonding and smaller particles favour slag bonding in sintering process. Accordingly, the study of the assimilation characteristics of different size range iron ore has an important role to control the reactions in the sinter bed and to obtain the target mineral structure. Too much variation in coarser and finer particle size range in sinter mix, the behavior of these +10 mm and -0.15 mm particles have been a subject of investigation and it is necessary to understand the role of iron ore particle size on sinter microstructure, sinter strength, sinter RDI, and productivity. In present work pot grate sintering experiments have been carried out in laboratory with different level iron ore size (mean particle size from 1.22 to 3.95 mm) to understand the influence of iron ore mean particle size on mineralogy, productivity, physical and metallurgical properties of the sinter. Sinter productivity increased with increase in iron ore mean particle size due to increased flame front speed (FFS) and improved bed permeability with lower sintering time. Sinter with iron ore mean particle size of 2.59 mm (Classifier fines) yielded better sinter strength with lower fines (-5 mm) and lower RDI. Higher sinter strength is due to effective distribution of acicular silico ferrites of calcium and alumina (SFCA) phases. The improvement in sinter RDI is due to the change in proportion of magnetite and hematite phase with flame front speed.KEY WORDS: iron ore sinter; iron ore feed size; microstructure; productivity; sinter strength; sinter RDI.
Nitrogen (N) is a primary macronutrient essential for plant structures and metabolic processes, and the deficiency of N leads to critical plant disorders. The spectral reflectance can be used to predict the N status of plants using hyperspectral data. Therefore, the N status of wheat was predicted from hyperspectral data using machine learning techniques. Different derivative pre-processing treatments have been shown to have an impact on the spectral model performance. Therefore, we used different spectral pre-processing techniques (first derivative, deresolve and deresolve plus first derivative) coupled with six machine learning regression models (Support Vector Regression, Random Forest, k-nearest neighbours, Multilayer Perceptron, Gradient Boosting Regression and Partial Least Square Regression) to predict the N status of wheat. The deresolve plus first derivative spectral pre-processing technique along with Random Forest and Gradient Boosting Regression (R 2 > 0.85) were better than the other combination of spectral pre-processing and machine learning models to predict the N status of wheat. The eXplainable Artificial Intelligence (XAI) tool was used to provide the local and global explanations of the model decisions using SHapley Additive explanations (SHAP) values. The important wavelengths predicting N status were between 790 and 862 nm (global model) for Random Forest model. However, these wavelengths varied with the growth stages of wheat. The most important wavelength were 672, 794, 804, 806, 816 and 820 nm during the first six days of wheat growth (local model), 716, 794, 804 and 806 nm after 45 days of wheat growth, 724, 806, 820, 1556 and 1582 after 63-72 days of wheat growth and 718, 720, 724 and 1272 nm after 91-97 days of wheat growth. These results suggest that XAI tools are useful to explain the complex machine learning models related to hyperspectral data for remote monitoring of N status of wheat.
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