The extraction of various metals by N,N,N 0 ,N 0 -tetraoctyldiglycolamide (TODGA) from nitric acid to ndodecane was investigated. It was obvious that divalent Ca (ionic radius: 100 pm), trivalent and tetravalent ions with ionic radii of 87-113 and 83-94 pm are highly extractable by TODGA. The limits of metal concentration (LOC) for Ca(II), Nd(III) and Zr(IV) in the extraction solvents, TODGA/n-dodecane, TODGA-DHOA(N,N-dihexyloctamide)/n-dodecane, TDDGA (N,N,N 0 ,N 0 -tetradecyldiglycolamide) and TDdDGA (N,N,N 0 ,N 0 -tetradodecyldiglycolamide) were determined. It is evident that LOC is influenced strongly by and increases with DHOA concentration and the length of alkyl chain attached to N atom of DGA.
In order to evaluate the extraction property of new extractants, diglycolamide (DGA) compounds, we investigated the maximum extraction of di-, tri-, and tetravalent metal ions using nitric acid and n-dodecane. The limits of metal concentration (LOC) for Ca(II), Nd(III) and Zr(IV) in the organic phase are strongly influenced by HNO3 and the extractant concentration. For the purpose of enhancing the LOC value, we employed a modifier of the solvent, N,Ndihexyl-octanamide (DHOA) and DGA with a long alkyl chain, and examined the results. It was evident that LOC increased with the DHOA concentration and the length of the alkyl chain attached to the N atom of DGA. The stoichiometric values of LOC(Zr) estimated from the extraction reaction were confirmed by using the extraction condition: tetraoctyl-DGA/1 M DHOA + n-dodecane and 3 M HNO3.
Data classification exists in various practical applications, such as the classification of words in natural language processing, classification of meteorological conditions, classification of environmental pollution degree, and so on. Artificial neural network is a basic method of data classification. A reasonable optimization algorithm will get better results for a loss function in the neural network. The research and improvement of these optimization algorithms has been a focus in this field. Because of the various optimizers developing in building the neural networks, an improved NAdam Algorithm (RNAdam) is proposed in this paper, on the basis of discussing and comparing several Algorithms with Adam Algorithm. This algorithm not only combines the advantages of RAdam algorithm, but also keeps the convergence of NAdam algorithm. A classification experiment is carried out on the data set composed of 300 sample points generated by the Make moon function. The experimental results show that the RNAdam algorithm is better than SGDM, Adam and Nadam algorithm in terms of the loss and accuracy between the output and the actual results, when the data are classified by the three-layer neural network. Therefore, the classification effect will be improved when this algorithm is applied to neural network for various practical data classification problems.
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