No abstract

In this work, liquid–liquid equilibrium (LLE) data for the ternary systems (water + propionic acid + solvent) were experimentally obtained at atmospheric pressure and 298.2 K. The ternary systems show type-1 behavior of LLE. Cyclopentane, cyclopentanol, 2-octanone, and dibutyl maleate were chosen as solvent and it has been noted that there are no data in the literature on these ternary systems. The consistency of the experimental tie-line data was checked using the Hand and Othmer-Tobias correlation equations. A comparison of the extracting capabilities of the solvent was made with respect to the distribution coefficients and separation factors. The correlation of the experimental tie-line data was confirmed by the NRTL thermodynamic model. A Group Method of Data Handling (GMDH)-type neural network (NN) was also used to correlate the experimental tie-lines. It is shown that the results of the both models cohere with the experimental values.

No abstract

Ð In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm de®ned in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is de®ned by the template matrices A, B and the template vector I. We have optimized weight coecients of these templates using Recurrent Perceptron Learning Algorithm (RPLA). The advantages of CNN as a real-time stochastic method are that it introduces little distortion to the shape of the original image and that it is not eected signi®cantly by factors such as the overlap of power spectra of residual ®elds. The proposed method is tested using synthetic examples and the average depth of the buried objects has been estimated by power spectrum analysis. Next the CNN approach is applied to magnetic data over the Golalan chromite mine in Elazig which lies East of Turkey. This area is among the largest and richest chromite masses of the world. We compared the performance of CNN to classical derivative approaches.

No abstract