Fernando Rojas

ABSTRACT Adsorption of benzene, toluene, and p-xylene (BTX) on dealuminated clinoptilolites was investigated in the temperature range of 398−498 K by way of the inverse gas chromatography method. The dealuminated zeolites were prepared via a chosen number of acid treatments of natural clinoptilolite precursors with aqueous HCl. Both natural and acid-leached substrates were characterized by X-ray diffraction, N2 adsorption at 76 K, and BTX adsorption. The Freundlich, Langmuir, and Dubinin−Astakhov adsorption models were found to approximately fit the BTX adsorption data, within the selected temperature range. The uptake degree of these aromatic hydrocarbons was temperature-dependent. Additionally, the degree of interaction between these BTX vapors and the dealuminated clinoptilolite samples was examined through the evaluation of the isosteric heat of adsorption by employing the Clausius−Clapeyron equation.

The micropore-filling characteristics of a series of natural and modified microporous clinoptilolite-type zeolites with N2 at 76 K are measured and analysed. The adsorption behaviour of these substrata is examined in the range of relative pressures between 10−5–1. Several methods such as: Sing's αs-plots, de Boer's t-plots, Lee and Newnham's direct comparison plots, Dubinin's classical methods and a difference isotherm procedure proposed here, are used to assess the microporosity of the samples. Natural samples are used as reference materials to perform these sorption analyses of the modified samples. The effect of narrow micropore constrictions on the adsorption behaviour of clinoptilolites is explored experimentally. The occurrence of a low-pressure hysteresis loop along the sorption isotherm of a modified sample is frequently found and may be due to the strong adsorption of adsorbate molecules at the entrance of necked micropores that interfere with the diffusion of adsorbate molecules inside the porous structure of these zeolites.

ABSTRACT Adsorption of benzene, toluene, and p-xylene (BTX) on dealuminated clinoptilolites was investigated in the temperature range of 398−498 K by way of the inverse gas chromatography method. The dealuminated zeolites were prepared via a chosen number of acid treatments of natural clinoptilolite precursors with aqueous HCl. Both natural and acid-leached substrates were characterized by X-ray diffraction, N2 adsorption at 76 K, and BTX adsorption. The Freundlich, Langmuir, and Dubinin−Astakhov adsorption models were found to approximately fit the BTX adsorption data, within the selected temperature range. The uptake degree of these aromatic hydrocarbons was temperature-dependent. Additionally, the degree of interaction between these BTX vapors and the dealuminated clinoptilolite samples was examined through the evaluation of the isosteric heat of adsorption by employing the Clausius−Clapeyron equation.

The micropore-filling characteristics of a series of natural and modified microporous clinoptilolite-type zeolites with N2 at 76 K are measured and analysed. The adsorption behaviour of these substrata is examined in the range of relative pressures between 10−5–1. Several methods such as: Sing's αs-plots, de Boer's t-plots, Lee and Newnham's direct comparison plots, Dubinin's classical methods and a difference isotherm procedure proposed here, are used to assess the microporosity of the samples. Natural samples are used as reference materials to perform these sorption analyses of the modified samples. The effect of narrow micropore constrictions on the adsorption behaviour of clinoptilolites is explored experimentally. The occurrence of a low-pressure hysteresis loop along the sorption isotherm of a modified sample is frequently found and may be due to the strong adsorption of adsorbate molecules at the entrance of necked micropores that interfere with the diffusion of adsorbate molecules inside the porous structure of these zeolites.

The function approximation problem has been tackled many times in the literature by using radial basis function neural networks (RBFNNs). In the design of these neural networks there are several stages where, the most critical stage is the initialization of the centers of each RBF since the rest of the steps to design the RBFNN strongly depend on it. The improved clustering for function approximation (ICFA) algorithm was recently introduced and proved successful for the function approximation problem. In the ICFA algorithm, a fuzzy partition of the input data is performed but, a fuzzy partition can behave inadequately in noise conditions. Possibilistic and mixed approaches, combining fuzzy and possibilistic partitions, were developed in order to improve the performance of a fuzzy partition. In this paper, a study of the influence of replacing the fuzzy partition used in the ICFA algorithm with the possibilistic and the fuzzy-possibilistic partitions will be done. A comparative analysis of each kind of partition will be performed in order to see if the possibilistic approach can improve the performance of the ICFA algorithm both in normal and in noise conditions. The results will show how the employment of a mixed approach combining fuzzy and possibilistic approach can lead to improve the results when designing RBFNNs.

The use of Radial Basis Function Neural Networks (RBFNNs) to solve functional approximation problems has been addressed many times in the literature. When designing an RBFNN to approximate a function, the first step consists of the initialization of the centers of the RBFs. This initialization task is very important because the rest of the steps are based on the positions of the centers. Many clustering techniques have been applied for this purpose achieving good results although they were constrained to the clustering problem. The next step of the design of an RBFNN, which is also very important, is the initialization of the radii for each RBF. There are few heuristics that are used for this problem and none of them use the information provided by the output of the function, but only the centers or the input vectors positions are considered. In this paper, a new algorithm to initialize the centers and the radii of an RBFNN is proposed. This algorithm uses the perspective of activation grades for each neuron, placing the centers according to the output of the target function. The radii are initialized using the center’s positions and their activation grades so the calculation of the radii also uses the information provided by the output of the target function. As the experiments show, the performance of the new algorithm outperforms other algorithms previously used for this problem.