The classification of fresh fruits according to their visual ripeness is typically a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques to automate this process. Machine learning techniques such as artificial neural networks, support vector machines, decision trees, and K-nearest neighbor algorithms have been successfully applied for classification problems in the literature, particularly for images of fruit. However, the particularities of each classification problem make it difficult, if not impossible, to select a general technique that is applicable to all types of fruit. In this paper, combinations of four machine learning techniques and three color spaces (RGB, HSV, and L*a*b*) were evaluated with regard to their ability to classify Cape gooseberry fruits. To this end, 925 Cape gooseberry fruit samples were collected, and each fruit was manually classified into one of seven different classes according to its level of ripeness. The color values of each fruit image in the three color spaces and their corresponding ripening stages were organized for training and validation following a 5-fold cross-validation strategy in an iterative process repeated 100 times. According to the results, the classification of Cape gooseberry fruits by their ripeness level was sensitive to both the color space and the classification technique used. The models based on the L*a*b* color space and the SVM classifier showed the highest f-measure regardless of the color space, and the principal component analysis combination of color spaces improved the performance of the models at the expense of an increased complexity.

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space.

The pressure swing adsorption (PSA) process, is a novel method for the purification and production of bioethanol. A highly non-linear rigorous model is implemented to simulate the cyclic dynamics of PSA, achieving purity of 99% wt of ethanol, which meets international standards to be used as fuel. The contribution of this work focuses on obtaining an identified model capable of capturing the important dynamics of the PSA process (with a fit above of 90%) and to be used for controller design purposes, since it is very complicated to design control in highly nonlinear models that are represented with partial differential equations (PDE). There is a comparison between a Hammersetein-Wiener (HW) and an Artificial Neural-Network (ANN) using the same input and output signals. Both identified models capture the important dynamics of the rigorous PSA model.

Adsorption processes are characterized by their kinetics and equilibrium isotherms described by mathematical models. Nowadays, adsorption with molecular sieves is a method used to separate certain elements or molecules from a mixture and produce hydrogen, nitrogen, oxygen, ethanol, or water treatment. This study had two main objectives. The first one was focused on the use of different natural (Clinoptilolite-S.L. Potosi, Clinoptilolite-Puebla, and Heulandite-Sonora) and synthetic (Zeolite Type 3A) adsorbents to separate the mixtures $H_{2}O/H_{2}S{O}_4$ and $H_{2}O/C_2{H}_{5}OH$ . It was determined that both Zeolite Type-3A and Heulandite-Sonora have greater adsorption capacity in a shorter time compared with the Clinoptilolites at different temperatures. The second objective was the simulation of a pressure swing adsorption process to dehydrate ethanol using the parameters obtained from Zeolite Type 3A (with maximum adsorption capacity). Several configurations were considered to calculate the appropriate nominal values for the optimal process. The results illustrate that the purity of ethanol is increased when the following parameters are considered in the adsorption process: a high pressure, a constant temperature between 100 and 120 ${}^{°}$ C, a feed composition near the azeotropic point with lower water content, and a purge pressure near the vacuum. Finally, the results show that it is possible to take advantage of the length of the absorber bed in order to reduce the energy costs by increasing the ethanol production as well as complying with the international purity standards.