Effect of olive ripeness on chemical properties and phenolic composition of chétoui virgin olive oil

BACKGROUND: The aim of the present work was to investigate the influence of fruit ripening on oil quality in an attempt to establish an optimum harvesting time for Chétoui olives, the second main olive variety cultivated in Tunisia. RESULTS: Our results showed that many analytical parameters, i.e., peroxide value, UV absorbance at 232 and 270 nm, chlorophyll pigments, carotenoids and oleic acid contents decreased during ripening, whilst oil content and linolenic acid increased. Free acidity remained practically stable with a very slight rise at the highest maturity index. The trend of oxidative stability, total phenols and o‐diphenols, showed an increase at the early stages followed by a reduction at more advanced stages of maturity. The major phenolic compounds, such as hydroxytyrosol, ligstroside aglycon, elenolic acid, acetoxy‐pinoresinol and oleuropein aglycon, seemed to have the same behaviour. In the case of tyrosol, a strong decrease was observed directly related with the ripening progress. CONLUSION: On the basis of the evolution of the analytical parameters studied, the best stage of Chétoui olive fruits for oil processing seems to be at ripeness index higher than 2.0 and lower than 3.0. Copyright © 2009 Society of Chemical Industry     

Neural computation to predict in-flight particle characteristic dependences from processing parameters in the APS process

In-flight particle sensors for thermal spraying are used for real-time monitoring of coating manufacture. However, such tools do not offer facilities to tune the processing parameters when the monitoring reveals fluctuations or instabilities in the thermal jet. To complete the process control, any diagnostic sensors need to be coupled with a predictive system to separate the effect of each processing parameter on the in-flight particle characteristics. In this work, a nonlinear dynamic system based on an artificial neural network (ANN) model is proposed to play this role. It consists of a method that relates the processing parameters to the particle emitted signal characteristics recorded with a DPV2000 (TECNAR Automation, St-Bruno, QC, Canada) optical sensing device. In such a way, a database was built to train and optimize an ANN structure. The in-flight particle average velocity, temperature, and diameter of an alumina-13wt.%titania feedstock were correlated to the injection and power parameters. Correlations are discussed on the basis of these predictive results.     

Numerical modelling of rotating packed beds used for CO2 capture processes: A review

Over the last decades, renewable and clean energy sources are being rigorously adopted along with carbon capture technologies to tackle the increasing carbon dioxide (CO₂) concentration level in the environment. CO₂ capture is a quintessential option for tackling global warming issues. In this context, the present paper has reviewed the process intensification equipment called a rotating packed bed (RPB), which is highly industry applicable due to high gravity (HiGee) force. This facilitates strong mass transfer characteristics, a compact design, and low energy consumption. In this review, the current research scenario of RPBs using numerical, computational fluid dynamics (CFD), and mathematical modelling, along with different machine learning approaches in the CO₂ capture process, has been reviewed. The different geometry designs, hydrodynamic characteristics, performance parameters, research methods, and their effects on CO₂ removal efficiency have been discussed. Furthermore, the latest experimental studies are also summarized, especially in the absorption and adsorption domain. Finally, recommendations have been given to support the RPBs in different industrial and commercial applications of CO₂ removal.     

Machine learning-based clustering protocols for Internet of Things networks: An overview

The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML-based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology-centered ML-based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML-based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.