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Static headspace gas chromatography-ion mobility spectrometry (SHS GC-IMS) is a relatively new analytical technique that has considerable potential for analysis of volatile organic compounds (VOCs). In this study, SHS GC-IMS was used for the identification of the major terpene components of various essential oils (EOs). Based on the data obtained from 25 terpene standards and 50 EOs, a database for fingerprint identification of characteristic terpenes and EOs was generated utilizing SHS GC-IMS for authenticity testing of fragrances in foods, cosmetics, and personal care products. This database contains specific normalized IMS drift times and GC retention indices for 50 terpene components of EOs. Initially, the SHS GC-IMS parameters, e.g., drift gas and carrier gas flow rates, drift tube, and column temperatures, were evaluated to determine suitable operating conditions for terpene separation and identification. Gas chromatography-mass spectrometry (GC-MS) was used as a reference method for the identification of terpenes in EOs. The fingerprint pattern based on the normalized IMS drift times and retention indices of 50 terpenes is presented for 50 EOs. The applicability of the method was proven on examples of ten commercially available food, cosmetic, and personal care product samples. The results confirm the suitability of SHS GC-IMS as a powerful analytical technique for direct identification of terpene components in solid and liquid samples without any pretreatment. Graphical abstract Fingerprint pattern identification of terpenes and essential oils using static headspace gas chromatography-ion mobility spectrometry.

Arsenic contamination in water remains a critical global challenge, particularly in rural and resource-limited regions. Clays have been widely studied as cost-effective and efficient adsorbents for arsenic removal. This systematic review provides a comprehensive analysis of the application of clays in arsenic adsorption, focusing on clay types, operational units, and study methodologies. The review classifies the adsorption mechanisms, highlights key factors influencing adsorption performance—such as pH, ionic strength, and surface modifications—and examines the effectiveness of various modifications. Furthermore, the study categorizes adsorption research into kinetic, iso-thermal, thermodynamic, and efficiency studies, providing insights into the state of the art and the experimental conditions that govern arsenic removal. It also discusses the scalability and practical application of clay-based adsorption technologies, emphasizing gaps in field validation, regeneration studies, and large-scale implementation. The findings highlight the potential of natural and modified clays in arsenic remediation, while underscoring the need for further research to optimize adsorption conditions and enhance sustainability in water treatment systems.

Simulations of vapor-liquid equilibrium (VLE) are widely used given their impact on the scale, design, and extrapolation of different operational units. However, due to a number of factors, it is almost impossible to experimentally study each of the VLE systems. VLE simulations can be developed using representations that are strongly dependent on the nature and interactions of the compounds forming mixtures. A model that helps in predicting these interactions would facilitate simulation processes. A Gray Box Neural Network Model (GNM) was created as Binary Interaction Parameters predictors (BIP), which are estimated using state variables and information from pure components. This information was used to predict VLE behavior in mixtures and ranges not used in the mathematical formulation. The GNM prediction capabilities (including temperature dependency) showed an error level lower than 5% and 20% for mixtures considered and not considered in the training data, respectively.

This article presents an intelligent system for container stacking based on fuzzy logic. The method establishes a defined criterion for accepting or rejecting in real time an entry request to the stacking areas of the port in Valparaiso, Chile. A case study based on expert knowledge illustrates the proposed method with real data. First, the optimum solution is determined for a problem of maximization of entries, based on historical records from the traffic and information center of Valparaiso Port. Second, this solution is used to establish a strategy for making “the best possible decisions.” The combination of the optimization and the fuzzy results (which consider the type of cargo, prices, and capacity) is performed at two levels. First, the optimization results are used as feed for the fuzzy system to determinate a ratio of future acceptances. Second, the optimization results are compared to the fuzzy system results in order to estimate a parameter to establish the minimal percentage value for accepting a request. As a result, a proper use of the stacking area is achieved, which results in an increase of profits and revenue management.

Selection of optimal technologies for novel biobased products and processes is a major challenge in process design, especially when are considered many alternatives available to transform materials into valuable products. Furthermore, such technological alternatives vary in their technical performances and cause different levels of economic and environmental impacts throughout their life cycles. Additionally, selection of optimal production pathways requires a shift from the traditional materials management practices to more sustainable practices. This contribution provides a method for optimizing multi-product network systems from a sustainability perspective by applying the GREENSCOPE framework as a sustainable objective function. A case study is presented in which the four GREENSCOPE target areas (i.e., efficiency, energy, economics, and environment) are evaluated by 21 preselected indicators as part of a multi-objective optimization problem of a biojet fuel production network. The biojet fuel production network evaluated in this study consists of four main elements: (1) feedstocks management, (2) conversion technologies, (3) co-products upgrading, and (4) auxiliary sections for in situ production of raw materials and utilities. For the sustainability objective function, the 21 indicators are analyzed considering multiple perspectives of stakeholders to study their influence on the decision-making process. It is, different sets of weighting factors are assigned to each of the four target areas. Hence, this sustainability evaluation from different stakeholders’ perspectives allows identifying optimal networks, specific target areas with great potential for improvements, and processing steps with great influence in the entire network performance. As a result, diverse optimal network arrangements were obtained according to the multiple stakeholders’ perspectives. This evidences that a win–win situation for all sustainability aspects considered can hardly be reached. Finally, this contribution demonstrated the applicability of the proposed methodology for sustainability evaluation, optimization, and decision-making in the context of a multi-product material facility by developing a multi-objective optimization model.

Este trabajo presenta el control de un secador rotatorio directo basado en optimización no lineal. Esta metodología permite auto-sintonizar dinámicamente un controlador PI o PID, mejorando claramente el control del proceso respecto de la sintonización clásica. El control del proceso se realiza a través de simulación computacional vía Matlab y su verificación a través de un proceso piloto, lo que permite apreciar la potencialidad de la sintonía dinámica que se propone. Los resultados, tiempo de respuesta y dinámica de control posibilitan que investigación futura pueda escalar esta propuesta desde el laboratorio a procesos industriales.

Industry is at the forefront of adopting new technologies, and the process followed by the adoption has a significant impact on the economy and society. In this work, we focus on analysing the current paradigm in which industry evolves, making it more sustainable and Trustworthy. In Industry 5.0, Artificial Intelligence (AI), among other technology enablers, is used to build services from a sustainable, human-centric and resilient perspective. It is crucial to understand those aspects that can bring AI to industry, respecting Trustworthy principles by collecting information to define how it is incorporated in the early stages, its impact, and the trends observed in the field. In addition, to understand the challenges and gaps in the transition from Industry 4.0 to Industry 5.0, a general perspective on the industry's readiness for new technologies is described. This provides practitioners with novel opportunities to be explored in pursuit of the adoption of Trustworthy AI in the sector.

The significance of learning constraints from data is underscored by its potential applications in real-world problem-solving. While constraints are popular for modeling and solving, the approaches to learning constraints from data remain relatively scarce. Furthermore, the intricate task of modeling demands expertise and is prone to errors, thus constraint acquisition methods offer a solution by automating this process through learnt constraints from examples or behaviours of solutions and non-solutions. This work introduces a novel approach grounded in Deep Neural Network (DNN) based on Symbolic Regression that, by setting suitable loss functions, constraints can be extracted directly from datasets. Using the present approach, direct formulation of constraints was achieved. Furthermore, given the broad pre-developed architectures and functionalities of DNN, connections and extensions with other frameworks could be foreseen.

Las Simulaciones de Equilibrio Líquido Vapor (VLE) son ampliamente utilizadas dado su impacto en el escalamiento, diseño y extrapolación de diferentes operaciones unitarias. Sin embargo, dado considerable factores, es casi imposible experimentalmente estudiar cada uno de los sistemas de VLE. La simulación de VLE puede ser desarrollada utilizando representaciones que son fuertemente dependientes de la naturaleza e interacción de los compuestos que conforman la mezcla. Un modelo que ayude en la predicción de esas interacciones facilitará el proceso de simulación. Una Red Neuronal Gris (GNM) fue creada como un predictor de parámetros de interacción binaria, los que son estimados utilizando variables de estado e información de componentes puros. Esta información fue utilizada para predecir el comportamiento de VLE en mezclas y rangos no utilizados en la formulación matemática. Las capacidades predictivas del GNM (incluida la dependencia de temperatura) mostraron errores menores al 5% y al 20% para mezclas consideradas y no consideradas en los datos de entrenamiento, respectivamente.