Julián Cabrera-Ruiz

Significant advances are being made in the development of self‐healing polymers; however, most studies focus on thermally induced repair mechanisms or on encapsulating repair agents within the polymer matrix. This research explores a novel self‐healing hybrid polymer coating incorporating cerium oxide (CeONP) and zinc oxide (ZnONP) nanoparticles into a polymer matrix (with β‐cyclodextrin). The nanoparticles enhance the material's mechanical and self‐repairing properties. The nanoparticles, polymer matrix, and composites are characterized using XRD, SEM, XPS, and NMR techniques. Properties such as adhesion (5B adhesion for all systems), density, and contact angle are evaluated in accordance with ASTM standards. Shape recovery under UV light and the behavior of the composite are investigated after mechanical deformation and exposure to saturated NaCl medium. Notably, WPU containing 0.5% CeONP exhibits enhanced corrosion resistance, with electrochemical impedance increasing from 3.71 kΩ·cm ² post‐corrosion to 16.38 kΩ·cm ² after 3 h of UV treatment (the originally set value is 16.61 kΩ·cm ² , corresponding to 98.6% recovery). The self‐healing ability is evaluated under UV exposure, showing an impedance recovery of up to 80% in the presence of β‐cyclodextrin (β‐CD) and nanoparticles, attributed to hydrogen‐bonding interactions. This study presents UV‐induced self‐healing coatings with strong potential for durable, corrosion‐resistant industrial applications.

Ethanol derived from biomass is a promising renewable fuel; however, its long-term use as a gasoline additive is becoming increasingly uncertain due to the rise of electric vehicles and alternative propulsion technologies. This trend motivates the exploration of higher-value applications for ethanol, particularly in the food and pharmaceutical sectors, where product safety is critical. A key challenge in ethanol purification is breaking the ethanol–water azeotrope, as conventional entrainers such as ethylene glycol or glycerol can leave residual traces that limit ethanol’s use in sensitive markets. Magnesium chloride (MgCl2) offers an effective alternative, enabling high-purity ethanol without introducing hazardous organic residues, while exhibiting favorable hygroscopic properties and operational reliability. Simulating this system is challenging due to strong non-ideal and electrolyte interactions in phase equilibrium. Conducting a rigorous controllability analysis is also difficult; therefore, within an Integrated Design and Control (IDC) framework, accurate approximations are essential for successful implementation. This work presents a multi-objective optimization of a MgCl2-based ethanol dehydration column, simultaneously minimizing the Total Annual Cost (TAC) and the open-loop controllability criterion (A_gamma + gamma_sm). The optimization integrates Aspen Plus® with Python and employs validated surrogate cost models for the preconcentrator and salt concentrator processes. Dynamic boundaries ensure product purity by maintaining the ethanol/water feed below the salt feed. The ASF (Achievement Scalarizing Function) solution achieved a 64.33% improvement in controllability with a 13.37% increase in TAC relative to the reference case reported in the literature, illustrating the trade-off between economic and control objectives. This study demonstrates that incorporating simplified controllability metrics into multi-objective optimization enables the efficient and practical design of complex systems, providing a viable approach for managing cost–control trade-offs in processes with challenging dynamics.

New research on complex intensified distillation schemes has popularized the use of several commercial process simulation software. The interfaces between process simulation and optimizationoriented software have allowed the use of rigorous and robust models. This type of optimization is mentioned in the literature as "Black Box Optimization", since successive evaluations exploits the information from the simulator without altering the model that represents the given process. Among process simulation software, Aspen Plus® has become popular due to their rigorous calculations, model customization, and results reliability. This work proposes a comparative study for Aspen Plus software and Microsoft Excel VBA®, Python® and MATLAB® interfaces. Five distillation schemes were analyzed: conventional column, reactive column, extractive column, column with side rectifier and a Petlyuk column. The optimization of the 𝑇𝑇𝑇𝑇𝑇𝑇 (Total Annual Cost) was carried out by a modified Simulated Annealing Algorithm (m-SAA). The evaluation criteria are the time per iteration (𝑇𝑇𝑇𝑇) and 𝑇𝑇𝑇𝑇𝑇𝑇 values. The results indicate that the best option to carry out the optimization was by using the VBA interface, however the one carried out with Python did not differ radically (12%).

Intensified schemes, such as reactive distillation, have been proposed to produce silane (SiH4). Several studies have been carried out around this intensified scheme focusing directly on its improvement in energy or economic criteria. However, these mentioned criteria do not ensure that the scheme is also optimal from the control point of view. There is a direct compromise between the economic criterion and the control criterion. Thus, the best controllable scheme is not necessarily the most economical and vice versa. Analyses have been proposed to evaluate the controllability of steady-state processes using open-loop with Singular Value Decomposition (SVD) under quantitative a criterion such as Aγ + γsm with simplified first-order transfer functions. This work considers four feasible designs with multiple reactive zones and evaluates their controllability from their open-loop dynamic responses obtained from Aspen Dynamics® by calculating the condition number for different frequency ranges analyzing qualitative aspects and the Aγ + γsm criterion evaluating the controllability of each scheme and the conditions of the simplification of their dynamic responses. For this, simplifications of first and second order transfer function models are compared with the models that robustly predict their behavior. The results of this study show that first order functions, despite their simplicity, can predict well the complex model of these distillation schemes. However, for low frequencies (i.e. ω < 10 rad/h) second order functions are better options because they capture the overshooting (damping) effect. The proposed methodology can be applied in global optimization with controllability as an additional criterion in the objective function.

En respuesta a la creciente preocupación por los problemas ambientales y la necesidad urgente de reducir las emisiones de gases de efecto invernadero. Industrialmente una de las formas actuales para minimizar los impactos de la industria es la creación de procesos intensificados, nuestro equipo ha desarrollado una investigación centrada en la optimización del proceso de captura de dióxido de carbono (CO2). Utilizando un algoritmo genético y el software de simulación Aspen Plus, diseñamos una planta que permite la absorción eficiente de CO2 mediante soluciones acuosas de monoetanolamina (MEA). Este enfoque busca mitigar los altos costos energéticos asociados con los métodos convencionales de captura de CO2, proporcionando una solución más viable desde el punto de vista técnico y económico. Nuestro trabajo se basa en la implementación de dos columnas de destilación, una para la absorción y otra para la desorción del CO2, optimizando cada etapa del proceso para maximizar la eficiencia de captura y minimizar el consumo energético. La metodología aplicada incluyó la especificación detallada de los componentes y reacciones involucrados, así como la configuración de los equipos principales en la simulación. Los resultados obtenidos demuestran una mejora en la eficiencia energética del proceso, subrayando la relevancia de la investigación en el contexto de las políticas ambientales actuales y su potencial aplicación industrial.

This Process Synthesis and Intensification (PS+I) framework uses a low-aggregation superstructure to solve the synthesis problem, and its solution is interpreted and translated into a task-integrated intensified process. Then, the process is post-optimized to find a better balance between operation and equipment costs. The solution leads to novel and counterintuitive intensified processes with low cost and energy requirements.