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.

In this work, the synthesis, design and optimization of a quaternary double dividing wall distillation column (QDDWC) is presented. The effect of the feed composition over the performance of this intensified configuration is studied. The synthesis and design of the QDDWC takes place using as basis a conventional direct sequence for the separation of a n-butane/n-pentane/n-hexane/n-heptane mixture. The column is tested for three molar feed compositions: 40/10/10/40, 25/25/25/25, and 10/40/40/10. The configurations are optimized through a multiobjective genetic algorithm to simultaneously minimize the total heat duty and the number of stages. According to the results, the proposed structure allows savings in heat duty up to 59% but requiring up to 28% more stages than the conventional sequences.

The wine industry generates large volumes of organic effluents, whose inadequate management poses significant environmental challenges but also offers opportunities for resource recovery. In this work, an integrated biorefinery scheme for the valorization of winery effluents is proposed and evaluated through steady-state simulation in Aspen Plus®. The biorefinery converts winery wastewater into a portfolio of value-added chemicals and biofuels, including levulinic acid, propylene glycol, formic acid, light gases, naphtha, sustainable aviation fuel, green diesel, and bioethanol, while enabling water recovery and carbon dioxide management. Two alternative CO2 capture routes are analyzed and compared: a conventional CaO-based carbonation–calcination process and an innovative absorption system using deep eutectic solvents (DES), specifically choline chloride–urea. Technical performance is assessed through chemical oxygen demand (COD) removal, recovery, conversion, yield, and product mass ratios. Economic feasibility is evaluated using profit-based indicators, while environmental performance is quantified through CO2-equivalent emissions associated with utility consumption. Results show that the proposed biorefinery achieves a COD removal efficiency of 99.99%, producing treated water compliant with Mexican regulations for internal reuse. The DES-based configuration reduces raw material costs by 48.86%, enables hydrogen recovery as an additional valuable product, and increases overall profit by 2.86% compared to the CaO-based scheme. Although the relative reduction in total CO2 emissions is modest (˜0.5%), the DES configuration achieves an absolute annual reduction of 2, 198 t CO2. Overall, the results demonstrate that integrating DES-based CO2 capture into winery effluent biorefineries enhances economic performance and supports circular economy principles through waste valorization, water reuse, and emissions mitigation.

Process intensification (PI) has emerged as a promising design philosophy to enhance the performance, compactness, and integration of energy storage systems. However, its application to electrochemical, thermal, and mechanical storage technologies is often fragmented and lacks quantitative benchmarking and systematic discussion of trade-offs. This review provides a critical, system-level assessment of PI strategies in energy storage, focusing on physically grounded mechanisms, realistic performance gains, and practical limitations. Reported PI implementations show power density increases of 2–5 ×, round-trip efficiency improvements of 5–20%, and system volume reductions of 20–60%, driven by enhanced heat and mass transfer, functional integration, and compact architectures. These benefits are frequently accompanied by penalties, including increased thermal gradients (10–15 K cm⁻¹), higher pressure drops (50–200%), and capital cost increases of 10–30%, which may limit robustness and scalability. Additionally, the review discusses the implications of process intensification for sustainability and circular economy-oriented energy storage development, including potential benefits associated with resource efficiency, lifetime extension, compact multifunctional architectures and reduction of material intensity, together with challenges related to recyclability, system integration and end-of-life management. Key challenges related to scale-up, benchmarking, and system integration are discussed, positioning PI as a context-dependent engineering framework requiring careful techno-economic balancing.

This work performed a sensitivity analysis based on a conventional extractive distillation system to thoroughly evaluate the cost of separating bioethanol from water. The analysis considers the compositions and production volumes that are likely to result from the fermentation process of various biorefineries, regardless of their specific generation. It also outlines how the cost of bioethanol purification decreases as the ethanol concentration in the fermentation broth increases. For each composition-flow point in a gridded workspace, a distillation train was designed using the Aspen Plus (R) simulation framework, focusing on minimizing the total annual cost. The results are discussed graphically, illustrating total annual costs and specific column costs in relation to feed stream composition and inflow. The findings quantitatively demonstrate that the cost of separation per mass unit of anhydrous ethanol decreases with higher inflow and increased input ethanol concentration. Additionally, it is evident that the primary cost is associated with the preconcentrator column.

This work aims to shorten the time of lager beer fermentation through a temperature profile determined by a model-based controller, as an exploratory proposal to reduce fermentation time while maintaining yeast viability and process performance, without compromising the fermentation dynamics or negatively affecting the yeast activity. This study was developed from an engineering perspective focused on the optimization of the beer fermentation process through model-based control, preserving the beer properties of the original process. This exploratory work was carried out in four stages: (1) performance of constant temperature fermentations of a lager-type beer where concentrations of yeast and ethanol were monitored along the process, (2) model parameters adjustment and validation of a beer fermentation mathematical model on the basis of data obtained from experiments, (3) outline of a temperature trajectory, in a simulation framework, from an ethanol controller of movable convergence rate constructed with a nonlinear technique and the mathematical model, (4) experimental implementation of the outlined temperature trajectory in the beer fermentation. Beer batches’ quality-control endpoints suggested by Mexican quality standards frameworks, such as fermentation time, alcoholic and caloric content, and fermentation efficiency, were analyzed. The lag stage was reduced when the temperature profile devised by the controller was employed, resulting in a reduction in the time required to reach the stationary stage. No significant final characteristic variations in bottled beers brewed at constant and variable temperatures were identified. The quality assessment of the analyzed variables was conducted in accordance with the measurement capabilities of the employed equipment and under the applicable Mexican quality standards framework. This proposal presents an alternative systematic strategy to reduce the fermentation time of lager beer, favoring the efficiency and profitability of craft beer production.

In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors—such as pH and nutrient availability—influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying pH conditions and substrate compositions. The yeast strain Candida guilliermondii was selected as the model microorganism due to its industrial relevance. Experimental growth data were collected through optical density measurements using a Multiskan™ FC Microplate Photometer (Thermo Scientific), covering a pH range from 6.0 to 8.5 and two substrate scenarios: pure xylose, and a 1:2 glucose–xylose mixture. The experimental data were used to train the MLP neural network, which generated predictive models capable of estimating growth behavior under the specified input conditions. This modeling approach enables the simulation of microbial growth curves at any given time point within the defined parameter space, providing a more flexible and comprehensive tool compared to classical models. The results of this study demonstrate that the proposed MLP-based model is a powerful computational tool for both the design and real-time control of bioprocesses. The integration of these tools into predictive control strategies is a key aspect of the bioprocess research. The model's versatility and its ability to predict microbial behavior make it a very important tool for bioreactor control.

Notwithstanding its low thermodynamic efficiency, distillation is among the most used separation processes in industry. As a consequence of the high energy consumption and the related environmental impact the research of more efficient alternatives remains one of the main interests in process engineering. Dividing-wall configurations are a well-known alternative to reduce the energy requirement of ternary distillation. Nevertheless, the design of dividing-wall columns for the separation of quaternary mixtures represents a challenge due to the number of possible configurations. In this work, a synthesis and design procedure is proposed for a double dividing-wall structure to separate quaternary mixtures. The performance of the intensified scheme is analyzed in terms of energy requirements, total annual cost, CO2 emissions and thermodynamic efficiency. Due to the complexity of the system, after initial synthesis the structure is optimized through a meta-heuristic approach. The direct synthesis procedure leads to a dividing-wall system with higher energy requirements than the conventional sequences. However, after the rigorous optimization, savings in reboiler duty of 14.3% are obtained, with a reduction in TAC of 6.3%.

ABSTRACT Ethanol dehydration is a crucial task in the production of bioethanol for fuel applications. Such separation represents a challenge due to the presence of the ethanol‐water azeotrope. The effective removal of water from the ethanol‐water mixture beyond the azeotropic composition is a task requiring the development of advanced technologies, which is directly associated with the application of the process systems engineering principles. In this review, advanced technologies for the separation of the ethanol/water mixture are discussed, including distillation‐based systems, adsorption, and membrane‐based schemes. The advantages and areas of opportunity for such technologies are described, highlighting the role of process intensification as a strategy to enhance the performance of the purification units for ethanol dehydration.

El glicerol se puede obtener a partir en los procesos de producción de biodiésel, en constante aumento. Como ejemplo, la producción mundial de biodiésel en 2021 generó más de 4.5 billones de litros de glicerol. Debido al exceso en el volumen de glicerol producido, se le ha llegado a considerar como un inconveniente financiero y medioambiental para la industria del biodiésel, surgiendo la necesidad de buscar alternativas para aprovechar el glicerol como una fuente renovable para la obtención de productos químicos que representen un considerable beneficio económico. Por otra parte, México cuenta con un gran potencial para producir biodiésel a partir de aceites de palma africana, higuerilla y Jatropha, estimándose un potencial de producción de hasta 368 millones de litros de biodiésel para el año 2030. Por ello, en este artículo se presentan algunos de los retos y oportunidades que ofrece la valorización del glicerol en México, un país que busca impulsar el uso de biocombustibles y desarrollar una industria química verde y sustentable.

Biodiesel market in Mexico still faces some challenges due to its high production costs. An additional challenge that may arise is the consequent excessive production of glycerol. Therefore, the glycerol valorization into high value-added chemical products is necessary to enhance the economic potential of the biodiesel industry. In this work, a mathematical model representing the supply chain for the valorization of crude glycerol from the biodiesel industry in Mexico is developed. The proposed model involves operational and logistical decisions such as the selection of high value-added chemical products, biorefinery locations, and production volumes. Furthermore, the model analyzes the impact of variations in selling prices, product demand, and the availability of glycerol on the optimal supply chain configuration. According to the results, the maximum profit implies only the production of lactic acid (LA) in Mexico City and the state of Nuevo Leon, satisfying less than 4

Process intensification has revolutionized chemical process design by integrating reaction and separation, enhancing efficiency, reducing energy consumption, and promoting sustainability. However, these advancements introduce significant control challenges due to increased process complexity, nonlinear interactions, and dynamic constraints. Over the past 25 years, conventional control strategies have been progressively replaced by predictive, adaptive, and data-driven methods, which are better suited for managing multivariable interactions and real-time optimization. The widespread adoption of predictive control frameworks has improved stability, reduced response times, and enhanced energy efficiency in reactive and extractive distillation, dividing-wall columns, and hybrid separation processes. Furthermore, integrating intelligent decision-making tools has enabled real-time adaptability, ensuring robust performance under fluctuating operating conditions. The emergence of hybrid control strategies, which combine predictive models with data-driven learning techniques, has further enhanced the ability to address nonlinearities and process uncertainties. This shift underscores a transition toward more intelligent and sustainable process operations, where control systems not only optimize efficiency but also minimize emissions and improve resource utilization. As process intensification continues to advance, future research should focus on scalable, autonomous, and computationally efficient control solutions to ensure operational reliability and economic feasibility in sustainable chemical manufacturing.

Evolutionary algorithms, which emulate natural selection and species evolution, have long been applied to process optimization in chemical engineering. While these methods have demonstrated robustness to various optimization challenges, their computational requirements escalate with increasing case study complexity. This paper investigates the application of the Boltzmann Univariate Marginal Distribution Algorithm (BUMDA) as an optimization tool for distillation processes. BUMDA is a distribution estimation algorithm (EDA) based on the Boltzmann distribution, characterized by its alignment to the optimal value of the fitness function. The performance of BUMDA is benchmarked against Differential Evolution (DE), a widely adopted algorithm in chemical engineering optimization. Both algorithms are coupled with a self-adaptive constraint handling technique. The optimization objective is to minimise the total heat input in three different distillation systems while satisfying purity and recovery constraints. Results indicate that BUMDA outperforms DE, yielding superior solution quality, reduced computational complexity and lower computing time. Furthermore, BUMDA effectively avoids local minima entrapment. A statistical comparison of the algorithms using bootstrap test, confirms the enhanced performance of BUMDA over DE.

In this work, the convergence time of the Extremum Seeking Control applied to ensure the optimal performance of a distillation column is studied and diminished in such a way that it is comparable to the one of a linear PI controller. The distillation column is one of the trays in continuous operation that separates an ideal binary mixture. Firstly, a sensitive-type analysis of the convergence time with respect to the ESC tuning parameters is carried out, locating the values for which the convergence time is minimum; next, a variation of the ESC is applied, which consists of the addition of a decreasing function to initially speed up the ESC performance, and a saturation block is also added to constrain likely large changes in the control inputs. The control problem is one of regulation, for which, for validation purposes, the optimal conditions for the testing cases are determined by a conventional sensitivity analysis of the output with respect to the inputs. The testing runs show not only the effectiveness of the ESC but also that the modified ESC has a reduced convergence time compared to the typical ESC, and it is even comparable to that of a linear PI controller.

The continuous increase in the production of biodiesel has caused the generation of a large amount of crude glycerol, negatively impacting the economics of the biodiesel production process. Glycerol can be used as a platform molecule to obtain high value-added chemical products. Such an approach allows valorizing the glycerol obtained as by-product in the production of biodiesel. This study proposes four intensified processes to produce renewable propylene glycol (PG) by implementing thermally coupled distillation and reactive distillation systems. The proposed configurations are optimized using the total annual cost as objective function. Compared to the conventional scheme (CS), the intensified alternatives significantly reduce the total annual cost, in a range of 12.2 % to 19.6 %. Additionally, a side stream reactive distillation scheme (SSRDS) enables the production of high-purity PG within a single unit, achieving a 65 % reduction in energy intensity compared to the conventional scheme.

Ethanol derived from lignocellulosic sources is an important chemical that can have several uses such as biofuel that can be blended with gasoline, as sanitizer for the disinfection of surfaces, or as additive in the food, cosmetic and pharmaceutical industry. Nevertheless, its separation poses a challenge because it must be separated from water and this mixture forms an azeotrope. This research focuses on the ethanol dehydration using magnesium chloride (MgCl2) as a mass separating agent, which is an innocuous chemical used in the food industry to process Tofu. Therefore, it does not have any side effect, or it is not harmful for humans. Therefore, the use of this salt makes possible to use the purified ethanol as an additive in the food, cosmetic, or pharmaceutical industry. The proposed separation process is optimized using surrogate nonlinear models for the preconcentration and salt evaporation units. The results showed that using magnesium chloride dodecahydrate is a feasible alternative to bioethanol valorization.

The net-zero coalition looks for worldwide commitment to reach net zero CO2 emissions for 2050. Several strategies must be implemented to reach this target, being the production and use of sustainable biofuels are one of the most promissory. Sustainable biofuels can significantly reduce CO2 emissions during their complete life cycle, where the raw material and the production process stand out; the first one must absorb CO2 as much as possible during its growth, while the second one must release as minimum as possible. In this context, microalgae have many advantages, such as high growth rate and high CO2 absorption. Thus, the use of microalgae for biofuel production is attractive, but its use at industrial scale is not common. Therefore, this chapter presents a revision of the microalgae culture as well as its conversion to biofuels. After, future trends related to the use of microalgae for biofuel production are discussed.

This paper presents a numerical analysis of liquid-vapor equilibrium in a sieve tray of a distillation column using the Smoothed Particle Hydrodynamics (SPH) method. This Lagrangian approach provides a comprehensive understanding of the hydrodynamics, heat transfer, and liquid-vapor interactions within the tray, considering variations in deck area (85%, 90%, and 95%). The study examines flow patterns, flow regimes, weeping phenomena, and heat transfer within the tray. Results indicate that with a reduced deck area, the bubble regime predominates, leading to higher weeping rates and lower temperature uniformity between phases. Conversely, increasing the deck area to 90% or 95% shifts the regime to steam jet and spray, reduces weeping, and enhances phase interaction, thereby improving heat transfer and equilibrium stage efficiency. The study also highlights the effectiveness of the SPH method in simulating complex flow behavior within sieve trays.