Experimental validation of multi-objective optimization techniques for design of vacuum swing adsorption processes

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps

The current methods applied to material screening for adsorption-based heat pumps are based on a fixed set of temperatures or their independent variation, providing a limited, insufficient, and unpractical evaluation of different adsorbents. This work proposes a novel strategy for the simultaneous optimization and material screening in the design of adsorption heat pumps by implementing a meta-heuristic approach, particle swarm optimization (PSO). The proposed framework can effectively evaluate variable and broad operation temperature intervals to search for viable zones of operation for multiple adsorbents at once. The criteria for selecting the adequate material were the maximum performance and the minimum heat supply cost, which were considered the objective functions of the PSO algorithm. First, the performance was assessed individually, followed by a single-objective approximation of the multi-objective problem. Next, a multi-objective approach was also adopted. With the results generated during the optimization, it was possible to find which adsorbents and temperature sets were the most suitable according to the main objective of the operation. The Fisher-Snedecor test was applied to expand the results obtained during PSO application and a feasible operating region built around the optima, enabling the arrangement of close-to-optima data into practical design and control tools. This approach allowed for a fast and intuitive evaluation of multiple design and operation variables.

An Upper Bound Visualization of Design Trade-Offs in Adsorbent Materials for Gas Separations: CO2 , N2 , CH4 , H2 , O2 , Xe, Kr, and Ar Adsorbents

The last 20 years have seen many publications investigating porous solids for gas adsorption and separation. The abundance of adsorbent materials (this work identifies 1608 materials for CO2 /N2 separation alone) provides a challenge to obtaining a comprehensive view of the field, identifying leading design strategies, and selecting materials for process modeling. In 2021, the empirical bound visualization technique was applied, analogous to the Robeson upper bound from membrane science, to alkane/alkene adsorbents. These bound visualizations reveal that adsorbent materials are limited by design trade-offs between capacity, selectivity, and heat of adsorption. The current work applies the bound visualization to adsorbents for a wider range of gas pairs, including CO2 , N2 , CH4 , H2 , Xe, O2 , and Kr. How this visual tool can identify leading materials and place new material discoveries in the context of the wider field is presented. The most promising current strategies for breaking design trade-offs are discussed, along with reproducibility of published adsorption literature, and the limitations of bound visualizations. It is hoped that this work inspires new materials that push the bounds of traditional trade-offs while also considering practical aspects critical to the use of materials on an industrial scale such as cost, stability, and sustainability.

Identifying steep pareto fronts in multicomponent adsorption using a novel elliptical method

Multicomponent adsorption processes are affected by both mixture and process variables viz. feed composition, pH, adsorbent dosage, and adsorbent type. Optimization of multicomponent adsorption processes with multiple objectives is challenging. It is important to accurately identify possible solutions and select the compromise solution that best satisfies the different objectives. Conventional algorithms, when applied to multicomponent adsorption, were found to identify the Pareto front less accurately, thereby necessitating the need for a reliable method. The steep portion of the Pareto front was especially not captured satisfactorily by the different conventional algorithms such as pattern search (PS), Non-dominated Sorting Genetic Algorithm (NSGA-II), and Epsilon-Constraint (EC). This portion assumes importance, if the compromise solution occurs in its vicinity. To address these challenges, a novel bi-objective optimization technique termed as elliptical method (EM) was developed and described in this work. It involves an exhaustive search, provides a well distributed Pareto front, and clearly delineates the steep region. After validating with benchmark problems, EM was applied to batch multi-component adsorption. The two objectives optimized simultaneously were adsorbent loading and percentage removal of the different solutes. The Pareto front and the compromise solution involving the best combination of the two objectives were significantly superior in the elliptical method when compared to those obtained from typical algorithms including epsilon-constraint (EC) method. The Pareto front was also well defined by the elliptical method without discontinuities near the extreme and steep regions. The number of points found by EM in the steeper region for the grade II adsorbent was 10 times greater than those found by the EC method while the PS and NSGA could not delineate this portion. The average time taken (considering both adsorbents) for EM per solution was 0.17 s which was at least 30.6% faster than the other methods. The compromise solution with the elliptical method was superior to the other methods. For instance, with grade II adsorbent, the compromise solution from the elliptical method suggested operating conditions that led to a total adsorbent loading and percentage removal of 333.4 mg/g and 56.0%. On the other hand, pattern search gave 324.1 mg/g and 56.5%, whereas the NSGA-II method gave 321.9 mg/g and 53.3%. For this adsorbent, elliptical method's compromise solution was 50% and 20% closer in terms of the Euclidean distance to the utopia point than NSGA and PS methods, respectively. The elliptical method will facilitate reliable wastewater tertiary treatment taking into cognizance the utilization of the adsorbent as well as the percentage purity requirement.

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Radial Movement Optimization Based Optimal Operating Parameters of a Capacitive Deionization Desalination System

The productivity of the capacitive deionization (CDI) system is enhanced by determining the optimum operational and structural parameters using radial movement optimization (RMO) algorithm. Six different parameters, i.e., pool water concentration, freshwater recovery, salt ion adsorption, lowest concentration point, volumetric (based on the volume of deionized water), and gravimetric (based on salt removed) energy consumptions are used to evaluate the performance of the CDI process. During the optimization process, the decision variables are represented by the applied voltage, capacitance, flow rate, spacer volume, and cell volume. Two different optimization techniques are considered: single-objective and multi-objective functions. The obtained results by RMO optimizer are compared with those obtained using a genetic algorithm (GA). The results demonstrated that the RMO optimization technique is useful in exploring all possibilities and finding the optimum conditions for operating the CDI unit in a faster and accurate method.

Multi-Objective Optimization Applications in Chemical Process Engineering: Tutorial and Review

This tutorial and review of multi-objective optimization (MOO) gives a detailed explanation of the 5 steps to create, solve, and then select the optimum result. Unlike single-objective optimization, the fifth step of selection or ranking of solutions is often overlooked by the authors of papers dealing with MOO applications. It is necessary to undertake a multi-criteria analysis to choose the best solution. A review of the recent publications using MOO for chemical process engineering problems shows a doubling of publications between 2016 and 2019. MOO applications in the energy area have seen a steady increase of over 20% annually over the last 10 years. The three key methods for solving MOO problems are presented in detail, and an emerging area of surrogate-assisted MOO is also described. The objectives used in MOO trade off conflicting requirements of a chemical engineering problem; these include fundamental criteria such as reaction yield or selectivity; economics; energy requirements; environmental performance; and process control. Typical objective functions in these categories are described, selection/ranking techniques are outlined, and available software for MOO are listed. It is concluded that MOO is gaining popularity as an important tool and is having an increasing use and impact in chemical process engineering.