Multi-objective optimisation using surrogate models for the design of VPSA systems

Optimization and Recovery of a Pressure Swing Adsorption Process for the Purification and Production of Bioethanol

Today, there are new technologies to produce bioethanol: one of them is the Pressure Swing Adsorption (PSA) process. This process has displaced other separation technologies due to the use of natural adsorbents and its methodology to obtain high purities with a lower energy cost. The aim of this work focuses on the optimization of the PSA process (experimental case) to obtain a higher recovery and production of bioethanol using lower energy consumption. The results are favorable since the energy cost is reduced to a range of 60% and 62%, obtaining purities above 99% wt of ethanol and recovery between 75% and 77.41%. The bioethanol produced and purified in the different scenarios meets international standards to be used as a fuel or oxygenating additive.

A Review of Numerical Research on the Pressure Swing Adsorption Process

The pressure swing adsorption (PSA) process has been considered a promising method for gas separation and purification. However, experimental methods are time-consuming, and it is difficult to obtain the detailed changes in variables in the PSA process. This review focuses on the numerical research developed to realize the modelling, optimization and control of the cyclic PSA process. A complete one-dimensional mathematical model, including adsorption bed, auxiliary devices, boundary conditions and performance indicators, is summarized as a general modelling approach. Key simplified assumptions and special treatments for energy balance are discussed for model reliability. Numerical optimization models and control strategies are reviewed for the PSA process as well. Relevant attention is given to the combination of deep-learning technology with artificial-intelligence-based optimization algorithms and advanced control strategies. Challenges to further improvements in the adsorbent database establishment, multiscale computational mass transfer model, large-scale PSA facility design, numerical computations and algorithm robustness are identified.

Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water

The machine learning techniques are capable of predicting virtual material design space and optimizing material fabrication parameters. In this article, we construct machine learning models to describe the photoelectrochemical properties of molecularly engineered halide perovskite materials based on CH₃NH₃PbI₃ in an aqueous solution and predict a complex multidimensional design space for the halide perovskite materials. The machine learning models are trained and tested based on an experimental photocurrent data set consisting of 360 data points with varying experimental conditions and dye structures. Machine learning algorithms including support vector machine (SVM), random forest, k-nearest neighbors, Rpart, Xgboost, and Kriging algorithms are compared, with the Kriging algorithm achieving the best accuracies (r = 0.99 and R² = 0.98) and SVM achieving the second best. A total of 50,905 data points representing the complex multidimensional design space are predicted via the machine-learned models to benefit the future perovskite studies. In addition, the symbolic regression based on the genetic algorithms effectively and automatically designs hybrid descriptors that outperform the individual descriptors. This article highlights the machine learning and symbolic regression methods for designing stable and high-performance halide perovskite materials and serves as a platform for further experimental optimization of halide perovskite materials.

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.

Surrogate Modeling Approaches for Multiobjective Optimization: Methods, Taxonomy, and Results

Most practical optimization problems are comprised of multiple conflicting objectives and constraints which involve time-consuming simulations. Construction of metamodels of objectives and constraints from a few high-fidelity solutions and a subsequent optimization of metamodels to find in-fill solutions in an iterative manner remain a common metamodeling based optimization strategy. The authors have previously proposed a taxonomy of 10 different metamodeling frameworks for multiobjective optimization problems, each of which constructs metamodels of objectives and constraints independently or in an aggregated manner. Of the 10 frameworks, five follow a generative approach in which a single Pareto-optimal solution is found at a time and other five frameworks were proposed to find multiple Pareto-optimal solutions simultaneously. Of the 10 frameworks, two frameworks (M3-2 and M4-2) are detailed here for the first time involving multimodal optimization methods. In this paper, we also propose an adaptive switching based metamodeling (ASM) approach by switching among all 10 frameworks in successive epochs using a statistical comparison of metamodeling accuracy of all 10 frameworks. On 18 problems from three to five objectives, the ASM approach performs better than the individual frameworks alone. Finally, the ASM approach is compared with three other recently proposed multiobjective metamodeling methods and superior performance of the ASM approach is observed. With growing interest in metamodeling approaches for multiobjective optimization, this paper evaluates existing strategies and proposes a viable adaptive strategy by portraying importance of using an ensemble of metamodeling frameworks for a more reliable multiobjective optimization for a limited budget of solution evaluations.

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.