Search

• A new approach for multi-objective optimization of Pressure Swing Adsorption. • Fisher–Snedecor test to the solution of a multi-objective problem is deduced. • The concept of Feasible Operating Regions is presented to address the study case. • The obtained Pareto region is divided into operating sub-regions clustering. Optimization of cyclic adsorption processes is a challenging issue due to the dynamic-operating complexity of these processes. In this context, this work proposes a new approach for multi-objective optimization of Pressure Swing Adsorption (PSA) units, extending the concept of the Pareto front to Pareto region. The proposed methodology, hitherto unexplored in the literature, consists of integrating a likelihood test, an arrangement from the Fisher–Snedecor test to the solution of a multi-objective problem provided by a Swarm Particle Optimization technique. The Pareto region is divided into operating sub-regions that meet the optimization constraints and prioritize a determined objective by a clustering process. These sub-regions make the operation more flexible. Furthermore, the analysis of the operating variables feasible operation interval demonstrated to be an important tool to provide information regarding the system behavior. As a study case, it is presented the optimization of a PSA process for syngas purification. The results demonstrate that the methodology proposed here uses the feasible operation region map and the clustering strategy to exploit the multi-objective optimization. Therefore, providing a more reliable and precise optimization of PSA units, while providing an important tool for making decisions in the PSA system. [ABSTRACT FROM AUTHOR]

A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked. [ABSTRACT FROM AUTHOR]

• Material screening and simultaneous processes optimization. • Deep learning neural networks providing deep insights about the process behavior. • A nested loop is proposed to provide the integration of the level within the optimization framework. • Uncertainty assessment of the optimal points provides a map of the feasible operating regions. The present work proposes a novel strategy for simultaneous material screening and process optimization. This strategy is based on the capacities of deep neural networks to extract knowledge of a database. It makes use of a nested optimization loop which is designed to couple the process and material points of view simultaneously. The optimization problem results are analyzed by a Fisher–Snedecor test, designed to assess the optimal points uncertainties, building the process's feasible operating regions. This methodology describes the processes' possible operating points that lead to optimal conditions, considering the material type as a decision variable. The methodology shows that this complex problem can be better understood when the uncertainties are taken into consideration. On the other hand, the proposed optimization problem can provide a way to address the issues related to optimizing adsorption processes considering the material screening. [ABSTRACT FROM AUTHOR]