A trust region-based two phase algorithm for constrained black-box and grey-box optimization with infeasible initial point

A Tandem Running Strategy-Based Heat Transfer Search Algorithm and Its Application to Chemical Constrained Process Optimization

Constrained optimization problems (COPs) are widely encountered in chemical engineering processes, and are normally defined by complex objective functions with a large number of constraints. Classical optimization methods often fail to solve such problems. In this paper, to solve COPs efficiently, a two-phase search method based on a heat transfer search (HTS) algorithm and a tandem running (TR) strategy is proposed. The main framework of the MHTS–TR method aims to alternate between a feasible search phase that only examines feasible solutions, using the HTS algorithm, and an infeasible search phase where the treatment of infeasible solutions is relaxed in a controlled manner, using the TR strategy. These two phases play different roles in the search process; the former ensures an intensified optimum in a relevant feasible region, whereas the latter is used to introduce more diversity into the former. Thus, the ensemble of these two complementary phases can provide an effective method to solve a wide variety of COPs. The proposed variant was investigated over 24 well-known constrained benchmark functions, and then compared with various well-established metaheuristic approaches. Furthermore, it was applied for solving a chemical COP. The promising results demonstrate that the MHTS–TR approach is applicable for solving real-world COPs.

Flexible oxygen concentrators for medical applications

Medical oxygen concentrators (MOCs) are used for supplying medical grade oxygen to prevent hypoxemia-related complications related to COVID-19, chronic obstructive pulmonary disease (COPD), chronic bronchitis and pneumonia. MOCs often use a technology called pressure swing adsorption (PSA), which relies on nitrogen-selective adsorbents for producing oxygen from ambient air. MOCs are often designed for fixed product specifications, thereby limiting their use in meeting varying product specifications caused by a change in patient's medical condition or activity. To address this limitation, we design and optimize flexible single-bed MOC systems that are capable of meeting varying product specification requirements. Specifically, we employ a simulation-based optimization framework for optimizing flexible PSA- and pressure vacuum swing adsorption (PVSA)-based MOC systems. Detailed optimization studies are performed to benchmark the performance limits of LiX, LiLSX and 5A zeolite adsorbents. The results indicate that LiLSX outperforms both LiX and 5A, and can produce 90% pure oxygen at 21.7 L/min. Moreover, the LiLSX-based flexible PVSA system can manufacture varying levels of oxygen purity and flow rate in the range 93-95.7% and 1-15 L/min, respectively. The flexible MOC technology paves way for transitioning to an envisioned cyber-physical system with real-time oxygen demand sensing and delivery for improved patient care.

DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems

The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer nonlinear programming problems. In this framework, bi-level optimization problems are approximated as single-level optimization problems by collecting samples of the upper-level objective and solving the lower-level problem to global optimality at those sampling points. This process is done through the integration of the DOMINO framework with a grey-box optimization solver to perform design of experiments on the upper-level objective, and to consecutively approximate and optimize bi-level mixed-integer nonlinear programming problems that are challenging to solve using exact methods. The performance of DOMINO is assessed through solving numerous bi-level benchmark problems, a land allocation problem in Food-Energy-Water Nexus, and through employing different data-driven optimization methodologies, including both local and global methods. Although this data-driven approach cannot provide a theoretical guarantee to global optimality, we present an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved to global optimality at convergence.