Abstract
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.
Shigellosis, caused by Shigella species, is a major public health problem in Bangladesh. To determine the prevalence and distribution of different Shigella species, we analyzed 10,827 Shigella ...isolates from patients between 2001 and 2011. S. flexneri was the predominant species isolated throughout the period. However, the prevalence of S. flexneri decreased from 65.7% in 2001 to 47% in 2011, whereas the prevalence of S. sonnei increased from 7.2% in 2001 to 25% in 2011. S. boydii and S. dysenteriae accounted for 17.3% and 7.7% of the isolates respectively throughout the period. Of 200 randomly selected S. sonnei isolates for extensive characterization, biotype g strains were predominant (95%) followed by biotype a (5%). Resistance to commonly used antibiotics including trimethoprim-sulfamethoxazole, nalidixic acid, ciprofloxacin, mecillinam and ampicillin was 89.5%, 86.5%, 17%, 10.5%, and 9.5%, respectively. All isolates were susceptible to ceftriaxone, cefotaxime, ceftazidime and imipenem. Ninety-eight percent of the strains had integrons belonging to class 1, 2 or both. The class 1 integron contained only dfrA5 gene, whereas among class 2 integron, 16% contained dhfrAI-sat1-aadA1-orfX gene cassettes and 84% harbored dhfrA1-sat2 gene cassettes. Plasmids of ∼5, ∼1.8 and ∼1.4 MDa in size were found in 92% of the strains, whereas only 33% of the strains carried the 120 MDa plasmid. PFGE analysis showed that strains having different integron patterns belonged to different clusters. These results show a changing trend in the prevalence of Shigella species with the emergence of multidrug resistant S. sonnei. Although S. flexneri continues to be the predominant species albeit with reduced prevalence, S. sonnei has emerged as the second most prevalent species replacing the earlier dominance by S. boydii and S. dysenteriae in Bangladesh.
Energy storage is critical for overcoming challenges associated with the intermittency and the variable availability of renewable sources for decarbonizing the energy sector. Cryogenic energy storage ...(CES) is of interest due to its high technology readiness level, no geographical limitations, and moderate round-trip efficiency. The time-varying nature of demands and renewable availability needs to be considered at the design and integration stages of energy storage. We develop a mixed-integer nonlinear program (MINLP) model to obtain the energy storage costs on a daily basis for different scenarios that typically arise over an entire year. Using this optimization-based framework, we address key decision-making questions towards energy transition: What is the energy cost when CES is integrated with renewables and power plants? How does each scenario affect the overall energy cost? How much storage is needed for complete transition to renewables? What is the optimal integration towards 100% renewable energy? What are the optimal storage designs for both renewables and fossil-based power generation with current and future energy demands? We discuss different scenarios and solutions to these questions.
•An optimization-based model for cryogenic energy storage integrated with power plants.•The model accounts for interactions between power sources, storage, and grid demand.•Scenario analysis for energy storage from renewables and fossil power plants.•Energy storage can meet the current demands with a marginal burden on power plants.•Renewable energy farms benefit from large-scale storage to overcome intermittency.•Fossil plants are economical without energy storage due to cheaper ramping options.
•A two phase algorithm for constrained black-box problems is presented.•It does not require a feasible initial point and can handle hard constraints.•Performance compared to NOMAD and COBYLA by ...applying on large set of problems.•Applied to optimize a process involving nonlinear algebraic and partial differential equations.
This paper presents an algorithm for constrained black-box and grey-box optimization. It is based on surrogate models developed using input-output data in a trust-region framework. Unlike many current methods, the proposed approach does not require feasible initial point and can handle hard constraints via a novel optimization-based constrained sampling scheme. A two-phase strategy is employed, where the first phase involves finding feasible point through minimizing a smooth constraint violation function (feasibility phase). The second phase improves the objective in the feasible region using the solution of the feasibility phase as starting point (optimization phase). The method is applied to solve 92 test problems and the performance is compared with established derivative-free solvers. The two-phase algorithm outperforms these solvers in terms of number of problems solved and number of samples used. We also apply the algorithm to solve a chemical process design problem involving highly-coupled, nonlinear algebraic and partial differential equations.
In the present study, the effect of concentration of titanium carbide (TiC) particles on the structural, mechanical, and electrochemical properties of Ni-P composite coatings was investigated. ...Various amounts of TiC particles (0, 0.5, 1.0, 1.5, and 2.0 g L
) were co-electrodeposited in the Ni-P matrix under optimized conditions and then characterized by employing various techniques. The structural analysis of prepared coatings indicates uniform, compact, and nodular structured coatings without any noticeable defects. Vickers microhardness and nanoindentation results demonstrate the increase in the hardness with an increasing amount of TiC particles attaining its terminal value (593HV
) at the concentration of 1.5 g L
. Further increase in the concentration of TiC particles results in a decrease in hardness, which can be ascribed to their accumulation in the Ni-P matrix. The electrochemical results indicate the improvement in corrosion protection efficiency of coatings with an increasing amount of TiC particles reaching to ~ 92% at 2.0 g L
, which can be ascribed to a reduction in the active area of the Ni-P matrix by the presence of inactive ceramic particles. The favorable structural, mechanical, and corrosion protection characteristics of Ni-P-TiC composite coatings suggest their potential applications in many industrial applications.
A methodology is proposed to reduce the cost and capital intensity of small‐scale chemical processes by creating new opportunities for economies of numbers through standardizing the equipment designs ...across multiple processes. We depart from asynchronous design of single‐processes and adopt a common‐functionality based simultaneous design of multiple processes that use similar unit operations. A generalized cost function is used to appropriately balance the trade‐offs between economies of scale and economies of numbers. An optimization‐based framework for design standardization is developed and illustrated using two case studies. The first involves the simultaneous synthesis of methanol and ammonia processes, and the second addresses the optimal synthesis of multi‐column natural gas liquid (NGL) fractionation processes for different natural gas sources. We observe that considerable reduction in capital intensity of small‐scale processes is possible through equipment standardization.
Energy storage allows flexible use and management of excess electricity and intermittently available renewable energy. Cryogenic energy storage (CES) is a promising storage alternative with a high ...technology readiness level and maturity, but the round-trip efficiency is often moderate and the Levelized Cost of Storage (LCOS) remains high. The complex flowsheets with intricate thermodynamics at cryogenic temperatures as well as the presence of multiple loops and refrigeration cycles pose considerable challenges for rigorous model-based design and optimization of CES systems. We present an optimization strategy that couples rigorous process simulation and Bayesian optimization with flowsheet decomposition and identification of hidden coupling constraints to optimally design standalone CES systems. Further refinement is done via a local search using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. Our results indicate that it is possible to achieve more than 52% round-trip efficiency and an LCOS of $153/MWh for a standalone 100 MW/400 MWh CES system limited to short-term storage with daily charging–discharging. However, a detailed techno-economic assessment reveals that the LCOS considering total capital investment may exceed $267/MWh when all direct and indirect costs of installation and operation are considered.
•A systematic process optimization and techno-economic analysis of stand-alone cryogenic energy systems.•Simulation-based black-box optimization technique with embedded convergence algorithm for feasible CES flowsheet simulation.•Maximizing the CES round-trip efficiency (RTE).•Detailed calculation of the levelized cost of storage (LCOS) for standalone CES.•Sensitivity analysis for varying ambient conditions.
•A systematic approach to increase the profitability and decrease the cost of power plant operations in the presence of active regulatory constraint on carbon emission under uncertainty.•A ...multi-stage stochastic programming algorithm for power plant scheduling with flexible carbon capture.•Analysis of the effect of regulatory constraint on CO2 emission on the profitability under market uncertainty.•Surrogate model-based continuous expressions that eliminate need for discretization of state space and reduces computational effort.•Comparison of the results incorporating price uncertainty with a base case.
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To mitigate CO2 emissions, it is often suggested that power plants deploy carbon capture systems. However, high cost is an impediment for the deployment of these systems. To counter this, a power plant can be integrated with a flexible capture unit which varies its load with fluctuating electricity prices. In this work, a multi-stage stochastic programming approach is applied to optimally schedule power production and carbon capture operations to maximize daily profit in a market with uncertain hourly electricity prices. Low-complexity surrogate models are developed for optimal action policy at each stage, which reduce the computational complexity of estimating profit for different price scenarios. The expected value of perfect information obtained is within 25% of the maximum achievable profit while meeting the CO2 emission constraints. Moreover, the profitability improves by 40% compared with the deterministic case assuming expected values of stochastic parameters. This demonstrates the quality of the stochastic solution.
One challenge in accounting for process safety incidents is that accurate modeling is complex, time-intensive, and requires many inputs. Process safety consequence modeling using first principles can ...be complicated. At the same time, setting up experiments is not always practical. This work proposes an artificial neural network (ANN) framework to predict process safety metrics to prevent overpressure during tube rupture scenarios with reasonable accuracy. Specifically, we apply a feed forward neural network to predict heat exchanger safety rating that is proportional to the heat exchanger pressure normalized with respect to the maximum allowable pressure. By training ANN to a set of tube rupture simulation data, we are able to bypasses the need for solving tedious dynamic and non-smooth system of equations. The ANN-based models yield safety rating predictions that comply with API 521 overpressure standards. We further demonstrate how these predictions can be used to perform real-time monitoring for a network of heat exchangers in a plant setting.
•ANN framework introduced for detecting overpressure severity in heat exchangers.•Detailed algorithm serves as basis for consequence modeling predictions.•Single high fidelity and generalized safety rating predictions models were developed.•ANN models successfully bypassed rigorous dynamic overpressure models.•Real-time overpressure monitoring demonstrated for heat exchanger network.
•New graph theoretic representation of zeolites based on Single Repeating Unit (SRU).•Algorithmic, optimization and hybrid approaches for identifying SRU.•159 existing and 10000+ hypothetical zeolite ...structures found to be Hamiltonian.•SRU identification can be modeled as Traveling Salesman Problem (TSP).•25–50% Reduction in T-nodes using SRU in comparison to unit cell representation.
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Zeolites are microporous materials with periodic framework structures. Efficient representation of crystal frameworks is important for the design and discovery of new zeolites. We explore a graph-theoretic representation, namely Single Repeating Unit (SRU), which utilizes fewest tetrahedral atoms (T-nodes) and their connectivity graph to describe a zeolite framework. SRUs use topologically distinctive T-nodes, thereby significantly reducing the description space. We also propose several approaches to identify SRUs of large crystallographic frameworks. In the optimization-based approach, SRU identification is formulated as a special instance of traveling salesman problem (TSP). We analyze SRUs of 159 existing and over 10,000 hypothetical zeolites that are all observed to be Hamiltonian cycle graph networks. Considerable reduction in T-nodes is also possible using SRUs. For example, the large Chabazite framework is represented using only 12 T-nodes in the Hamiltonian graph representation. Additionally, SRUs provide a systematic approach to generate Aluminum substituted frameworks for different Si/Al ratios.
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