Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Microchannel reactors have garnered significant attention in various chemical processes due to their ability to achieve rapid mixing. However, there remains ample opportunity for the development of microchannel reactors with simple structures and high throughput. In this work, we propose a novel microannular rotating bed (MARB) that integrates the high-gravity field and microscale effect to enhance the micromixing efficiency. The Villermaux−Dushman reaction system is employed to investigate the influence of the operating parameters on the mixing performance. The micromixing time of the MARB ranges from 10 −4 to 10 −3 s for low-viscosity Newtonian fluids and from 10 −4 to 10 −2 s for shear-thinning non-Newtonian fluids. In a lab-scale setup, the MARB demonstrates a throughput of 6 L/min, markedly surpassing the capabilities of the existing microchannel reactors. Furthermore, the evaluation of energy dissipation rate underscores the MARB's remarkable energetic efficiency in intensifying mixing processes. The MARB, with its simple design, high throughput capacity, and efficiency, presents a promising alternative for the intensification of mixing across diverse chemical applications.

Section: R D Rementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Micromixing in a Novel Microannular Rotating Bed

Zhang,

Xue,

Liu

et al. 2024

“…There have also been numerous studies of RTD in systems other than phase separators. These include for example, a miniaturized magnetic reactor (MMR) to intensify multiphase mixing at operational conditions of relatively large solution viscosity and flow rate ratio, using magnetic rotating force, 18 coiled flow inverter, 19 filter-press-type FM01-LC electrochemical cells, 20 different kinds of reactors such as laboratory reactors of small capacity at a different rotational speed of the impeller and feed flow rates for baffled and unbaffled arrangements, 21 multiphase monolith reactors, 22 and concurrent gas−liquid trickle bed reactor at various liquid phase velocities etc. 23 Different modeling approaches, such as the axial dispersion, piston-dispersion-exchange, etc.…”

Section: Introductionmentioning

confidence: 99%

Interplay of Residence Time Distribution and Electric Chaining Tendency due to Distributor Design Determine Electrocoalescer Performance

Kothmire,

Naik,

Thaokar

2024

An electrocoalescer is a piece of equipment wherein an electric field is applied across a water-in-oil emulsion to enhance the separation of its constituent phases. The equipment is employed in all petroleum refineries, almost without exception, to produce dehydrated, desalted crude oil, which is safe and suitable for further processing in the downstream equipment. The performance of an electrocoalescer critically depends upon the electrohydrodynamics and hydrodynamics in the equipment vessel, which acts at two levels. First the droplet−droplet interaction, fundamental to the microphysics of separation of a water-in-oil emulsion, is affected by electrohydrodynamics of coalescence and noncoalescence. Second, at a more macroscopic level, the electrohydrodynamics and hydrodynamics in the equipment vessel jointly influence the local water volume fraction of the emulsion and the probability of formation/disruption of water droplet chains across electrodes, which if formed, can result in electrical short-circuiting in the electrocoalescer. In this work, we present some avenues to intervene in the electrohydrodynamics and hydrodynamics at the second level to improve the performance of a continuously operated electrocoalescer. The study was carried out in a 6 L capacity custom-built laboratory-scale electrocoalescer of a design similar to that of industrial electrocoalescers. The apparatus comprised a horizontal axis cylindrical vessel, two horizontal-plane electrode grids mounted one above the other, and a single horizontal pipe with multiple orifices along its length, acting as a feed distributor. The flow characteristics of this bigrid electrocoalescer were studied in terms of residence time distribution (RTD) curves by conducting tracer experiments and by carrying out particle tracking simulations using commercial softwares COMSOL as well as ANSYS-FLUENT. Alternative physical flow models comprising combinations of stirred tanks and tubular elements were proposed and analytical expressions were derived to best fit the experimental and simulation data. The findings were compared with results obtained by carrying out electric dehydration of a model water-in-oil emulsion using the same experimental setup. We demonstrate that the vertical position of the emulsion distributor with respect to the two-electrode grids and the particular direction in which the emulsion ejects into the vessel by virtue of the orientation of the orifices, significantly affect the dehydration performance. For lowwater cut emulsions, it was found beneficial to keep the distributor between the two electrodes. On the other hand, for high-water cut emulsion, it was better to place the distributor between the lower electrode and the emulsion−water interface at the bottom. The work demonstrates how the presence of a complex interplay between RTD and water droplet chaining tendency dictates the optimal position of the feed distributor and the best possible orientation of the orifice on the same, for achieving effective dehydration of water-in-oil emulsi...

“…Subsequently, we applied the GA to optimize the electrolytic voltage. In contrast to a comprehensive variable optimization, this method uses only 5 key features selected through the SHAP method − as decision variables. It yielded results comparable to those of full-variable optimization while substantially improving time efficiency.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

Zhang,

Tan,

Yuan

et al. 2024

The optimization of the membrane electrode assembly (MEA) is crucial for enhancing the performance of proton exchange membrane water electrolysis. Nevertheless, achieving global optimization of all manufacturing parameters of the MEA poses challenges due to their high-dimensional complexity and limited experimental data. In this study, machine learning (ML) techniques were introduced to tackle this intricate engineering challenge. 58 MEAs were fabricated and tested to construct a comprehensive database enriched with features and ample data. This was achieved through a data expansion method that involves altering the operating temperature of the electrolyzer. The XGBoost was employed to perform regression predictions on high-dimensional variables, achieving a remarkable coefficient of determination (R 2 ) value of 0.99926. The SHAP (SHapley Additive exPlanations) method and the genetic algorithm were applied for model interpretation and global optimization, respectively. By utilizing the insights provided by the SHAP method, we could narrow the decision variable dimensionality down to 5 key variables, achieving results that are comparable to full-variable optimization while notably reducing time costs by 67.9%. Guided by ML, the MEA with globally optimized variables achieved a voltage of only 1.828 V at 3 A cm −2 . The study presents an approach that integrates intelligent optimization techniques with data-driven methods for highdimensional variable optimization. This contribution provides valuable insights into energy conversion and storage technologies in the chemical industry.