Bi-level optimization for the energy conversion efficiency improvement in a photocatalytic-hydrogen-production system

Ren, Ting; Ma, Tianzeng; Li, Sha; Li, Xin

doi:10.1016/j.energy.2022.124138

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…However, due to the variety of parameters and analytic approaches to each process stage, it may not be possible to simply optimize each stage separately such that the solar-to-product figure of merit is maximized. One possibility is to use supervised ML as a powerful tool for making a variety of predictions for catalyst and reactor properties, [84][85][86][87][88][89][90] where relevant features in catalyst and reactor design (i.e., electronegativity, band center, surface area, reaction conditions, and reactor geometry/topology) can serve as input features for training a model on desired outputs (i.e., product formation rate, selectivity, stability, and efficiency). [91][92][93] Within photocatalysis, ML has been successfully employed, for example, to predict perovskite materials (using features including from electronegativity, light intensity, photocatalyst quantity, and calcination temperature) [94] and layered double hydroxides (using elemental and structural features generated from external packages [95,96] and based on local chemical hardness) for water splitting, organic heterojunction photocatalysts (using electronic descriptors such as electron affinity and reorganization energy) for hydrogen production, [97] and optimal reaction conditions (flow rate and reactor temperature) for the degradation of dyes.…”

Section: A Way Forwardmentioning

confidence: 99%

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Loh,

Wang,

Mohan

et al. 2024

Advanced Science

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Although solar fuels photocatalysis offers the promise of converting carbon dioxide directly with sunlight as commercially scalable solutions have remained elusive over the past few decades, despite significant advancements in photocatalysis band‐gap engineering and atomic site activity. The primary challenge lies not in the discovery of new catalyst materials, which are abundant, but in overcoming the bottlenecks related to material‐photoreactor synergy. These factors include achieving photogeneration and charge‐carrier recombination at reactive sites, utilizing high mass transfer efficiency supports, maximizing solar collection, and achieving uniform light distribution within a reactor. Addressing this multi‐dimensional problem necessitates harnessing machine learning techniques to analyze real‐world data from photoreactors and material properties. In this perspective, the challenges are outlined associated with each bottleneck factor, review relevant data analysis studies, and assess the requirements for developing a comprehensive solution that can unlock the full potential of solar fuels photocatalysis technology. Physics‐informed machine learning (or Physics Neural Networks) may be the key to advancing this important area from disparate data towards optimal reactor solutions.

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Section: A Way Forwardmentioning

confidence: 99%

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Loh,

Wang,

Mohan

et al. 2024

Advanced Science

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“…The photocatalytic process is a multiphase reaction and flow process involving mass and energy balance. Numerical simulation of this process is challenging due to the diversity of reaction substances, light sources, flow conditions, and catalyst forms [19]. Kumar and Bansal simulated the photocatalytic process in an immobilized reactor using computational fluid dynamics for the first time [20].…”

Section: Introductionmentioning

confidence: 99%

“…15, x FOR PEER REVIEW 14 of 18 21.6 mm, and 24.4 mm with increasing incident radiation intensity. Combined with Equation(19), it is observed that when the photocatalyst concentration is constant, the exponential part on the right side of the radiation field control equation remains unchanged.…”

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confidence: 99%

Numerical Simulation of Energy and Mass Transfer in a Magnetic Stirring Photocatalytic Reactor

2023

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Hydrogen production via photocatalytic water splitting is one of the promising solutions to energy and environmental issues. Understanding the relationship between hydrogen production in suspended photocatalytic reactions and various influencing factors is crucial for expanding the scale of the system. However, the complexity of physical and chemical factors involved in hydrogen production via photocatalytic water splitting makes systematic research of this technology challenging. In recent research, the simulated light source reactor has become a preferred study object due to its strong controllability. This paper presents a comprehensive energy and mass transfer model for the suspended photocatalytic reaction in a magnetically stirred reactor. The mutual impacts between the flow field, radiation field, and reaction field are analyzed. The simulation results show that the rotating speed of the stirring magneton in the reactor has a significant influence on the flow field. The rotation of the stirring magneton generates a vortex in the central axis area of the reactor, with the relationship between the depth of the vortex f(s) and the rotating speed of the magneton s described as f(s) = 0.27e0.0032s. The distribution of radiation within the reactor is influenced by both the incident radiation intensity and the concentration of the catalyst. The relationship between the penetration depth of radiation g(i) and the incident radiation intensity i is described as g(i) = 10.73ln(i) − 49.59. The relationship between the penetration depth of radiation h(c) and the particle concentration c is given as h(c) = −16.38ln(c) + 15.01. The radiation distribution in the reactor has a substantial impact on hydrogen production, which affects the concentration distribution law of hydrogen. The total amounts of hydrogen generated in the reactor are 1.04 × 10−3 mol and 1.35 × 10−3 mol when the reaction times are 1.0 s and 2.0 s, respectively. This study serves as a foundation for the future scaling of the system and offers theoretical guidance for the optimization of the photocatalytic reactor design and operating conditions.

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Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis

Wang

Bozal‐Ginesta

Kumar

et al. 2023

Matter

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Bi-level optimization for the energy conversion efficiency improvement in a photocatalytic-hydrogen-production system

Cited by 4 publications

References 25 publications

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Leave No Photon Behind: Artificial Intelligence in Multiscale Physics of Photocatalyst and Photoreactor Design

Numerical Simulation of Energy and Mass Transfer in a Magnetic Stirring Photocatalytic Reactor

Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis

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