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Record W3080136069 · doi:10.1002/er.5827

The role of co <sub>2</sub> in improving sonic hydrogen production

2020· article· en· W3080136069 on OpenAlexafffundabout
Sherif S. Rashwan, İbrahim Dinçer, Atef Mohany

Bibliographic record

VenueInternational Journal of Energy Research · 2020
Typearticle
Languageen
FieldMaterials Science
TopicUltrasound and Cavitation Phenomena
Canadian institutionsOntario Tech University
FundersGovernment of Ontario
KeywordsHydrogen productionHydrogenGreenhouse gasEnergy carrierProcess engineeringCarbon dioxideHydrogen fuelEnvironmental scienceChemical processHydrogen economyCarbon fibersBiochemical engineeringChemistryComputer scienceChemical engineeringEngineeringEcology

Abstract

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Highlighting the demand for extreme cut in carbon dioxide emissions, hydrogen is among the most promising fuels that is proper for carbon-free energy. Although the key merits beyond using hydrogen as an energy carrier and/or fuel are (a) environmental friendly, (b) clean, (c) sustainable, (d) efficient, (e) effective, (f) nontoxic, (g) reliable and (h) healthy, a fundamental question remains for researchers to address: how can we produce clean hydrogen without any negative impact on the environment? In addition, various research and developments efforts undertaken in the fields of chemistry and chemical engineering, we have started enhancing these efforts in the area of mechanical engineering by dwelling on the sonic hydrogen production reactors and their design, analysis and assessment.1 It is therefore named as “sonohydrogen process,” which helps in accomplishing low carbon or carbon-free hydrogen production. This innovative approach is benefiting from the ultrasound waves to trigger auxiliary chemical reactions.2 Hydrogen production can be achieved by a simple separation process from water molecules. This approach is similar to that used by Merouani,3 in the field of sonochemistry. However, our approach is different in the sense that the sound is used primarily to generate the acoustic cavitation bubbles rather than boosting existing chemical reactions. This perspective provides a path forward to enhance the hydrogen production rate while simultaneously reducing greenhouse gases, such as carbon dioxide CO2. The principal idea lies on a novel finding associated with the effect of using carbon dioxide as a dissolved gas on the sonohydrogen process. This innovative approach is illustrated in Figure 1A, clean water is fed into a sonoreactor along with carbon dioxide as a dissolved gas after being captured from the exhaust gases of stationary emission sources, such as combustion chimneys or power plants. In Figure 1B, we present a newly proposed system for ultrasonic water treatment and hydrogen production, simultaneously. At which, the wastewater is taken from the drainage areas and supplied to an ultrasonic water treatment bath, where ultrasonic waves are introduced to remove the algae in a pre-treatment process by the use of the power ultrasound. Then the water is passed by filtration and disinfection processes before it is supplied to the sonoreactor for hydrogen production. The benefit of this process is the concurrent use of wastewater and carbon dioxide for clean hydrogen production. The novelty of this perspective is that it sheds the light on the opportunity of enhancing the hydrogen production rate during the sonohydrogen process by using carbon dioxide as a dissolved gas. The objectives of this perspective can be summarized as follows: (a) it gives a snapshot on howCO2 is utilized in improving the hydrogen production process, (b) it suggests a novel system for wastewater treatment and hydrogen production processes, simultaneously. It also discusses the novel opportunity that could lead to secure clean hydrogen energy for the future energy. The hydrogen production rate is governed by several essential factors, including, the acoustic frequency, acoustic intensity, the geometry of the sonoreactor, and the dissolved/soluble gases. But, some limited efforts have been exerted to investigate the effect of carbon dioxide CO2 as a dissolved gas on the sonication process.4 The impact of using dissolved/soluble gases on the sonohydrogen process depends on three significant physical-properties; (a) volumetric specific heat capacity Cp [kJ/m3/K], (b) thermal conductivity k [W/m K], and thermal diffusivity α = k/ρ. Cp [m2/s]. Dissolved/soluble gases with high heat capacity could sustain high bubble temperatures. In contrast, dissolved/soluble gases with low conductivity have small heat dissipation rate, which allows more heat to be accumulated within the bubbles. The combination of both physical properties of high heat capacity and low conductivity will collectively lead to low thermal diffusivity. Consequently, this achieves the optimal performance of water vapor dissociation process and as a result, more hydrogen is produced. Figure 2 illustrates the sonohydrogen process with an ultrasound transducer probe immersed in a water tank to generate cavitation bubbles and it also displays the resultant dissociation mechanism of H2O. The sonohydrogen process has three successive stages; (a) the transducer (ultrasonic source) is dipped in a water tank emitting sound waves at a frequency range of 20-40 kHz. (b) The shape of the sonoreactor controls the generated acoustic field, and cavitation bubbles are initiated at the tip of the transducer and then dispersed inside the sonoreactor. (c) The separation mechanism of H2O into radicals, such as hydroxyl (*OH) and hydrogen (H*), takes place. The radicals are recombined to generate hydrogen bubbles in the presence of CO2, which acts as a diluent. Simulations are conducted using the chemical reaction engineering submodule in COMSOL 5.4 to study the kinetics of a cavitation bubble that is saturated with H2O/CO2,more details about that can be found here.5 This article aims to bring an eye opening dimension and discussion by deploying something which is known as undesired chemical (so-called: CO2) to improve the hydrogen production performance of sonic reactors. In this regard, a computational study is performed comprehensively, and its results are presented in this perspective to compare the base case of water dissociation (ie, no dissolved gases) with the developed case that contains dissolved carbon dioxide. Figure 3 shows the change in hydrogen mole fraction with respect to the reaction time. The bubble that is initially saturated with 39% H2O/CO2 has a high hydrogen mole fraction than the water vapor bubble. This is because of the resultant lower thermal diffusivity associated with the H2O/CO2 mixture. Moreover, a series of numerical simulations of the bubble dynamics and the reaction kinetics that occur in a single bubble are performed for four saturating gases (CO2, Air, Ar, O2). Figure 4 presents the evolution of the reaction system inside a bubble as a function of time at an ultrasonic frequency of f = 20 kHz and acoustic pressureamplitude of 0.1 MPa. These values fall within the recommended range of the sonoreactor operation. The bubble temperature was maintained at 8000 K for all the simulations. To achieve high temperature, it is recommended that the sonoreactor should be operated at an ultrasonic frequency in the range between 20-40 kHz. Figure 4 shows that the hydrogen production for CO2 is a batch reaction that peaks at around 1 μs and then diminishes with time. However, for all other dissolved gases, the hydrogen production peaks around the same time, but it rests at higher hydrogen production. The theory beyond this finding lies in the lower thermal conductivity, the higher heat capacity, and the lower thermal diffusivity that the dissolved bubbles provide. If all of those factors achieved, the optimum hydrogen production is achieved. Calculation on hydrogen bubbles produced from the sonohydrogen process can be found here.5 The study revealed that, the required bubble temperature to achieve minimum hydrogen productivity is 3000 K. The higher the bubble temperature, the faster the reaction rate, which would enhance the hydrogen productivity. From the energy consumption perspective, the sonohydrogen process produces hydrogen in the range between 1.05-1.63 μmol/kWh for O2/H2O bubbles. However, in the case of CO2/H2O bubble, the hydrogen produced showed an outstanding improvement in the range 22.26-34.98 μmol/kWh. This is an increase of over 2000% within the same range of the bubble temperatures. The first author expresses gratitude to the Government of Ontario, Canada, for funding this work in the form of the Ontario Trillium Scholarship (OTS).

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.313
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2020
Admission routes3
Has abstractyes

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