A techno-economic assessment of the potential for combining supercritical water oxidation with ‘in-situ’ hydrothermal synthesis of nanocatalysts using a counter current mixing reactor

Al-Atta, Ammar; Huddle, Thomas; Rodríguez, Yoana García; Mato, Fidel; García-Serna, Juan; Cocero, Marı́a José; Gomes, Rachel L.; Lester, Edward

doi:10.1016/j.cej.2018.03.058

Cited by 19 publications

(9 citation statements)

References 56 publications

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“…The organic molecules are easily attacked by hydroxyl radicals which are produced during the Fenton reactions. The present study results indicated that the addition of a small amount of dissolved iron salt to H 2 O 2 solution that might increase the performance of SCWO on the degradation of acrylic acid, which play a synergistic role in the Fenton oxidation and SCWO at relatively moderate reaction temperature (Al-Atta et al 2018 ).…”

Section: Introductionmentioning

confidence: 72%

“…The reaction temperature was set to exceed the critical point of water which is governed by heater limitation. A short residence time was chosen because it was shown to have very little effect on the oxidation of other compounds like acrylic acid (Al-Atta et al 2018 ). The results of the preliminary experiments in terms of the removal of phenol and COD are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Supercritical water oxidation of phenol and process enhancement with in situ formed Fe2O3 nano catalyst

Al-Atta

Sher

Hazafa

et al. 2021

Environ Sci Pollut Res

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During the past few decades, the treatment of hazardous waste and toxic phenolic compounds has become a major issue in the pharmaceutical, gas/oil, dying, and chemical industries. Considering polymerization and oxidation of phenolic compounds, supercritical water oxidation (SCWO) has gained special attention. The present study objective was to synthesize a novel in situ Fe2O3nano-catalyst in a counter-current mixing reactor by supercritical water oxidation (SCWO) method to evaluate the phenol oxidation and COD reduction at different operation conditions like oxidant ratios and concentrations. Synthesized nano-catalyst was characterized by powder X-ray diffraction (XRD) and transmission electron microscope (TEM). TEM results revealed the maximum average particle size of 26.18 and 16.20 nm for preheated and non-preheated oxidant configuration, respectively. XRD showed the clear peaks of hematite at a 2θ value of 24, 33, 35.5, 49.5, 54, 62, and 64 for both catalysts treated preheated and non-preheated oxidant configurations. The maximum COD reduction and phenol oxidation of about 93.5% and 99.9% were observed at an oxidant ratio of 1.5, 0.75 s, 25 MPa, and 380 °C with a non-preheated H2O2 oxidant, while in situ formed Fe2O3nano-catalyst showed the maximum phenol oxidation of 99.9% at 0.75 s, 1.5 oxidant ratio, 25 MPa, and 380 °C. Similarly, in situ formed Fe2O3 catalyst presented the highest COD reduction of 97.8% at 40 mM phenol concentration, 1.0 oxidant ratio, 0.75 s residence time, 380 °C, and 25 MPa. It is concluded and recommended that SCWO is a feasible and cost-effective alternative method for the destruction of contaminants in water which showed the complete conversion of phenol within less than 1 s and 1.5 oxidant ratio.

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Section: Introductionmentioning

confidence: 72%

Section: Methodsmentioning

confidence: 99%

Supercritical water oxidation of phenol and process enhancement with in situ formed Fe2O3 nano catalyst

Al-Atta

Sher

Hazafa

et al. 2021

Environ Sci Pollut Res

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“…It is possible that the HEPP plants will able to use their own process waste as fuel if not entirely on their own but with integration into hybrid processes that will generate energy as well as energy recovery and material products from waste (see for example Al-Atta et al, 2018).…”

Section: Discussion Of Potential Hybrid Energy Process Plant Applicatmentioning

confidence: 99%

Artificial Intelligence Assisted Dynamic Control of Environmental Emissions From Hybrid Energy Process Plants (HEPP)

Tüzün

2020

Front. Energy Res.

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A model digital data processing platform is proposed based on "deep-learning" methodology that can be used to identify the emissions patterns from process plants with hybrid energy recovery and energy generation facilities. The non-invasive dynamic monitoring and correlation of particulate, VOCs and other greenhouse gas emissions from semi-batch and continuous process plants is demonstrated with use of neural encoding and pattern recognition using a multi-layer perceptron and multi-stack encoder configuration. A multi-layer environmental perceptron (MLEP) is developed based on the above analyses that aims to detect patterns of emission types, rates and concentrations as a function of variation of plant operational conditions and process variables. Four different task algorithms are constructed and are currently trained for use in (i) In-Plant Product Quality Control Domain and (ii) In-Plant Process Efficiency Target Control Domain. As a further consequence, environmental impact assessment is considered within the hazards and process safety frameworks that conventionally issue sanctions and penalize non-compliance with imposition of environmental levy scales rather than offering process improvement incentives. The latter is demonstrated to be possible by facilitating dynamic corrective action and hazard prevention using MLEP platforms should emission ceilings be frequently and/or periodically exceeded in 24/7 continuous plant operations. Potential applications of the MLEP (MLEP) are illustrated in the context of dynamic emissions control and abatement in hybrid energy process plants (HEPP) and combined power plants using process-integrated CO 2 capture and storage schemes.

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“…e software allows, also, the use of available broad database of chemicals and physical parameters that permit built-in operation units suitable for sensitivity analysis and modelling of various processes related to petrochemicals [28], catalysts [29], hydrogen production [30], and carbon dioxide capture [31]. e application of Aspen Plus was reported in the field of nanotechnology [32,33]; however, it should be pointed out that, to the best of our knowledge, this approach has never been used for investigating such nanomaterials synthesis. Hence, the present work is devoted to sensitivity analysis and simulation of large-scale production of molybdenum disulfide nanoparticles, using Aspen Plus.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Sensitivity Analysis of Molybdenum Disulfide Nanoparticle Production Using Aspen Plus

Hachhach

Akram

Hanafi

et al. 2019

International Journal of Chemical Engineering

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The sensitivity analysis of molybdenum disulfide nanoparticles synthesis process is studied using Aspen Plus with the aim of investigating the effect of reactants’ amounts on the production of molybdenum disulfide nanoparticles. The adopted approach consists in simulating the synthesis process based on experimental data, obtained at laboratory scale followed by sensitivity analysis with respect to the following precursors: ammonium heptamolybdate, elemental sulfur, and hydrazine used as a reducing agent. The sensitivity analysis revealed that the precursors have more significant impact on the obtained amount of molybdenum disulfide compared to hydrazine. The obtained result showed that the approach adopted in the study might be of interest for further investigation of the process design and scaling-up.

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A techno-economic assessment of the potential for combining supercritical water oxidation with ‘in-situ’ hydrothermal synthesis of nanocatalysts using a counter current mixing reactor

Abstract: A techno-economic assessment of the potential for combining supercritical water oxidation with 'insitu' hydrothermal synthesis of nanocatalysts using a counter current mixing reactor, Chemical Engineering

Cited by 19 publications

References 56 publications

Supercritical water oxidation of phenol and process enhancement with in situ formed Fe2O3 nano catalyst

Supercritical water oxidation of phenol and process enhancement with in situ formed Fe2O3 nano catalyst

Artificial Intelligence Assisted Dynamic Control of Environmental Emissions From Hybrid Energy Process Plants (HEPP)

Simulation and Sensitivity Analysis of Molybdenum Disulfide Nanoparticle Production Using Aspen Plus

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