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Dobkina, E. I.; Кузнецова, С.М.; Nefedova, L. A.; Lavrishcheva, S. A.

doi:10.1023/a:1013062816899

Cited by 3 publications

(2 citation statements)

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“…Alvarez and Blanco [31] Benzinger et al [36] 2016 60 Pt Titania 573-848 3-6 0-66 Colom et al [37] 2016 53 Na, V Silicate 523-1073 0.1 0-80 Corro et al [38] 2001 197 Pt, Sn Alumina 493-973 0.0035 0-90 Dobkina et al [39] Doering et al [19] 1988 133 Cs, V Silicate 635-677 8-14 94-100…”

Section: Referencementioning

confidence: 99%

Catalyst design using artificial intelligence: SO₂ to SO₃ case study

2020

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Catalyst design is key to the improvement of chemical process efficiency. The required work for the development of new catalysts can be supported through the proper application of artificial intelligence to identify optimal compositions. A generic methodology for the application of machine learning to catalysis research is therefore outlined in this work. The catalytic oxidation of SO2 was used to exemplify the first iteration of this methodology. 1784 data points from 31 published papers were compiled into a databank. The inlet SO2 concentration ranged from 0 to 66 mol%. An artificial neural network (ANN) was trained on the databank in order to predict SO2 conversion based on the catalyst composition and the reactor operating conditions (temperature, pressure, catalyst mass: volumetric flowrate ratio (w/v), and feed composition). The model achieved a root‐mean‐square error of 6.6%. A preliminary screening step identified 3:1 V‐Mg/SiO2 catalysts as exhibiting high conversion at 648 K. A multi‐objective optimization was then performed on a single catalyst to identify solutions exhibiting high conversion and high productivity at 648 K while minimizing the catalyst cost. The optimal solution was predicted to be a 2.9 wt% V/0.2 wt% Mg/SiO2 catalyst operating at a w/v of 7.49 kg‐cat · s/m3 STP, achieving 100% SO2 conversion with a material cost among the bottom third of cost values. Artificial intelligence can then be employed to extract useful knowledge from published catalytic data and orient future search for novel catalyst development.

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Section: Referencementioning

confidence: 99%

Catalyst design using artificial intelligence: SO₂ to SO₃ case study

2020

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“…Mechanism and kinetic of this reaction have been investigated by many researchers. Dunn et al studied the oxidation of this reaction over a supported vanadia catalyst, while its activity was analyzed by Dobkina et al . Also, Lee et al examined the removal of SO 2 from flue gas by the adsorbent synthesized from the coal fly ash and calcium oxide.…”

Section: Introductionmentioning

confidence: 99%

Enhanced BTX Production in Refineries with Sulfur Dioxide Oxidation by Thermal Integrated Model

2018

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More stringent environmental regulations as well as limitations of traditional energy sources lead to the development of innovative techniques to supply the required energy of different industries. Multifunctional autothermal reactors as a novel strategy in process integration technology have been introduced as a response to this requirement. The catalytic naphtha reforming process is one of the main processes in the refinery industries which demand several sources of energy to manage the existing reactions. Also, oxidization of sulfur dioxide to sulfur trioxide as a highly exothermic reaction is one of the typical solutions to reduce and control this greenhouse gas in various industries. According to the main aims of process integration and by considering environmental regulations, a novel thermal integration model is proposed. The results demonstrate the aromatic upgrading and high conversion of sulfur dioxide in this model.

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