Design of the Process for Methane-Methanol at Soft Conditions Applied to Selection the Best Descriptors for Periodic Structures Using Artificial Intelligence

Josue Lozada, E. Reguera, C. I. Aguirre-Velez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Methane is a relevant energy vector in modern society considering both, its natural abundance, and the possibility to be produced from residual biomass through an anaerobic process. While methane is a supercritical gas at room temperature, methanol is a liquid, which facilitates its storage and handling as a fuel. This explains the interest and convenience to have a technology for the methane to methanol conversion. The selective oxidation of methane to methanol in a direct way is currently a challenge because depending on the reaction route different results can be obtained. The use of zeolites materials as support of copper complexes promises a clare way to find conditions in order to realize a catalytic process. There are different parameters involve zeolites with copper complexes that contribute to make a chemical environment for the interaction between methanol and active sites in the structure. Taking into account several structural and physical chemical parameters it is possible to make a selection of the best structures for this conversion process, helping on artificial intelligence algorithms.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence and Computer Engineering - Proceedings of ICAETT 2021
EditorsMiguel Botto-Tobar, Omar S. Gómez, Raul Rosero Miranda, Angela Díaz Cadena, Sergio Montes León, Washington Luna-Encalada
PublisherSpringer Science and Business Media Deutschland GmbH
Pages157-167
Number of pages11
ISBN (Print)9783030961466
DOIs
StatePublished - 2022
Event3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021 - Riobamba, Ecuador
Duration: 10 Nov 202112 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
Volume407 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021
Country/TerritoryEcuador
CityRiobamba
Period10/11/2112/11/21

Keywords

  • Database
  • Machine learning
  • Zeolites

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