Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

Xim Bokhimi

doi:10.3389/fceng.2021.740270

Abstract

We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

Highlights

Since everything presented in the paper is related to supervised learning, we describe the essential elements of this learning method for the non-specialist in artificial intelligence
When we develop a new catalyst for hydrodesulfurization, the goal is to bring out a product with low sulfur content
The results show that artificial intelligence techniques and relatively few experiments are sufficient to save both cost and time to discover new catalysts

Summary

Introduction

Models have been developed for oil production (Li and Horne, 2003; Irisarri et al, 2016; Hutahaean et al, 2017) and oil transformation (Farrusseng et al, 2003; Wang et al, 2019). These models, have not been entirely successful (Cai et al, 2021). They have been constantly evolving and have included artificial intelligence techniques. In exact sciences (Sauceda et al, 2021; Hajibabaei and Kim, 2021; Cerioti et al, 2021; Bahlke et al, 2020; Chmiela et al, 2020; Saar et al, 2021; Artrith and Urban, 2016; Ch’ng et al, 2017; Shallue and Vanderburg, 2018; Sadowski et al, 2016), in social sciences (Ng et al, 2020; Lattner and Grachten, 2019), in technology (Bae et al, 2021; Cunneen et al, 2019; Feldt et al, 2018; Huang et al, 2014), and health sciences (Bashyam et al, 2020; Zhou et al, 2019; Themistocleous et al, 2021; Lagree et al, 2021)

Methods

Results

Conclusion