Artificial Intelligence and Evolutionary Approaches in Particle Technology

Author:

Thon Christoph1ORCID,Röhl Marvin1,Hosseinhashemi Somayeh1,Kwade Arno1ORCID,Schilde Carsten1ORCID

Affiliation:

1. Institute for Particle Technology (iPAT), Technische Universität Braunschweig, Germany

Publisher

Hosokawa Powder Technology Foundation

Subject

General Engineering,General Materials Science,General Chemical Engineering,General Chemistry

Reference99 articles.

1. Ashraf C., Joshi N., Beck D.A.C., Pfaendtner J., Data science in chemical engineering: applications to molecular science, Annual Review of Chemical and Biomolecular Engineering, 12 (2021) 15–37. DOI: 10.1146/annurev-chembioeng-101220-102232

2. Astray G., Mejuto J.C., Approach of different properties of alkylammonium surfactants using artificial intelligence and response surface methodology, Tenside Surfactants Detergents, 54 (2017) 132–140. DOI: 10.3139/113.110483

3. Bajimaya S.M., Park S., Wang G.-N., Predicting extrusion process parameters using neural networks, International Journal of Mechanical and Mechatronics Engineering, 1 (2007) 644–648. DOI: 10.5281/zenodo.1061615

4. Beinert S., Fragnière G., Schilde C., Kwade A., Multiscale simulation of fine grinding and dispersing processes: stressing probability, stressing energy and resultant breakage rate, Advanced Powder Technology, 29 (2018) 573–583. DOI: 10.1016/j.apt.2017.11.034

5. Bowler A.L., Bakalis S., Watson N.J., Monitoring mixing processes using ultrasonic sensors and machine learning, Sensors, 20 (2020) 1813. DOI: 10.3390/s20071813

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