Optimization of Catalyst for Methanol Synthesis by a Combinatorial Approach Using a Parallel Activity Test and Genetic Algorithm Assisted by a Neural Network
Abstract
Optimization of a Cu−Zn−Al−Sc oxide catalyst for methanol synthesis from syngas was performed by a combinatorial approach using a genetic algorithm (GA) with/without various neural networks (NNs). Performance of optimum catalysts found by the various methods was compared. The catalyst with maximum activity was found by the combination of GA and radial basis function network (RBFN) in the optimization of Cu−Zn−Al−Sc oxide catalyst composition. Therefore this method was found to be the most efficacious method. In addition, we conducted the optimization on the RBFN with a larger population in the GA program to find the best catalyst in the early stage of evolution. On the other hand, we also tried to optimize simultaneously both composition and calcination temperature of a Cu−Zn oxide catalyst. In that case, the optimum catalyst was found by the combination of GA and back-propagation network. Thus, GA is a more robust tool when it is combined with NNs.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
An evolutionary approach in the combinatorial selection and optimization of catalytic materials
Wolf, D., Buyevskaya, O. V., & Baerns, M. (2000) · Applied Catalysis A General · doi:10.1016/s0926-860x(00)00643-8 · Cited by 210 · see citing works
An Infrared Study of Methanol Synthesis from CO2 on Clean and Potassium-Promoted Cu/SiO2
Clarke, D. B., & Bell, A. T. (1995) · Journal of Catalysis · doi:10.1006/jcat.1995.1173 · Cited by 159 · see citing works
Applications of combinatorial methods in catalysis
Hagemeyer, A., Jandeleit, B., Liu, Y., et al. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00886-9 · Cited by 152 · see citing works
Heinz, U., Hegetschweiler, Κ., Acklin, P., Faller, B., Lattmann, R., & Schnebli, H. P. (1999) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19990903)38:17 · Cited by 128 · see citing works
Buyevskaya, O. V., Brückner, A., Kondratenko, E. V., et al. (2001) · Catalysis Today · doi:10.1016/s0920-5861(01)00329-7 · Cited by 108 · see citing works
Neural network as a tool for catalyst development
Hattori, T., & Kito, S. (1995) · Catalysis Today · doi:10.1016/0920-5861(94)00148-u · Cited by 107 · see citing works
Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling
Huang, K., Chen, F., & Lü, D.-W. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00659-7 · Cited by 105 · see citing works
Ethylene and propene by oxidative dehydrogenation of ethane and propane
Buyevskaya, O. V., Wolf, D., & Baerns, M. (2000) · Catalysis Today · doi:10.1016/s0920-5861(00)00411-9 · Cited by 99 · see citing works
Hunting for better catalysts and materials-combinatorial chemistry and high throughput technology
Scheidtmann, J., Weiss, P. W., & Maier, W. F. (2001) · Applied Catalysis A General · doi:10.1016/s0926-860x(01)00831-6 · Cited by 70 · see citing works
High-throughput synthesis and screening of catalytic materials
Rodemerck, U., Wolf, D., Buyevskaya, O. V., et al. (2001) · Chemical Engineering Journal · doi:10.1016/s1385-8947(00)00348-x · Cited by 61 · see citing works
Omata, K., Ishiguro, G., & Yamada, M. (2000) · 石油学会誌 · doi:10.1627/jpi1958.43.317 · Cited by 15 · see citing works
Self-citationOptimization of Cu-Zn-Al Oxide Catalyst for Methanol Synthesis Using Genetic Algorithm.
Omata, K., Umegaki, T., Ishiguro, G., et al. (2001) · 石油学会誌 · doi:10.1627/jpi1958.44.327 · Cited by 14 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617 · see citing works
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Discovery and Optimization of Materials Using Evolutionary Approaches
Le, T. C., & Winkler, D. A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00691 · Cited by 204 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Watanabe, Y., Umegaki, T., Hashimoto, M., Omata, K., & Yamada, M. (2004) · Catalysis Today · doi:10.1016/j.cattod.2004.02.001 · Cited by 51 · see citing works
Self-citation
- Authors
- Umegaki, T., Watanabe, Y., Nukui, N., Omata, K., & Yamada, M.
- Affiliations
- Tohoku University (JP)
- ORCID
- Umegaki, Tetsuo
- Venue
- Energy & Fuels
- Publisher
- American Chemical Society (ACS)
- Published
- 2003-06-06
- Volume
- 17
- Issue
- 4
- Pages
- 850-856
- ISSN
- 0887-0624 (ISSN-L)
- 1520-5029
- OpenAlex
- W2019853123
- Cited by
- 34
- References
- 12
- Citations by year
- 2025: 2 · 2022: 3 · 2021: 6 · 2020: 1 · 2019: 1 · 2017: 2 · 2016: 2 · 2012: 1
- FWCI
- 1.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy & Fuels (publishedVersion)
- Topics
- Physical Sciences › Chemical Engineering › Catalysis › Catalysts for Methane Reforming
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- methanol synthesis, syngas, genetic algorithm, artificial neural networks, radial basis function neural network, BP neural network, catalyst composition optimization, calcination temperature optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
