RESEARCH ON THE VOLUME WEIGHT OF FOAMED COMPOSITES BASED ON BRICK WASTE USING NEURAL NETWORKS
- Hits: 177
Volume 5 (1), June 2022, Pages 87-93
A.R. Aliev1,2, Y.N. Gahramanli1, S.I. Aliyev1
2 Institute of Mathematics and Mechanics of Ministry of Science and Education of the Republic of Azerbaijan, Baku, Azerbaijan
This paper described the opportunity to use artificial neural networks to predict the chemical reaction result under given conditions. Applied three layers neural network for prediction of the mass content of alkaline trained using the results of the chemical reactions. As inputs were used values of the chemical quantities before the reaction and output values of the chemical quantities after the reaction. HPC technologies and multi-worker technology were used for accurate results.
Data Prediction, Parallelization, Neural Networks, Multi Worker Processing.
El-Naggar, K. A. M., Amin, S. K., El-Sherbiny, S. A., & Abadir, M. F. (2019). Preparation of geopolymer insulating bricks from waste raw materials. Construction and Building Materials, 222, 699-705.
European Comission (2022). EU economy greenhouse gases near pre-pandemic levels https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20220215-1
Gahramanli, Y.N., Hajiyeva, R.Sh., Hasanova, M.B., Samedzadeh, B.A. (2020) An obtaining of foamed composites on the basis of ceramic wastes and researching their volume weight. Azerbaijan journal of chemical news, 1, 4-13.
Gahramanli, Y.N., Samedzade, B.A., Hajiyeva, R.Sh., Hasanova, M.B. (2020). Izuchenie-osobennostej-izmeneniya-obemnoj-massy-vo-vspenennykh-betonnykh-kompoziciyakh. Shlyakhi rozvitku nauki v suchasnikh krizovikh-umovakh-tezi-dop-i-m-zhnarodno-naukovo-praktichno-nternet-konferencii, 442.
Gahramanli, Y.N., Samedzade, B.A., Hajiyeva, R.Sh., Hasanova, M.B. (2020a) Issledovanie izmeneniya obemnoj massy zhidkostekolnykh penobetonnykh kompozicij. Proceedings of the online conference of young scientists and PhD students dedicated to 100 years anniversary of ADNSU, 171-176.
Raj, A., Sathyan, D., & Mini, K. M. (2019). Physical and functional characteristics of foam concrete: A review. Construction and Building Materials, 221, 787-799.
Tensorflow (2022). Multi-worker training with Keras https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras