Obtaining Bricks Using Silicon-Based Materials: Experiments, Modeling and Optimization with Artificial Intelligence Tools

Costel Anton, Florin Leon, Marius Gavrilescu, Elena-Niculina Drăgoi, Sabina-Adriana Floria, Silvia Curteanu and Cătălin Lisa
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Costel Anton: Faculty of Chemical Engineering and Environmental Protection, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 73, 700050 Iași, Romania
Florin Leon: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 27, 700050 Iasi, Romania
Marius Gavrilescu: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 27, 700050 Iasi, Romania
Elena-Niculina Drăgoi: Faculty of Chemical Engineering and Environmental Protection, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 73, 700050 Iași, Romania
Sabina-Adriana Floria: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 27, 700050 Iasi, Romania
Silvia Curteanu: Faculty of Chemical Engineering and Environmental Protection, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 73, 700050 Iași, Romania
Cătălin Lisa: Faculty of Chemical Engineering and Environmental Protection, “Gheorghe Asachi” Technical University of Iasi, Bd. Mangeron, No. 73, 700050 Iași, Romania

Mathematics, 2022, vol. 10, issue 11, 1-21

Abstract: In the brick manufacturing industry, there is a growing concern among researchers to find solutions to reduce energy consumption. An industrial process for obtaining bricks was approached, with the manufacturing mix modified via the introduction of sunflower seed husks and sawdust. The process was analyzed with artificial intelligence tools, with the goal of minimizing the exhaust emissions of CO and CH 4 . Optimization algorithms inspired by human and virus behaviors were applied in this approach, which were associated with neural network models. A series of feed-forward neural networks have been developed, with 6 inputs corresponding to the working conditions, one or two intermediate layers and one output (CO or CH 4 , respectively). The results for ten biologically inspired algorithms and a search grid method were compared successfully within a single objective optimization procedure. It was established that by introducing 1.9% sunflower seed husks and 0.8% sawdust in the brick manufacturing mix, a minimum quantity of CH 4 emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions.

Keywords: bricks; artificial neural networks; optimization algorithms; biologically inspired methods; modeling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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