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

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 CH4. 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 CH4, 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 CH4 emissions was obtained, while 0% sunflower seed husks and 0.5% sawdust were the minimum quantities for CO emissions.

Automated summary

In the brick manufacturing industry, researchers are concerned about reducing energy consumption. This study modified the manufacturing mix by introducing sunflower seed husks and sawdust, and used artificial intelligence tools along with optimization algorithms inspired by human and virus behaviors. The results showed that by adding a certain amount of sunflower seed husks and sawdust, the emissions of CH4 and CO can be minimized.