Intelligent Optimization of Material Mixing Ratio and Process Parameters for Aerated Concrete

Abstract

To determine the optimal material mixing ratio of and process parameters for aerated concrete products, we adopted various combinations of material mixing ratio and process parameters to test aerated concrete according to the characteristics of local material and process equipment in this study, and obtained the strength and density of the aerated concrete. Using artificial neural network, we built a three-layer neural network model, which was trained based on the data of test samples to obtain a neural network based model system. Sample test showed that the predicted values of the model system fit the test values well; we utilized this system to analyze the material mixing ratio of and process parameters for aerated concrete products, and obtained their optimization results. The strength and density of the aerated concrete manufactured with the optimized parameters reached the desired targets. This method has some reference value for instructing the production of aerated concrete.