Modelling and optimization of the pore structure of carbon aerogels using an artificial neural network
Source abstract
Abstract
An intelligent simulation method for predicting and optimizing the pore structure of carbon aerogels is proposed by using an artificial neural network (ANN) algorithm. The ANN model has been optimized based on an improved genetic algorithm from six typical training algorithms. The volumes and diameters of pores in the simulated samples are predicted by the optimized ANN model, which shows correlation coefficients R2 of 0.992 and 0.981 and root-mean-square prediction errors (RMSPE) of 0.077 and 0.054 between the predicted and experimental values for the volumes and diameters of pores, respectively. The proposed model is expected to have practical applications in the pore structure control of carbon aerogels.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Aerogels and thermal insulation · Surface Modification and Superhydrophobicity · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (20)
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