Modifying the microstructure of algae-based active carbon and modelling supercapacitors using artificial neural networks

Abstract

An artificial neutral network has been applied to predict the specific capacitance of biomass-carbon supercapacitors.

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

“…The neurons in each layer receive input data that are passed through a weighted connection. The total input value received by the neuron would be compared with the threshold and then processed by activation function to generate the output of the neuron [37,38]. after centralization of data set, we can proceed the eigenvalue decomposition of covariance matrices , calculating the eigenvector corresponding the large contributing eigenvalue ( ≤ ≤ ) to build projection matrix:…”

“…The neurons in each layer receive input data that are passed through a weighted connection. The total input value received by the neuron would be compared with the threshold and then processed by activation function to generate the output of the neuron [37,38]. The threshold can be replaced by bias input , is the connection weight [39], therefore the mathematical model of ANN is given by:…”

Artificial Intelligence for Contemporary Chemistry Research

“…[32]. 马雷等 [5] 用酸浸法从念珠藻中合成了可被制作成超 级电容器的活性炭材料, 应用神经网络和随机森林两种 机器学习方法, 预测了活性炭材料的各个特征参数与超 级电容器比电容之间的关系, 分析比较了活性碳材料各 个特征参数之间的相对重要性. 他们首先通过多种分析 测试方法, 例如使用热重分析仪检测碳化期间的重量变 化; 扫面电子显微镜和透射电子显微镜表征样品的表面 形貌和微观结构; X 射线衍射仪测定结晶结构; 利 图 9 (a) NFAC-x 合成方式.…”

“…他们首先通过多种分析 测试方法, 例如使用热重分析仪检测碳化期间的重量变 化; 扫面电子显微镜和透射电子显微镜表征样品的表面 形貌和微观结构; X 射线衍射仪测定结晶结构; 利 图 9 (a) NFAC-x 合成方式. (b) 超级电容器电容分析的人工神经网络 模型 [5] Figure 9 (a) NFAC-x synthesis method. (b) Artificial neural network model for supercapacitor capacitance analysis [5] .…”

“…国内化学研究领域对现代人工智能认知度尚比较 有限, 将机器学习引入化学研究的工作刚刚起步, 也取 得了一定的成果. 如马雷等 [5] 利用神经网络等方法, 基 于大量实验数据的探索, 找到了电极材料参数与超级电 容器比电容性能的相关关系和相对重要性. 孙宽等 [6] 用 人工智能筛选 PV 材料.…”