Saldarriaga Juan F
Dept. Civil and Environmental Engineering, Universidad de los Andes, Carrera 1 Este #19A-40, Bogotá 111711, Colombia.
Heliyon. 2022 Nov 17;8(11):e11611. doi: 10.1016/j.heliyon.2022.e11611. eCollection 2022 Nov.
Artificial neural networks have been used since the last decade as a satisfactory alternative for the prediction of the fluid-dynamic behavior of particles. The aim of this work has been to develop a model based on artificial neural networks (ANN) suitable for quantifying the influence of multiple factors on the heat transfer rate in a conical spouted bed reactor. The Nusselt module has been taken as an exit point and nine input factors have been evaluated, among which are the height of the bed, the diameter of the contactor, the angle of the cone, and the minimum spouting speed, among others. The model has been found to fit appropriately to the equations proposed in the literature and can be used as a suitable model to predict the behavior of heat transfer in conical spouted bed reactors operating with biomass.
自上世纪九十年代以来，人工神经网络就被用作预测颗粒流体动力学行为的一种令人满意的替代方法。这项工作的目的是开发一种基于人工神经网络（ANN）的模型，该模型适用于量化多个因素对锥形喷动床反应器中传热速率的影响。努塞尔特模块被作为一个输出点，并评估了九个输入因素，其中包括床层高度、接触器直径、锥体角度和最小喷动速度等。已发现该模型与文献中提出的方程拟合良好，可作为预测以生物质为原料运行的锥形喷动床反应器中传热行为的合适模型。
