Abstract

Liquid-solid fluidization technique is being applied where low-grade coal or minerals enrichment is mostly density-based. Static and dynamic behavior of particles in a fluid medium has been extensively investigated over the years because of its dynamic applications across various industries. In this work, bed characterization studies and experiments have been conducted to study coal washing ability of the liquid-solid fluidized bed separator. Results have been recorded in terms of ash rejection%, combustible recovery% and separation efficiency%. Minimum fluidization velocity and pressure drop values have been predicted using existing theoretical correlations and compared with the experimental values. A three-layered (4:5:3) feedforward back-propagation (FFBP) neural network model was developed using Levenberg-Marquardt algorithm, LOGSIG and MSE as training, transfer and performance functions respectively. Garson’s algorithm and connection weight approach have been employed for sensitivity analysis to interpret the neural network results physically. Coefficients of correlation, all R (including training, validation & testing datasets) obtained for outputs ash rejection (R = 0.9960), combustible recovery (R = 0.9952) and separation efficiency (R = 0.9944) suggest that predicted values are in agreement with the experimental values and the developed model is a good fit.

摘要

低品位煤或矿物富集主要基于密度的地方正在应用液固流化技术。多年来，流体介质中粒子的静态和动态行为因其在各个行业的动态应用而得到广泛研究。在这项工作中，进行了床层特性研究和实验，以研究液固流化床分离器的洗煤能力。结果已记录在除灰率%、可燃物回收率% 和分离效率% 方面。最小流化速度和压降值已使用现有的理论相关性进行了预测，并与实验值进行了比较。使用 Levenberg-Marquardt 算法开发了一个三层 (4:5:3) 前馈反向传播 (FFBP) 神经网络模型，LOGSIG 和 MSE 分别作为训练、传输和性能函数。Garson 的算法和连接权重方法已用于灵敏度分析，以从物理上解释神经网络结果。相关系数、所有 R（包括训练、验证和测试数据集）获得的输出灰分去除率 (R = 0.9960)、可燃回收率 (R = 0.9952) 和分离效率 (R = 0.9944) 表明预测值与实验值和开发的模型是一个很好的拟合。