Abstract:

As a common form of renewable energy with abundant reserves, environmental friendliness and easy access, biomass has aroused general interests in the field of energy research and utilization. Biomass moisture content is a significant factor for the efficient utilization of biomass resources, therefore drying becomes a necessary step before the biomass utilization.Fluidized bed has been widely used in the drying process due to its good heat and mass transfer characteristics. For the online monitoring of the biomass drying process, arc-shaped electrostatic sensor arrays along with a LSTM (long short-term memory) neural network are deployed to establish a neural network model for the prediction of biomass moisture content in the fluidized bed dryer. Experimental tests under different conditions are carried out on a lab-scale fluidized bed dryer to obtain data for model training and testing. Besides, the LSTM neural network model is determined through the optimization of the model parameters. Compared with normal RNN (recurrent neural network) model,the prediction results from the LSTM neural network model have smaller mean relative error, which can predict the biomass moisture content in the fluidized bed dryer with certain accuracy.

Key words: LSTM model, biomass, moisture content, neural network, electrostatic sensor arrays, fluidized bed

摘要：

生物质作为一种储量丰富、环境友好且易于获取的可再生能源，日渐成为能源研究利用领域的热点。生物质湿度是影响生物质利用效率的关键因素，因此干燥是生物质利用之前的必要步骤。流化床由于其良好的传热传质特性，在干燥过程中得到了广泛的应用。为了实时监测生物质颗粒的干燥过程，利用弧形静电传感器阵列，结合用于时间序列建模的长短期记忆（LSTM）神经网络，实现了流化床干燥器内生物质颗粒湿度的预测。在实验室规模的流化床干燥器上进行了多工况实验获取训练和测试数据，通过模型参数优化确定了LSTM模型。通过与标准循环神经网络（RNN）模型的预测结果的对比表明，LSTM神经网络模型的平均相对误差较小，能够较为准确地预测流化床干燥器内生物质颗粒的湿度。

关键词: LSTM模型, 生物质, 湿度, 神经网络, 静电传感器阵列, 流化床