The synthesis of α-calcium sulfate hemihydrate (α-CSH) from flue gas desulfurization (FGD)gypsum is a good way to realize the comprehensive utilization of FGD gypsum. To obtainα-CSH with the satisfactory performanc...The synthesis of α-calcium sulfate hemihydrate (α-CSH) from flue gas desulfurization (FGD)gypsum is a good way to realize the comprehensive utilization of FGD gypsum. To obtainα-CSH with the satisfactory performances, a facile hydrothermal-aging pretreatment process for FGD gypsum raw materials was proposed, where FGD gypsum was firstly hydrothermally converted to α-CSH whiskers, and α-CSH whiskers were further hydrated to synthesize CaSO4·2H2O (CSD) by aging under the regulation of N,N'-methylenebisacrylamide (MBA). The effects of aging time, MBA addition, aging temperature, and pH on the morphology of the synthesized CSD were investigated. The synthesized CSD crystals exhibit highly uniform prismatic morphology with the length of ca 100μm and the whiteness of 91.56%. The regulation mechanism of MBA was also illustrated. The synthesized CSD crystals with prismatic morphology were further used as raw materials to synthesize the short columnar α-CSH. The absolute dry compressive strength of paste prepared from the short columnar α-CSH is 40.85 MPa, which reaches α40 strength grade.展开更多
为提高锂离子电池在复杂工况下的预测能力和建模精度,提出一种基于滑动窗口和长短时记忆(long short term memory,LSTM)神经网络的锂离子电池建模方法。首先建立了基于神经网络的锂离子电池模型,确定了神经网络的基本结构,通过LSTM层、...为提高锂离子电池在复杂工况下的预测能力和建模精度,提出一种基于滑动窗口和长短时记忆(long short term memory,LSTM)神经网络的锂离子电池建模方法。首先建立了基于神经网络的锂离子电池模型,确定了神经网络的基本结构,通过LSTM层、向量拼接层和全连接层分别实现了时序特征提取、特征融合和回归预测。然后提出了滑动窗口的输入向量处理方法,滑动窗口每次向前推进一个时间点,通过限制时间窗口内所能处理的最大信元数对数据量进行限制,为多个LSTM层的并行计算和深隐层的拼接层和全连接层预留了计算量的裕度,实现了对模型中循环网络层深度的优化选择。为解决模型在多工况下运行的泛化问题,提出使用离线数据集的预训练和在线数据的参数修正的训练方法,通过大量离线数据集的反复训练,使模型学习电池的共性部分;再使用部分在线数据,对网络参数进行调整,将其应用于预测中。最后使用恒流/恒压、随机电流脉冲、大功率脉冲等多个工况的数据分别进行测试。结果表明,基于长短时记忆神经网络的建模方法能够准确预测电池输出电压和荷电状态。展开更多
基金Funded by National Natural Science Foundation of China(No.22008049)Natural Science Foundation of Hebei Province,China (Nos.B2020202081 and B2018202330)+1 种基金Key Laboratory of Gas Hydrate,Guangzhou Institute of Energy Conversion,Chinese Academy of Sciences,China (No.E029kf1601)Research Fund Program of Science and Technology of Colleges and Universities of Hebei Province,China (No.QN2019012)。
文摘The synthesis of α-calcium sulfate hemihydrate (α-CSH) from flue gas desulfurization (FGD)gypsum is a good way to realize the comprehensive utilization of FGD gypsum. To obtainα-CSH with the satisfactory performances, a facile hydrothermal-aging pretreatment process for FGD gypsum raw materials was proposed, where FGD gypsum was firstly hydrothermally converted to α-CSH whiskers, and α-CSH whiskers were further hydrated to synthesize CaSO4·2H2O (CSD) by aging under the regulation of N,N'-methylenebisacrylamide (MBA). The effects of aging time, MBA addition, aging temperature, and pH on the morphology of the synthesized CSD were investigated. The synthesized CSD crystals exhibit highly uniform prismatic morphology with the length of ca 100μm and the whiteness of 91.56%. The regulation mechanism of MBA was also illustrated. The synthesized CSD crystals with prismatic morphology were further used as raw materials to synthesize the short columnar α-CSH. The absolute dry compressive strength of paste prepared from the short columnar α-CSH is 40.85 MPa, which reaches α40 strength grade.
文摘为提高锂离子电池在复杂工况下的预测能力和建模精度,提出一种基于滑动窗口和长短时记忆(long short term memory,LSTM)神经网络的锂离子电池建模方法。首先建立了基于神经网络的锂离子电池模型,确定了神经网络的基本结构,通过LSTM层、向量拼接层和全连接层分别实现了时序特征提取、特征融合和回归预测。然后提出了滑动窗口的输入向量处理方法,滑动窗口每次向前推进一个时间点,通过限制时间窗口内所能处理的最大信元数对数据量进行限制,为多个LSTM层的并行计算和深隐层的拼接层和全连接层预留了计算量的裕度,实现了对模型中循环网络层深度的优化选择。为解决模型在多工况下运行的泛化问题,提出使用离线数据集的预训练和在线数据的参数修正的训练方法,通过大量离线数据集的反复训练,使模型学习电池的共性部分;再使用部分在线数据,对网络参数进行调整,将其应用于预测中。最后使用恒流/恒压、随机电流脉冲、大功率脉冲等多个工况的数据分别进行测试。结果表明,基于长短时记忆神经网络的建模方法能够准确预测电池输出电压和荷电状态。
