De Jaegher Bram, Larumbe Eneko, De Schepper Wim, Verliefde Arne, Nopens Ingmar
BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
PaInT, Department of Green Chemistry and Technology, Ghent University, Coupure links 653, 9000 Ghent, Belgium.
Data Brief. 2020 May 25;31:105763. doi: 10.1016/j.dib.2020.105763. eCollection 2020 Aug.
This data paper aims to provide data on the effect of the process settings on the fouling of an electrodialysis pilot installation treating a sodium chloride solution (0.1 M and 0.2 M) in the presence of humic acid (1 g/L). This data was used by "Colloidal fouling in electrodialysis: a neural differential equations model" [1] to construct a predictive model and provides interpretive insights into this dataset. 22 electrodialysis fouling experiments were performed where the electrical resistance over the electrodialysis stack was monitored while varying the crossflow velocity (2.0 cm/s - 3.5 cm/s) in the compartments, the current applied (1.41 A - 1.91 A) to the stack and the salt concentration in the incoming stream. The active cycle was maintained for a maximum of 1.5 h after which the polarity was reversed to remove the fouling layer. Additional data is gathered such as the temperature, pH, flow rate, conductivity, pressure in the different compartments of the electrodialysis stack. The data is processed to remove the effect of temperature fluctuations and some filtering is performed. To maximise the reuse potential of this dataset, both raw and processed data are provided along with a detailed description of the pilot installation and sensor locations. The data generated can be useful for researchers and industry working on electrodialysis fouling and the modelling thereof. The availability of conductivity and pH in all compartments is useful to investigate secondary effects of humic acid fouling such as the eventual decrease in membrane permselectivity or water splitting effects introduced by the fouling layer.
本数据论文旨在提供有关工艺设置对电渗析中试装置污垢形成影响的数据,该装置在腐殖酸(1 g/L)存在的情况下处理氯化钠溶液(0.1 M和0.2 M)。“电渗析中的胶体污垢:神经微分方程模型”[1]使用了这些数据来构建预测模型,并对该数据集提供解释性见解。进行了22次电渗析污垢实验,在实验过程中监测电渗析堆的电阻,同时改变隔室中的错流速度(2.0 cm/s - 3.5 cm/s)、施加到电渗析堆的电流(1.41 A - 1.91 A)以及进料流中的盐浓度。活性循环最多维持1.5小时,之后反转极性以去除污垢层。还收集了其他数据,如电渗析堆不同隔室中的温度、pH值、流速、电导率、压力。对数据进行处理以消除温度波动的影响,并进行了一些过滤。为了最大限度地提高该数据集的重用潜力,同时提供了原始数据和处理后的数据以及中试装置和传感器位置的详细描述。所生成的数据对于研究电渗析污垢及其建模的研究人员和行业可能有用。所有隔室中电导率和pH值的可用性有助于研究腐殖酸污垢的二次效应,例如膜选择透过性的最终降低或污垢层引入的水分解效应。