Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway; Cambi Group AS, Asker, Norway.
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway; NIBIO, Norwegian Institute of Bioeconomy Research, Ås, Norway.
Water Res. 2019 Jul 1;158:350-358. doi: 10.1016/j.watres.2019.04.037. Epub 2019 Apr 23.
Efficient digestate dewatering is crucial to reduce the volume and transportation cost of solid residues from anaerobic digestion (AD) plants. Large variations in dewatered cake solids have been reported and predictive models are therefore important in design and operation of such plants. However, current predictive models lack validation across several digestion substrates, pre-treatments and full-scale plants. In this study, we showed that thermogravimetric analysis is a reliable prediction model for dewatered cake solids using digestates from 15 commercial full-scale plants. The tested digestates originated from different substrates, with and without the pre-AD thermal hydrolysis process (THP). Moreover, a novel combined physicochemical parameter (C/N•ash) characterizing different digestate blends was identified by multiplying the C/N ratio with ash content of the dried solids. Using samples from 22 full-scale wastewater, food waste and co-waste plants, a linear relationship was found between C/N•ash and predicted cake solids for digestates with and without pre-AD THP. Pre-AD THP improved predicted cake solids by increasing the amount of free water. However, solids characteristics like C/N ratio and ash content had a more profound influence on the predicted cake solids than pre-AD THP and type of dewatering device. Finally, C/N•ash was shown to have a linear relationship to cake solids and reported polymer dose from eight full-scale pre-AD THP plants. In conclusion, we identified the novel parameter C/N•ash which can be used to predict dewatered cake solids regardless of dewatering device and sludge origin.
高效的消化液脱水对于减少厌氧消化（AD）厂固体残留物的体积和运输成本至关重要。据报道，脱水饼固体的变化很大，因此预测模型对于这些工厂的设计和运行非常重要。然而，目前的预测模型缺乏在多种消化底物、预处理和全规模工厂中的验证。在这项研究中，我们表明，热重分析是一种使用来自 15 个商业全规模工厂的消化液来预测脱水饼固体的可靠预测模型。测试的消化液来自不同的底物，有和没有预 AD 热解处理（THP）。此外，通过将 C/N 比与干燥固体的灰分相乘，确定了一种新的表征不同消化液混合物的组合物理化学参数（C/N•ash）。使用来自 22 个全规模废水、食品废物和共废物工厂的样本，发现对于有和没有预 AD THP 的消化液，C/N•ash 与预测的饼固体之间存在线性关系。预 AD THP 通过增加自由水的量来提高预测的饼固体。然而，固体特性，如 C/N 比和灰分含量，对预测的饼固体的影响比预 AD THP 和脱水设备的类型更为深远。最后，C/N•ash 与饼固体呈线性关系，并显示了来自八个全规模预 AD THP 工厂的报告聚合物剂量。总之，我们确定了新的参数 C/N•ash，它可以用于预测无论脱水设备和污泥来源如何的脱水饼固体。
