Department of Soil and Water Systems, Twin Falls Research and Extension Center, University of Idaho, 315 Falls Avenue, Twin Falls, ID 83303-1827, USA.
Department of Soil and Water Systems, Twin Falls Research and Extension Center, University of Idaho, 315 Falls Avenue, Twin Falls, ID 83303-1827, USA.
Sci Total Environ. 2022 Dec 10;851(Pt 2):158321. doi: 10.1016/j.scitotenv.2022.158321. Epub 2022 Aug 26.
During anaerobic digestion (AD) of liquid dairy manure, organic nitrogen converts to ammonia nitrogen (NH-N) and subsequently escalates the NH-N concentrations in manure. Among different available NH-N removal processes treating anaerobically digested liquid dairy manure (ADLDM), vacuum thermal stripping is reported to be an effective technique. However, none of the studies have performed multi-parameter optimization, which is of utmost significance in maximizing process efficiency. In this study, critical operational parameters for vacuum thermal stripping of NH-N from ADLDM were optimized and modeled for the first time via integrating grey relational analysis (GRA)-based Taguchi design, response surface methodology (RSM), and RSM-artificial neural network (ANN). The initial experimental trials conducted using the GRA coupled with Taguchi L orthogonal array revealed the order of influence of the process parameters on NH-N removal as vacuum pressure (kPa) > temperature (°C) > treatment time (min) > mixing speed (rpm) > pH. The values of the first three most influential operating parameters were then further optimized and modeled using RSM and RSM-ANN models. Under the optimized conditions (temperature: 69.6 °C, vacuum pressure: 43.5 kPa, and treatment time: 87.65 min), the NH-N removal efficiency of 93.58 ± 0.59 % was experimentally observed and was in line with the RSM and RSM-ANN models' predicted values. While the RSM-ANN model showed a better prediction potential than did the RSM model when compared statistically. Moreover, the nutrient contents (nitrogen, N and sulfur, S) of the recovered NH-N as ammonium sulfate ((NH)SO) were in reasonable agreement with the market-available (NH)SO fertilizer. The results presented in this study provide important insights into improving the treatment process performance and will help design and operate future pilot- and full-scale vacuum thermal stripping processes in dairy farms.
在液态奶牛粪污的厌氧消化（AD）过程中，有机氮转化为氨氮（NH-N），从而使粪污中的 NH-N 浓度升高。在处理厌氧消化液态奶牛粪污（ADLDM）的不同可用 NH-N 去除工艺中，真空热汽提法被报道为一种有效的技术。然而，目前还没有研究进行多参数优化，这对于最大限度地提高工艺效率至关重要。在本研究中，首次通过整合灰色关联分析（GRA）-基于 Taguchi 设计、响应面法（RSM）和 RSM-人工神经网络（ANN），对从 ADLDM 中真空热汽提去除 NH-N 的关键操作参数进行了优化和建模。使用 GRA 与 Taguchi L 正交数组进行的初始实验表明，工艺参数对 NH-N 去除的影响顺序为真空压力（kPa）>温度（°C）>处理时间（min）>混合速度（rpm）>pH。然后，使用 RSM 和 RSM-ANN 模型进一步优化和建模前三个最具影响力的操作参数的值。在优化条件下（温度：69.6°C，真空压力：43.5kPa，处理时间：87.65min），实验观察到 NH-N 去除效率为 93.58±0.59%，与 RSM 和 RSM-ANN 模型的预测值一致。虽然 RSM-ANN 模型在统计学上比 RSM 模型具有更好的预测潜力。此外，回收的 NH-N 作为硫酸铵（(NH4)2SO4）的养分含量（氮，N 和硫，S）与市场上可用的（NH4）2SO4肥料基本一致。本研究的结果为提高处理工艺性能提供了重要的见解，并将有助于设计和运行未来奶牛场的真空热汽提工艺的中试和全规模。
