Abstract
During anaerobic digestion (AD) of liquid dairy manure, organic nitrogen converts to ammonia nitrogen (NH3-N) and subsequently escalates the NH3-N concentrations in manure. Among different available NH3-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 NH3-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 L16 orthogonal array revealed the order of influence of the process parameters on NH3-N removal as vacuum pressure (kPa) > temperature (degrees 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 degrees C, vacuum pressure: 43.5 kPa, and treatment time: 87.65 min), the NH3-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 NH3-N as ammonium sulfate ((NH4)2SO4) were in reasonable agreement with the market-available (NH4)2SO4 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.