Optimization and modeling of ammonia nitrogen removal from anaerobically digested liquid dairy manure using vacuum thermal stripping process

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.

Automated summary

This study optimizes and models the critical operational parameters for vacuum thermal stripping of NH3-N from anaerobically digested liquid dairy manure using grey relational analysis (GRA)-based Taguchi design, response surface methodology (RSM), and RSM-artificial neural network (ANN). The results show that under the optimized conditions, NH3-N removal efficiency reaches 93.58%. The recovered NH3-N also has nutrient contents that are in line with the market-available (NH4)2SO4 fertilizer.