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

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 (°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 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.

Keywords: Ammonia nitrogen removal; Artificial neural network; Dairy manure; Grey relational analysis-based Taguchi design; Optimization; Response surface methodology.