Near infrared spectroscopy coupled with radial basis function neural network for at-line monitoring of Lactococcus lactis subsp. fermentation

AbstractIn our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation

The Impact of Bioaugmentation on the Performance and Microbial Community Dynamics of an Industrial-Scale Activated Sludge Sequencing Batch Reactor under Various Loading Shocks of Heavy Oil Refinery Wastewater

The stable and efficient operation of the activated sludge sequencing batch reactor (ASSBR) in heavy oil refineries has become an urgent necessity in wastewater biotreatment. Hence, we constructed a green and efficient solid bioaugmentation agent (SBA) to enhance the resistance of the reactor to loading shock. The impact of bioaugmentation on the performance and microbial community dynamics under three patterns of heavy oil refinery wastewater (HORW) loading shock (higher COD, higher toxicity, and higher flow rate) was investigated on an industrial-scale ASSBR. Results showed that the optimal SBA formulation was a ratio and addition of mixed bacteria Bacillus subtillis and Brucella sp., of 3:1 and 3.0%, respectively, and a glucose concentration of 5.0 mg/L. The shock resistance of ASSBR was gradually enhanced and normal performance was restored within 6–7 days by the addition of 0.2% SBA. Additionally, the removal efficiency of chemical oxygen demand and total nitrogen reached 86% and 55%, respectively. Furthermore, we found that Burkholderiaceae (12.9%) was replaced by Pseudomonadaceae (17.1%) in wastewater, and Lachnospiraceae (25.4%) in activated sludge was replaced by Prevotellaceae (35.3%), indicating that the impact of different shocks effectively accelerated the evolution of microbial communities and formed their own unique dominant bacterial families