Machine learning revealing overlooked conjunction of working volume and mixing intensity in anammox optimization

• Published in: Water research (Oxford) Vol. 266; p. 122344
• Main Authors: Ji, Bohua, Kuok, Sin-Chi, Hao, Tianwei
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.11.2024
• Subjects: Algorithms; ammonium; Anaerobic Ammonia Oxidation; anaerobic ammonium oxidation; Anammox optimization; artificial intelligence; Bayes Theorem; Bayesian inference; Bayesian theory; Datasets as Topic; fluid mechanics; Hydrodynamics; Input power; Machine Learning; Mixing intensity; nitrates; nitrites; nitrogen; temperature; Velocity field; water; Input power; Bayesian inference; Mixing intensity; Velocity field; Anammox optimization
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2024.122344

•Bayesian nonparametric general regression was used for anammox optimization.•Working volume was identified as a neglected key process optimization variable.•Correlation among working volume-mixing intensity-nitrogen removal was established.•Input power-based anammox reactor design and operating conditions were derived. Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter. By integrating the BNGR findings with computational fluid dynamics simulation, which assessed mixing properties, it became feasible to conclude that working volume and mixing intensity co-regulated flow fields in reactors and had a significant influence on anammox performance. Furthermore, this study experimentally validated how mixing intensity affected performance, and specific input power (x), a parameter that conjugates both working volume and mixing intensity, was used to establish the relationship with ammonium removal rate (NH4+-N RR, y) y = 49.90x+1.97 (R2 = 0.94). With specific input power increased from 3.4 × 10−4 to 2.6 × 10−2 kW m−3, the ammonium removal rate exhibited a rise from 1.8 to 3.2 mg L−1h−1. Following, a relationship among input power-working volume-nitrogen removal rate was also established with a view to determining the design variables for anammox reactor. Consequently, the study highlighted the necessity to consider the working volume-mixing intensity correlation when optimizing the anammox process. [Display omitted]