Potential of multivariate statistical process monitoring based on the biogas composition to detect free ammonia intoxication in anaerobic reactors

• Published in: Biochemical engineering journal Vol. 140; pp. 17 - 28
• Main Authors: Lemaigre, Sébastien, Adam, Gilles, Gerin, Patrick A., Noo, Anaïs, De Vos, Bénédicte, Klimek, Dominika, Goux, Xavier, Calusinska, Magdalena, Delfosse, Philippe
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.12.2018
• Subjects: Ammonia intoxication; Anaerobic digestion; Biogas composition; Monitoring model; Multivariate statistical process control; Principal components analysis; TAN; Ammonia intoxication; LN; FM; SPE; Multivariate statistical process control; HRT; HNR; IPV; Anaerobic digestion; VFA; LNR; OTU; Biogas composition; OLR; T; HDS; GC; Monitoring model; SPElim; AD; BMP; rRNA; Principal components analysis; TA; PCA; TIC; PCA-MSPC; FAN; PC; DNA; RU-PCA-MSPC; E-nose; MSPC; VS; TS; UCL
• ISSN: 1369-703X; 1873-295X
• DOI: 10.1016/j.bej.2018.08.018

•Free ammonia nitrogen (FAN) extreme intoxication was reached at ∼5 gFAN Lslurry−1.•Multivariate statistical process control (MSPC) models were built using only the biogas composition.•Recursively updated MSPC models detected a decrease of reactor performance early.•Early signs of process perturbation coincided to a drastic change in the reactor microbiome.•These models performance was negatively affected by a drastic shift in the reactor microbiome. Three anaerobic digestion reactors were initially maintained at steady-state with low nitrogen input, then progressively exposed to increasing nitrogen input until extreme ammonia intoxication, leading to interruption of biogas production. Such interruption occurred for a free ammonia nitrogen (FAN) concentration of about 5 g Lslurry−1. A fourth reactor used as a control was maintained at low nitrogen steady-state. The biogas composition (CH4, CO2, H2 and H2S) was recorded over time and data were fed into a multivariate statistical process control model based on principal components analysis (PCA-MSPC). Static PCA-MSPC failed to distinguish between the actual reactor performance decrease and the drift of the in-control process baseline. However, recursively updated PCA-MSPC (effective memory length of 14 days, i.e. 0.25 hydraulic retention time) allowed detection of the early signs of FAN induced process dysfunction, which were simultaneous with drastic changes in the microbiome of the replicate reactors. However, the process control model was not able to adapt to a drastic modification of the microbiome. Propionate continuously accumulated since the detection of the first signs of process dysfunction.