Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques

Luz Alejo, John Atkinson, Víctor Guzmán-Fierro, Marlene Roeckel

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes.

Original languageEnglish
Pages (from-to)21149-21163
Number of pages15
JournalEnvironmental Science and Pollution Research
Volume25
Issue number21
DOIs
StatePublished - 1 Jul 2018
Externally publishedYes

Keywords

  • Anaerobic digestion
  • Machine learning
  • Prediction methods
  • Protein degradation
  • Support vector machines

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