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.
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07 January 2019
The original publication of this paper contains a mistake. Unfortunately, an author was inadvertently missed out, Constanza Arriagada had participated in the operation of the anaerobic digesters cited in the work and now as a PhD student, she is involved in the production of other publication
07 January 2019
The original publication of this paper contains a mistake. Unfortunately, an author was inadvertently missed out, Constanza Arriagada had participated in the operation of the anaerobic digesters cited in the work and now as a PhD student, she is involved in the production of other publication
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Acknowledgments
This research was conducted with the FONDECYT (Chile) No. 1140491, INNOVA (Chile) No. 12IDL2-13605, CONICYT-PCHA/Doctorado Nacional2016/21160226 and INNOVA (Chile) No 15VEIID-45613 grants.
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Alejo, L., Atkinson, J., Guzmán-Fierro, V. et al. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ Sci Pollut Res 25, 21149–21163 (2018). https://doi.org/10.1007/s11356-018-2224-7
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DOI: https://doi.org/10.1007/s11356-018-2224-7