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Alejo, L., Atkinson, J., Arriagada, C., Guzman-Fierro, V., & Roeckel, M. (2019). Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques (vol 25, pg 21149, 2018) (Vol. 26). Springer Heidelberg.
Abstract: 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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Alejo, L., Atkinson, J., Guzman-Fierro, V., & Roeckel, M. (2018). Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res., 25(21), 21149–21163.
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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Jara-Munoz, P., Guzman-Fierro, V., Arriagada, C., Campos, V., Campos, J. L., Gallardo-Rodriguez, J. J., et al. (2019). Low oxygen start-up of partial nitrification-anammox process: mechanical or gas agitation? J. Chem. Technol. Biotechnol., 94(2), 475–483.
Abstract: BACKGROUND Partial nitrification-anammox (PN-A) is a widely recognized technology to remove nitrogen from different types of wastewater. Low oxygen concentration is the most used strategy for PN-A start-up, but stability problems arise during the operation; thus, in the present study the effects of the type of agitation, oxygenation and shear stress on the sensitivity, energy consumption and performance were evaluated. Recognition of these parameters allows considered choice of the design of an industrial process for nitrogen abatement. RESULTS A mechanically agitated reactor (MAR) was compared to a stable, long-term operation period bubble column reactor (BCR), both started under low dissolved oxygen concentration conditions. MAR microbial assays confirmed the destruction of the nitrifying layer and an imbalance of the entire process when the oxygen to nitrogen loading ratio (O-2:N) decreased by 25%. The granule sedimentation rate and specific anammox activity were 17% and 87% higher (respectively) in BCR. Economic analysis determined that the cost of aeration for the MAR and for the BCR were 23.8% and 1% of the total PN-A energy consumption, respectively. CONCLUSIONS The BCR showed better results than the MAR. This study highlights the importance of type of agitation, oxygenation and shear stress for industrial-scale PN-A designs. (c) 2018 Society of Chemical Industry
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