toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links
Author Alejo, L.; Atkinson, J.; Arriagada, C.; Guzman-Fierro, V.; Roeckel, M. pdf  doi
openurl 
  Title Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques (vol 25, pg 21149, 2018) Type
  Year 2019 Publication Environmental Science And Pollution Research Abbreviated Journal Environ. Sci. Pollut. Res.  
  Volume 26 Issue 5 Pages 5234-5234  
  Keywords  
  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.  
  Address [Alejo, Luz; Arriagada, Constanza; Guzman-Fierro, Victor; Roeckel, Marlene] Univ Concepcion, Dept Ingn Quim, Victor Lamas 1290,Casilla 160-C Correo 3, Concepcion, Chile, Email: john.atkinson@uai.cl;  
  Corporate Author Thesis  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0944-1344 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000458990300090 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 982  
Permanent link to this record
 

 
Author Alejo, L.; Atkinson, J.; Guzman-Fierro, V.; Roeckel, M. pdf  doi
openurl 
  Title Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques Type
  Year 2018 Publication Environmental Science And Pollution Research Abbreviated Journal Environ. Sci. Pollut. Res.  
  Volume 25 Issue 21 Pages 21149-21163  
  Keywords Anaerobic digestion; Protein degradation; Machine learning; Prediction methods; Support vector machines  
  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.  
  Address [Alejo, Luz; Guzman-Fierro, Victor; Roeckel, Marlene] Univ Concepcion, Dept Ingn Quim, Victor Lamas 1290,Casilla 160-C Correo 3., Concepcion, Chile, Email: john.atkinson@uai.cl;  
  Corporate Author Thesis  
  Publisher Springer Heidelberg Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0944-1344 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000438830900080 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 890  
Permanent link to this record
 

 
Author Jara-Munoz, P.; Guzman-Fierro, V.; Arriagada, C.; Campos, V.; Campos, J.L.; Gallardo-Rodriguez, J.J.; Fernandez, K.; Roeckel, M. doi  openurl
  Title Low oxygen start-up of partial nitrification-anammox process: mechanical or gas agitation? Type
  Year 2019 Publication Journal Of Chemical Technology And Biotechnology Abbreviated Journal J. Chem. Technol. Biotechnol.  
  Volume 94 Issue 2 Pages 475-483  
  Keywords mechanical agitation; partial nitrification anammox; dissolved oxygen; gas agitation; granule  
  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  
  Address [Jara-Munoz, Pamela; Guzman-Fierro, Victor; Arriagada, Constanza; Jose Gallardo-Rodriguez, Juan; Fernandez, Katherina; Roeckel, Marlene] Univ Concepcion, Dept Chem Engn, Fac Engn, Concepcion, Chile, Email: mroeckel@udec.cl  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0268-2575 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000455262100014 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 972  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: