Argitalpenak (13) ISAAC MONROY CHORA argitalpenak

2020

  1. Production of polyhydroxybutyrate by pure and mixed cultures of purple non-sulfur bacteria: A review

    Journal of Biotechnology, Vol. 317, pp. 39-47

2018

  1. Biohydrogen production by batch indoor and outdoor photo-fermentation with an immobilized consortium: A process model with Neural Networks

    Biochemical Engineering Journal, Vol. 135, pp. 1-10

  2. Diagnosis of undesired scenarios in hydrogen production by photo-fermentation

    Water Science and Technology, Vol. 78, Núm. 8, pp. 1652-1657

  3. Temporary feeding shocks increase the productivity in a continuous biohydrogen-producing reactor

    Clean Technologies and Environmental Policy, Vol. 20, Núm. 7, pp. 1581-1588

2016

  1. A mechanistic model supported by data-based classification models for batch hydrogen production with an immobilized photo-bacteria consortium

    International Journal of Hydrogen Energy, Vol. 41, Núm. 48, pp. 22802-22811

2012

  1. A novelty detection approach for detecting faulty batches in a photo-Fenton process

    Computer Aided Chemical Engineering (Elsevier B.V.), pp. 972-976

  2. A promising OPC-based computer system applied to fault diagnosis

    Computer Aided Chemical Engineering (Elsevier B.V.), pp. 892-896

  3. Enhanced plant fault diagnosis based on the characterization of transient stages

    Computers and Chemical Engineering, Vol. 37, pp. 200-213

  4. Fault diagnosis of a benchmark fermentation process: A comparative study of feature extraction and classification techniques

    Bioprocess and Biosystems Engineering, Vol. 35, Núm. 5, pp. 689-704

2011

  1. Dynamic process monitoring and fault detection in a batch fermentation process. comparative performance assessment between MPCA and BDPCA

    , pp. 1371-1375

2010

  1. A semi-supervised approach to fault diagnosis for chemical processes

    Computers and Chemical Engineering, Vol. 34, Núm. 5, pp. 631-642

2009

  1. Anomaly detection in batch chemical processes

    Computer Aided Chemical Engineering, Vol. 26, pp. 255-260

  2. DICA enhanced SVM classification approach to fault diagnosis for chemical processes

    Computer Aided Chemical Engineering, Vol. 26, pp. 267-272