@InProceedings{BriguenteSantAmbr:2007:NeNeMo,
               author = "Briguente, Flavio Perpetuo and Santos, Marcus Venicius and 
                         Ambrozin, Andreia V. Pepe",
          affiliation = "{Monsanto do Brasil Ltda} and {Monsanto do Brasil Ltda} and 
                         {Monsanto do Brasil Ltda}",
                title = "Neural Network and Model-Predictive Control for Continuous 
                         Neutralization Reactor Operation",
            booktitle = "Proceedings...",
                 year = "2007",
               editor = "Loureiro, Geilson and Curran, Ricky",
                pages = "299--308",
         organization = "ISPE International Conference on Concurrent Engineering, 14. (CE 
                         2007).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Model-predictive control, Neural networks, Virtual on-line 
                         analyzers, Moisture, Process variability.",
             abstract = "This paper outlines neural network non-linear models to predict 
                         moisture in real time as a virtual on line analyzer (VOA). The 
                         objective is to reduce the moisture variability in a continuous 
                         neutralization reactor by implementing a model-predictive control 
                         (MPC) to manipulate the water addition. The acid-base reaction 
                         takes place in right balance of raw materials. The moisture 
                         control is essential to the reaction yield and avoids downstream 
                         process constraints. The first modeling step was to define 
                         variables that have statistical correlation and high effect on the 
                         predictable one (moisture). Then, it was selected enough 
                         historical data that represents the plant operation in long term. 
                         Outliers like plant shutdowns, downtimes or non-usual events were 
                         removed from the database. The VOA model was built by training the 
                         digital control system neural block using those historical data. 
                         The MPC was implemented considering constraints and disturbances 
                         variables to establish the process control strategy. Constraints 
                         were configured to avoid damages in equipments. Disturbances were 
                         defined to cause feed forward action. The MPC receives the 
                         predictable moisture from VOA and anticipates the water addition 
                         control. This process is monitored via computer graphic displays. 
                         The project achieved a significant reduction in moisture 
                         variability and eliminated off-grade products.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos",
      conference-year = "2007, July 16-20",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/ce@80/2007/03.05.22.30",
                  url = "http://urlib.net/ibi/dpi.inpe.br/ce@80/2007/03.05.22.30",
           targetfile = "paper.PDF",
                 type = "Collaborative concurrent engineering methodologies, methods and 
                         tools",
        urlaccessdate = "21 maio 2024"
}