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  • Chemical Engineering  (2)
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  • 1
    Electronic Resource
    Electronic Resource
    Hoboken, NJ : Wiley-Blackwell
    AIChE Journal 42 (1996), S. 455-465 
    ISSN: 0001-1541
    Keywords: Chemistry ; Chemical Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Two issues involving the methodology used for on-line control of product quality in batch manufacturing processes are addressed: the generation of fast, data-driven process models and the use of such process models for on-line feedback control of product quality. The methodology is investigated using the example of the control of dispersity and molecular weight distribution in a batch reactor for emulsion polymerization of vinyl acetate. An artifical neural network (ANN) is used as a model to predict the quality as a function of the manipulated variables and on-line measurements. This model is constructed using an augmented dataset that integrates experimental information and knowledge from a mathematical model. The proposed model is compared with other types such as a theoretical model whose key parameters are fitted to experimental data. The hybrid ANN is superior to the parameter-fitting approach for this case. Experimental and simulation studies confirm the advantage of using the proposed model and the predictive control algorithm.
    Additional Material: 9 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Hoboken, NJ : Wiley-Blackwell
    AIChE Journal 33 (1987), S. 26-35 
    ISSN: 0001-1541
    Keywords: Chemistry ; Chemical Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: A two-phase approach to the control and operation of complex chemical processes at their optimum operating conditions is presented. The first phase consists of on-line parameter identification and state estimation of approximate nonlinear dynamic process models using on-line and off-line measurements. In the second phase, the optimum operating strategy is determined by integrating and optimizing this identified process model over a selected time horizon into the future. The method is particularly suited to those processes that exhibit slow dynamic responses and are subject to disturbances that have a significant economic impact. Examples include batch chemical reactors, large distillation towers, and processes with significant holdup times such as large fluidized-bed reactors.
    Additional Material: 13 Ill.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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