BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Life Sciences
    • Life Sciences Publications
    • View Item
    •   Bradford Scholars
    • Life Sciences
    • Life Sciences Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Neural network based modelling and control of batch reactor.

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Publication date
    2006
    Author
    Mujtaba, Iqbal M.
    Aziz, Norashid
    Hussain, M.A.
    Keyword
    Batch reactor
    Dynamic optimization
    Control;
    Neural networks
    Inverse modelling
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better.
    URI
    http://hdl.handle.net/10454/3653
    Version
    No full-text available in the repository
    Citation
    Mujtaba, I.M., Aziz, N. and Hussain, M.A. (2006). Neural network based modelling and control of batch reactor. Chemical Engineering Research and Design. Vol. 84, No. 8, pp. 635-644.
    Link to publisher’s version
    http://dx.doi.org/10.1205/cherd.05096
    Type
    Article
    Collections
    Life Sciences Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.