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Determination of the Settling Rate of Clay/Cyanobacterial Floccules
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Nonlinear programming technique for analyzing flocculent settling data.

Md Mamunur Rashid, Donald F Hayes

    Water Environment Research : a Research Publication of the Water Environment Federation
    |May 24, 2014
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    Summary
    This summary is machine-generated.

    Nonlinear programming (NLP) offers a reliable method for analyzing flocculent settling data, outperforming traditional graphical and other empirical approaches in accuracy and efficiency for sedimentation basin design.

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    Area of Science:

    • Environmental Engineering
    • Water Treatment
    • Chemical Engineering

    Background:

    • Traditional graphical methods for analyzing flocculent settling data are difficult to implement in computer-based designs.
    • Empirical approaches have been developed to overcome these limitations in sedimentation basin analysis.

    Purpose of the Study:

    • To compare the effectiveness of five empirical approaches for fitting flocculent settling data.
    • To evaluate rule-based SETTLE and nonlinear programming (NLP) as alternatives to existing modeling methods.
    • To determine the reliability of NLP optimization techniques for analyzing settling data.

    Main Methods:

    • Mathematical curve fitting was performed on published flocculent settling data.
    • Modeled concentration data were graphically compared against observed data.
    • Design results, including solid removal efficiency and detention times, were analyzed.
    • Sum of squared errors (SSE) values were calculated for each approach.

    Main Results:

    • A strong correlation was observed between NLP-modeled and actual concentration data.
    • SETTLE and NLP approaches showed conservative results at lower retention times and less conservative results at longer retention times.
    • NLP ranked as the best model for 8 out of 15 datasets and second best for 6 datasets based on SSE.

    Conclusions:

    • Nonlinear programming (NLP) is recommended for analyzing flocculent settling data due to its accuracy and reliability.
    • NLP serves as a logical extension of existing modeling approaches for sedimentation basin design.
    • The optimization capabilities and use of conventional algorithms in NLP make it suitable for widely available software.