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Author: search.filters.author.Pimpunchat, BusayamasSubject: search.filters.subject.Algae growth

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    Using ANOVA to evaluate the effects of swine slaughterhouse wastewater conditions on algae growth
    (2018-01-01)
    Sornnery, Achara
    ;
    Pimpunchat, Busayamas
    ;
    Tuntiwarasakul, Daranporn
    ;
    Kitrungloadjanaporn, Pongpatai
    ;
    Amornsamankul, Somkid
    Wastewater is a major environmental problem. Swine slaughterhouses generate a large volume of wastewater with high an organic load and nutrients. It therefore has the potential to cause environmental problems. The effects of swine slaughterhouse wastewater conditions on algae growth are evaluated. Microalgae, Chlorella vulgaris TIST8580 in water mixtures Tris acetate phosphate medium (control) and in diluted sewage from a slaughter house with the ratio of 25:75 and 50:50 (sewage: water) were experimentally studied. We used One-way ANalysis Of Variance (One-way ANOVA) and Two-ways analysis of variance (Two-way ANOVA) techniques for testing the differences between the 3 cases of the culture conditions used. It was found that from the One-Way ANOVA, the average number of cells of algae in wastewater in the 25% and 50% groups is not significantly different but both of these groups are significantly different from that of the control group. In addition, from the Two-Way ANOVA, we found that unlike the control group, the same kind of data in both 25% and 50% groups had no significant difference. This implies that the characteristic growth of algae in these two group do not significantly change over the culture period. We believe that our finding could benefit the researchers to properly design experiments especially for the case of resource limitation.
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    Estimation of algae growth model parameters by a double layer genetic algorithm
    (2012-11-01)
    Nokkaew, Artorn
    ;
    Pimpunchat, Busayamas
    ;
    Modchang, Charin
    ;
    Amornsamankul, Somkid
    ;
    Triampo, Wannapong
    This paper presents a double layer genetic algorithm (DLGA) to improve performance of the information-constrained parameter estimations. When a simple genetic algorithm (SGA) fails, a DLGA is applied to the optimization problem in which the initial condition is missing. In this study, a DLGA is specifically designed. The two layers of the SGA serve different purposes. The two optimizations are applied separately but sequentially. The first layer determines the average value of a state variable as its derivative is zero. The knowledge from the first layer is utilized to guide search in the second layer. The second layer uses the obtained average to optimize model parameters. To construct a fitness function for the second layer, the relative derivative function of the average is combined into the fitness function of the ordinary least square problem as a value control. The result shows that the DLGA has better performance. When missing an initial condition, the DLGA provides more consistent numerical values for model parameters. Also, simulation produced by DLGA is more reasonable values than those produced by the SGA.