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    Reduction of Sound Pressure Levels with Noise Barriers Containing Agricultural Residues: Case Study
    (2025-01-01)
    Tinnam, Pasit
    ;
    Yodpijit, Nantakrit
    ;
    Junsupasen, Suparoek
    ;
    Sappakittipakorn, Manote
    ;
    Jongprasithporn, Manutchanok
    Over the past decade, PM 2.5 pollution has become an increasingly serious environmental concern in Thailand, with one of its primary sources being the open burning of agricultural residues, particularly sugarcane residues. To mitigate this issue, it is essential to develop alternative applications for sugarcane leaves that eliminate the need for burning. This study investigates a sustainable approach by incorporating sugarcane leaves into producing noise insulation mortar boards. The research aims to assess these boards' acoustic performance through a combination of laboratory experiments and field testing. Employing the Design of Experiments (DOE) methodology, mortar samples were prepared with sugarcane leaf content at 2%, 4%, 6%, 8%, and 10% by cement weight to identify the optimal mixture. Analysis of variance (ANOVA) indicated that the sugarcane leaf content had a statistically significant effect on sound pressure level reduction at a 95% confidence level. Both laboratory and field results demonstrated that the 4% sugarcane leaf mixture yielded the highest performance, achieving noise reductions of 20.6 dBA and 23 dBA, respectively. These findings confirm that mortar boards reinforced with sugarcane leaves effectively reduce noise and offer a viable solution for repurposing agricultural residues. This approach contributes to noise pollution mitigation and addresses environmental concerns related to the open burning of biomass.
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    Solving the incomplete data problem in Greco-Latin square experimental design by exact-scheme analysis of variance without data imputation
    (2024-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Wongsriya, Sirilak
    ;
    Leerojanaprapa, Kanogkan
    This study introduced a novel exact-scheme analysis of variance to tackle the challenge of incomplete data within the Greco-Latin square experimental design (GLSED), specifically for scenarios with a single missing observation across any treatment and block level, thus eliminating the need for conventional data imputation methods. This approach innovatively addresses and mitigates the bias in the treatment sum of squares, a significant drawback of traditional missing plot techniques, by providing a precise, exact-scheme-based formula for calculating the treatment sum of squares in fixed-effect GLSED contexts with unrecorded values. Moreover, it offers a method for correcting biased treatment sum of squares values, presenting an adjustment mechanism for instances where the least squares method was previously employed to estimate missing values. This comprehensive strategy not only enhances the methodological accuracy and integrity of GLSED studies but also contributes significantly to the field by offering a solution to navigate the complexities of incomplete datasets without resorting to data imputation, thus improving the rigor and validity of experimental designs in the face of missing data challenges.
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    Estimated parameters of 6 x 6 Latin square design consisting of two missing values
    (2019-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Thachongthumla, Kanokwan
    A design of experiment is a computational technique which is used to select the significant independent variables. The well-designed experiments should reduce the experiment error. We referred to the blocking technique to prevent the error from other variables. This paper focused on the incomplete latin square design (ILSD) based on the blocking technique. The missing values from experiments caused the unbalance design in which the instant formulae were not provided for the analysis of the variance (ANOVA). By means of the exact approach, this paper considered the incomplete latin square design of order 6 x 6 with the two missing values to develop the mathematical formulae for the estimated parameters for full effect model. Finally, it is easy to make the ANOVA. It is noted that the mathematical formulae in this paper can be used to solve all indexes.
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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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    One missing value problem in Latin square design of any order: Exact analysis of variance
    (2017-01-01)
    Sirikasemsuk, Kittiwat
    ;
    Leerojanaprapa, Kanogkan
    This research proposes a simplified exact approach based on the general linear model for solving the K × K Latin square design (LSD) with one replicate and one missing value, given the lack of ready-made mathematical formulas for the sub-variance. Under the proposed scheme, the effects of the potential variable were determined by means of the regression sums of squares under the full and reduced treatment models. The mathematical expressions could be applied to the LSD with one missing value of any order. Moreover, the treatment, row and column sums of squares are unbiased.