반응표면법을 이용한 아쿠아포닉스 전처리조 최적 운전 조건 평가 모델
Evaluation Model of Optimal Operating Conditions for Aquaponics Pretreatment Using Response Surface Methodology
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초록
The aim of this research was to apply a method designed to derive the factors influencing total ammonia removal when operating an additional pretreatment system at Aquaponics. The Box-Behnken method, among response surface analysis methods was used to characterize and determine the optimal nitrification conditions when operating the pretreatment system. Among the mathematically and statistically calculated prediction equations, the total ammonia nitrogen concentration Y1 measured on day 8 was derived as Y1= -195.8+2.23X1+42.9X2+47.5X3+0.18 56X1 2-1.380X1X2-1.770X1X3, and the time taken to reach the maximum total ammonia nitrogen concentration during the experiment period was derived as Y2=271-5.04X1+60.5X2-64.8X3+0.1654X1 2+6.54X3 2-0.600X1X3-9.00X2X3. The coefficients of determination of the regression models of Y1 and Y2 were 93.99% and 94.46%, respectively. The modified coefficients of determination were also high, at 89.48% and 88.91%, respectively. The prediction coefficients of determination of Y1 and Y2, were 70.68% and 62.11%, respectively, which was relatively lower than that of Y1, but still indicated a reliable prediction performance.
Abstract
The aim of this research was to apply a method designed to derive the factors influencing total ammonia removal when operating an additional pretreatment system at Aquaponics. The Box-Behnken method, among response surface analysis methods was used to characterize and determine the optimal nitrification conditions when operating the pretreatment system. Among the mathematically and statistically calculated prediction equations, the total ammonia nitrogen concentration Y1 measured on day 8 was derived as Y1= -195.8+2.23X1+42.9X2+47.5X3+0.18 56X1 2-1.380X1X2-1.770X1X3, and the time taken to reach the maximum total ammonia nitrogen concentration during the experiment period was derived as Y2=271-5.04X1+60.5X2-64.8X3+0.1654X1 2+6.54X3 2-0.600X1X3-9.00X2X3. The coefficients of determination of the regression models of Y1 and Y2 were 93.99% and 94.46%, respectively. The modified coefficients of determination were also high, at 89.48% and 88.91%, respectively. The prediction coefficients of determination of Y1 and Y2, were 70.68% and 62.11%, respectively, which was relatively lower than that of Y1, but still indicated a reliable prediction performance.
참고문헌 (12)
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