Optimization of fermentation condition to produce liquid organic fertilizer (LOF) from rotten vegetable waste using response surface methodology
This study aims to optimize fermentation process of vegetable waste into liquid organic fertilizer (LOF). Materials used in this study are rotten vegetables obtained from the local market and bio-activator from the biogas pilot plant. Level of variables for LOF fermentation such as fermentation time...
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Veröffentlicht in: | Cleaner Engineering and Technology 2023-10, Vol.16, p.100679, Article 100679 |
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Sprache: | eng |
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Zusammenfassung: | This study aims to optimize fermentation process of vegetable waste into liquid organic fertilizer (LOF). Materials used in this study are rotten vegetables obtained from the local market and bio-activator from the biogas pilot plant. Level of variables for LOF fermentation such as fermentation time and activator volume were adjusted by central composite design (CCD) and response surface methodology (RSM) was used to optimize process variables using JMP Pro ver. 13 software. This study observed except for yield of phosphorus (P) which is significantly correlated with fermentation time and bio-activator volume, other parameters such as pH, nitrogen (N), potassium (K), and carbon (C) are associated with time significantly but less significant with bio-activator volume. RSM predicted the best fermentation time to produce LOF was 11.17 days using bio-activator volume of 5.15 mL and pH level should be around 6 to 7.5. The LOF product under those optimum condition was predicted containing N, P K, and C approximately 0.15%, 0.042%, 0.27% and 0.58% respectively. Experimental verification had been carried out and t-test was used to compare the means of the observed and predicted data in this study. The experimental values were found significantly in agreement with the predicted N and P, but less significant for K with respect to t-Table. The t-test value indicates the predictive models were accepted. © 2001 Elsevier Science. All rights reserved. |
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ISSN: | 2666-7908 2666-7908 |
DOI: | 10.1016/j.clet.2023.100679 |