Civil Engineering Department, National Institute of Technology Patna, Ashok Rajpath, Mahendru, Patna, Bihar 800005, India.
CSIR-National Environmental Engineering Research Institute (NEERI), Nehru Marg, Nagpur 440 020, India.
Bioresour Technol. 2021 Mar;324:124672. doi: 10.1016/j.biortech.2021.124672. Epub 2021 Jan 7.
In this study, the mixture of floral waste and cattle dung in different proportions was utilised to convert into vermicompost using earthworm Eisenia fetida. In the design of the experiment, the optimum amount of floral waste (1325 g) and cattle dung (500 g) was obtained for vermicompost using central composite design (CCD) and compared with the output of artificial neural network (ANN). The optimum proportions of vermicompost showed pH of 7.10, electrical conductivity of 3.39 mS/cm, total organic carbon of 34.01%, C: N ratio of 13, phosphorous of 5.31 g/kg and potassium of 14.45 g/kg. This vermicompost was enriched with sufficient concentration of nutrients like potassium, sodium, phosphorous, and calcium, which are beneficial for the growth of the plants. The current study was based on comparing response surface methodology (RSM) and ANN for maturity parameters and the value of R in both the cases was near 1.
在这项研究中，利用花卉废物和牛粪的混合物以不同比例转化为蚯蚓赤子爱胜蚓的堆肥。在实验设计中，利用中心复合设计（CCD）获得了最佳花卉废物（1325 克）和牛粪（500 克）用量，并与人工神经网络（ANN）的输出进行了比较。最佳堆肥比例显示 pH 值为 7.10、电导率为 3.39 mS/cm、总有机碳为 34.01%、C:N 比为 13、磷为 5.31g/kg 和钾为 14.45g/kg。这种堆肥富含足够浓度的养分，如钾、钠、磷和钙，有利于植物的生长。本研究基于比较响应面法（RSM）和 ANN 对成熟度参数的影响，两种情况下的 R 值均接近 1。
