فهرست مطالب

  • Volume:2 Issue:5, 2013
  • تاریخ انتشار: 1392/05/23
  • تعداد عناوین: 3
|
  • Harendra Kumar Chauhan, Keshav Singh Page 1
    Background
    Abundant uses of chemical fertilizers have adversely affected the soil. The large production of livestock dung is recorded in India annually. The presence of abundant agrowastes and animal dung causes serious problems to animals as well as to human beings, due to the improper management of these wastes. Due to the presence of different physicochemical parameters, these agrowastes and animal dung as food source influence not only the earthworm population but also affect their growth and reproduction during vermicomposting. The effect of agrowastes (wheat straw, banana pills) and bran (barley, rice, and gram bran) with cow and goat dung as tertiary combinations (1:1:1) on the growth and reproduction of E. fetida was investigated.
    Results
    The significant (P < 0.05) highest cocoon production was 5.92 ± 0.01/worm/2 weeks observed in CWRr. The reproduction rate as the number of hatchling emerged per cocoon was also significantly the highest (P < 0.05) in CWBr as 1.9 ± 0.03. The maximum biomass gained was up to 898.67 ± 2.04 mg/worm, and significant growth rate was 7.32 ± 0.02 mg/worm/day in CWGr combination. There was a significant decrease in pH, C/N ratio, TOC, and EC while there was a significant increase in TKN, TK, TAP, and TCa in different tertiary combinations of final vermicompost when compared to the initial feed mixture.
    Conclusion
    The tertiary combinations of dung and bran with agrowastes used were effective and efficient culture media for the large-scale production of E. fetida, which will be important for the production of vermicompost.
    Keywords: Eisenia fetida, Growth rate, Tertiary combinations, Vermicomposting, Organic wastes
  • Parveen Fatemeh Rupani, Mahamad Hakimi Ibrahim, Sultan Ahmed Ismail Page 8
    Background
    Palm oil mill effluent and palm press fiber are problematic wastes generated by the palm oil mill industries in Malaysia. This study has endeavored to assess the possibility of the vermicomposting of residue from the palm oil mills using epigeic earthworms Lumbricus rubellus under laboratory conditions. The study was conducted over 50 days using four combinations in three replicates of each treatment as palm oil mill effluent: palm press fiber in 50:50 ratio (T1), palm oil mill effluent/palm press fiber/cow dung in 50:25:25 ratio (T2), palm oil mill effluent/palm press fiber/cow dung/lawn clipping in 50:20:15:15 ratio (T3), and only palm press fiber (T4). Twenty healthy adult L. rubellus with average weight of 3.92 g was introduced.
    Results
    Results showed that T3 has a significant decrease in C/N ratio (14.81 ± 0.07) compared to the other treatments. The presence of cow dung and lawn clipping in the mixtures makes it more suitable for vermicomposting process as early compost productions were recorded in T2 and T3.
    Conclusion
    The study showed that the major polluting problem in palm oil mills can be tackled through vermicomposting technique. Based on the results, vermicompost is found suitable for agriculture purposes as an organic fertilizer as well as soil conditioner.
    Keywords: Vermicomposting, Palm oil mill waste, Lumbricus rubellus, Bioconversion
  • Abedin Zafari, Mohammad Hossein Kianmehr, Rahman Abdolahzadeh Page 15
    Background
    The relationships between the density of the biomass pellet and the related variables are very complicated and highly nonlinear, which make developing a single, general, and accurate mathematical model almost impossible. One of the most appropriate methods to solve these problems is the intelligent method. Shankar and Bandyopadhyay and Shankar et al. successfully used genetic algorithms and artificial neural networks to understand and optimize an extrusion process.
    Results
    The results showed that a four-layer perceptron network with training algorithm of back propagation, hyperbolic tangential activation function, and Delta training rule with ten neurons in the first hidden layer and four neurons in the second hidden layer had the best performance for the prediction of pellet density. The minimum root mean square error and coefficient of determination for the multilayer perceptron network were 0.01732 and 0.972, respectively. Also, the results of statistical analysis indicate that moisture content, speed of piston, and particle size significantly affected (P < 0.01) the density of pellets while the influence of die length was negligible (P > 0.05).
    Conclusions
    The results indicate that a properly trained neural network can be used to predict effect of input variable on pellet density. The ANN model was found to have higher predictive capability than the statistical model.
    Keywords: Extrusion parameters, Biomass pellet, Density, Artificial neural network