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PKP Metadata Items |
Metadata for this Document |
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| 1. |
Title |
Title of document |
Mixture design formulation for optimized composting with the perspective of using artificial intelligence optimization algorithms |
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| 2. |
Creator |
Author's name, affiliation, country |
K. ECHARRAFI, H. EL HARHOURI, M. BEN ABBOU, Z. RAIS, I. EL HASSANI, M. EL HAJI; Z. RAIS 3, I. EL HASSANI4, M. EL HAJI1 |
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Subject |
Discipline(s) |
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Subject |
Keyword(s) |
Mixture design Surface response The composting process optimization |
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| 4. |
Description |
Abstract |
The increasing population size generates an increase in the amount of waste in the landfill. Most of this waste is biodegradable and its removal with traditional methods has many effects on the environment and public health. Thus, the composting process as new biotechnology allows the transformation of waste into a new useful product can be used in this case to produce the compost which can be used as bio-fertilizer in agriculture. The achievement of compost is a complicated task because of the non-linear interaction between biological and physic-chemical parameters in all steps of the process. Moreover, the composting process poses many other difficulties lies in the fact that it takes a long time for the degradation, maturity, and stability of organic matter and it has also the difficulty to find the bestformulation of feedstock leading to a C/N(Carbon/Nitrogen) ratio around 12 which is considered as a sign of maturity of the compost. That’s why an accurate optimization of the composting process is necessary for predicting the process parameters such as pH, C/N, %M (Moisture), EC (Electrical Conductivity) ensuring a good quality of compost, and the efficiency of the process. In order to optimize our process, the mixture-designs have been used in this article. The performance of the predictive models of C/N ratio and %OM will be a function of the rate of the different substrate of feedstock and will be measured using the coefficient of determination (R2) which is 85,31% for C/N and 71.64% for %MO |
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Publisher |
Organizing agency, location |
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Contributor |
Sponsor(s) |
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Date |
(YYYY-MM-DD) |
07-06-2019
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Type |
Status & genre |
Peer-reviewed Article |
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| 8. |
Type |
Type |
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| 9. |
Format |
File format |
PDF |
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| 10. |
Identifier |
Uniform Resource Identifier |
https://revues.imist.ma/index.php/JASES/article/view/16546 |
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| 10. |
Identifier |
Digital Object Identifier (DOI) |
https://doi.org/10.48393/IMIST.PRSM/jases-v1i2.16546 |
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| 11. |
Source |
Title; vol., no. (year) |
Journal of Applied Science and Environmental Studies; Vol 1, No 2 (2018) |
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Language |
English=en |
en |
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| 13. |
Relation |
Supp. Files |
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| 14. |
Coverage |
Geo-spatial location, chronological period, research sample (gender, age, etc.) |
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| 15. |
Rights |
Copyright and permissions |
Copyright (c) 2019 Journal of Applied Science and Environmental Studies
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