An artificial ecological system-based optimization to forecast the maximal TPH removal from Nigeria's Niger Delta crude oil-contaminated soil treated with a mix of biochar
In the present research, an optimization approach based on artificial ecological systems was used to predict the maximum TPH removal from Nigeria's Niger-Delta crude oil polluted soil remediated with biochar mix. The optimization approach involved the integration of artificial intelligence algo...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925001516 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In the present research, an optimization approach based on artificial ecological systems was used to predict the maximum TPH removal from Nigeria's Niger-Delta crude oil polluted soil remediated with biochar mix. The optimization approach involved the integration of artificial intelligence algorithms and ecological principles to simulate and optimize TPH removal process. The maximum TPH removal of 87.76 % was predicted by AEO with a very high-speed convergence time of 0.02s at 20 iterations. On performance evaluation AEO's optimization efficiency surpasses that of other cutting-edge methods such as PSO, SSBO and ASO. In terms of both convergence rate and computational effort, AEO outperforms other documented approaches, particularly for actual events engineering problems. The evaluation was performed at upper boundaries of (15g, 12g, 3g) and (20g, 12g, 3g). It is clear that AEO computed faster than the others in both cases of distinct upper boundaries. |
|---|---|
| ISSN: | 2772-9419 |