ABSTRACT

This study involved the analysis, optimization and evaluation of a bioremediation setup for the treatment of crude-contaminated soil. This was achieved via the setup and analysis of multiple experimental runs, all the while inducing variance in the process factors such as the remediation time and amount of biostimulants. The microbial species utilized were Aspergillus Niger and Pseudomonas Aeruginosa. The contaminated samples were analyzed at the laboratory for the measurement of the appropriate responses i.e., residual hydrocarbon content, total microbial count, and pH (RHC, TMC, and pH) both at the beginning of and at intermittent intervals during the study (at the 21-, 35-, and 49-day benchmarks).

At the end of the study, the obtained results were discussed and analyzed, and optimization was carried out via RSM and ANN techniques. These were done using Design Expert v.11.0 and CPC-X NeuralPower v.2.5 respectively.

The analysis carried out showed that the samples tested closer to the end of the study exhibited a higher degradation percentage, while those tested closer to the beginning of the study showed lower levels. The samples also displayed a general upward trend on the pH scale i.e., they got more alkaline or less basic with time. The pH ranges measured were found to be consistent with literature supporting the optimal environmental pH levels for the microbial specimens, 4.5 to 9.5 (for P. Aeruginosa) and 4.0 to 6.5 (for A. Niger).

Optimization-wise, it was found that though RSM utilized less organic matter, it gave a lower optimal degradation percentage (90.3%) compared to ANN (95.72%).