Remediation of Crude Oil-Contaminated Soil Using Vermicompost in the Niger Delta Area of Nigeria

Abstract

Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Adsorption of toxic metals has been achieved using Vermicompost, but there is dearth of knowledge in adsorption of crude oil using Vermicompost. This study brings to knowledge the effectiveness of earthworm waste (vermicompost) use for the remediation of crude oil contaminated soils. The remediation methods adopted were batch and column processes conditions. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the data from laboratory experiments. Machine learning models [Artificial neural network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS). ANN and ANFIS were evaluated on the observed and predicted percentage removal of crude-oil using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency ranged from 29% to 98.9% for batch process remediation and 56% to 92% for column process remediation. Optimum values of the experimental factors were absorbent dosage of 34.53 g, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, for batch and column processes. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation and 56% to 92% for column process remediation with the same optimum values of factors. Coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively. This result show strong correlation between the observed and predicted values for batch and column process, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between observed and predicted values. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. Machine learning models appear to be capable of predicting the removal of crude oil from polluted soil using vermicompost.