Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation
The extraction processes of palm oil from palm fruit bunch for industrial and domestic applications generate the palm oil mill effluent (POME) and it is considered a huge environmental challenge. The present study investigates the application of the POME as a suitable bio-stimulating organic nutrien...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-12-01
|
Series: | Environmental Challenges |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2667010021001955 |
id |
doaj-639a8b61750d406998dbe540faf735db |
---|---|
record_format |
Article |
spelling |
doaj-639a8b61750d406998dbe540faf735db2021-07-31T04:41:19ZengElsevierEnvironmental Challenges2667-01002021-12-015100216Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradationKingsley Amechi Ani0Chinedu Matthew Agu1Matthew Chukwudi Menkiti2Department of Chemical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria; Corresponding author: Mr. Kingsley Amechi Ani, Nnamdi Azikiwe University, NigeriaDepartment of Chemical Engineering, College of Engineering Michael Okpara University of Agriculture, Umudike, Abia State, NigeriaDepartment of Chemical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra State, NigeriaThe extraction processes of palm oil from palm fruit bunch for industrial and domestic applications generate the palm oil mill effluent (POME) and it is considered a huge environmental challenge. The present study investigates the application of the POME as a suitable bio-stimulating organic nutrient in hydrocarbon contaminated soil (HCCS). The wet POME (WPOME) and dried POME (DPOME) were investigated in this study. The physiochemical and microbial characteristics in the HCCS showed the inadequacies of the required organic nutrients in the HCCS while its microbial population indicated well-acclimatized microorganisms. The DPOME was able to stimulate the microbial population and caused a reduction from the initial HC concentration of 4,248mg/kg to 1,600.35mg/kg while the WPOME reduced the initial HC concentration from 4,248mg/kg to 2,600.56mg/kg. The organic nutrient levels and pH in POME need to be adjusted to be within the range for optimum HC degradation and microbial activity. Data from the first-order kinetics interpretation confirmed that the DPOME was more beneficial to the HCCS as an organic nutrient. Performance evaluation of the artificial neural network (ANN) model through the root mean square error (RMSE), mean square error (MSE), and correlation coefficient (R²) confirmed that the DPOME with lower RMSE, MSE, and a higher R² performed better in the HC degradation process. Finally, this study showed that the nutrient present in POME could potentially stimulate microbial growth and serve as an effective organic nutrient in HCCS degradation.http://www.sciencedirect.com/science/article/pii/S2667010021001955palm oil mill effluentfirst-order kineticshydrocarbon contaminationbio-stimulationANN model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kingsley Amechi Ani Chinedu Matthew Agu Matthew Chukwudi Menkiti |
spellingShingle |
Kingsley Amechi Ani Chinedu Matthew Agu Matthew Chukwudi Menkiti Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation Environmental Challenges palm oil mill effluent first-order kinetics hydrocarbon contamination bio-stimulation ANN model |
author_facet |
Kingsley Amechi Ani Chinedu Matthew Agu Matthew Chukwudi Menkiti |
author_sort |
Kingsley Amechi Ani |
title |
Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
title_short |
Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
title_full |
Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
title_fullStr |
Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
title_full_unstemmed |
Preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
title_sort |
preliminary investigation and neural network modeling of palm oil mill effluent as a potential bio-stimulating organic co-substrate in hydrocarbon degradation |
publisher |
Elsevier |
series |
Environmental Challenges |
issn |
2667-0100 |
publishDate |
2021-12-01 |
description |
The extraction processes of palm oil from palm fruit bunch for industrial and domestic applications generate the palm oil mill effluent (POME) and it is considered a huge environmental challenge. The present study investigates the application of the POME as a suitable bio-stimulating organic nutrient in hydrocarbon contaminated soil (HCCS). The wet POME (WPOME) and dried POME (DPOME) were investigated in this study. The physiochemical and microbial characteristics in the HCCS showed the inadequacies of the required organic nutrients in the HCCS while its microbial population indicated well-acclimatized microorganisms. The DPOME was able to stimulate the microbial population and caused a reduction from the initial HC concentration of 4,248mg/kg to 1,600.35mg/kg while the WPOME reduced the initial HC concentration from 4,248mg/kg to 2,600.56mg/kg. The organic nutrient levels and pH in POME need to be adjusted to be within the range for optimum HC degradation and microbial activity. Data from the first-order kinetics interpretation confirmed that the DPOME was more beneficial to the HCCS as an organic nutrient. Performance evaluation of the artificial neural network (ANN) model through the root mean square error (RMSE), mean square error (MSE), and correlation coefficient (R²) confirmed that the DPOME with lower RMSE, MSE, and a higher R² performed better in the HC degradation process. Finally, this study showed that the nutrient present in POME could potentially stimulate microbial growth and serve as an effective organic nutrient in HCCS degradation. |
topic |
palm oil mill effluent first-order kinetics hydrocarbon contamination bio-stimulation ANN model |
url |
http://www.sciencedirect.com/science/article/pii/S2667010021001955 |
work_keys_str_mv |
AT kingsleyamechiani preliminaryinvestigationandneuralnetworkmodelingofpalmoilmilleffluentasapotentialbiostimulatingorganiccosubstrateinhydrocarbondegradation AT chinedumatthewagu preliminaryinvestigationandneuralnetworkmodelingofpalmoilmilleffluentasapotentialbiostimulatingorganiccosubstrateinhydrocarbondegradation AT matthewchukwudimenkiti preliminaryinvestigationandneuralnetworkmodelingofpalmoilmilleffluentasapotentialbiostimulatingorganiccosubstrateinhydrocarbondegradation |
_version_ |
1721246932361281536 |