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...

Full description

Bibliographic Details
Main Authors: Kingsley Amechi Ani, Chinedu Matthew Agu, Matthew Chukwudi Menkiti
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