Browsing by Author "Patrick U. Okoye"

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Assessing absorption-based CO2 capture: Research progress and techno-economic assessment overview
(Elsevier, 2023-06-16) Usman Khan; Chukwuma C. Ogbaga; Okon-Akan Omolabake Abiodun; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Patrick U. Okoye; Jude A. Okolie
Rapid industrial developments and rising population are mounting concerns, leading to increased greenhouse gas (GHG) emissions and resultant climate change. Therefore, to curb such drastic trends, it is necessary to adopt and develop a sustainable environment. Among the most effective ways to lower GHG emissions is carbon capture. Absorption is one of the most mature methods of reducing CO2 due to its high processing capacity, excellent adaptability, and reliability. This study aims to evaluate the most recent advancements in various CO2 capture techniques, with an emphasis on absorption technology. The techno-economic analyses of absorption-based CO2 capture processes were meticulously discussed. These include studies on solvent screening as well as techno-economic analysis methods. Economic estimators such as the payback period, rate of return and net present value are discussed. The research progress in absorption-based capture compared to other separation methods, is elucidated. Advances in the applications of various absorption solvents including aqueous, phase change solvents and deep eutectic solvents are presented. Finally, key recommendations are provided to tackle the challenges for efficient utilization of the absorption technique.
Enhancing biomass Pyrolysis: Predictive insights from process simulation integrated with interpretable Machine learning models
(Elsevier, 2024-03-01) Douglas Chinenye Divine; Stell Hubert; Emmanuel I. Epelle; Alaba U. Ojo; Adekunle Akanni Adeleke; Chukwuma C. Ogbaga; Olugbenga Akande; Patrick U. Okoye; Adewale Giwa; Jude A. Okolie
Waste biomass pyrolysis is a promising thermochemical conversion process for the production of biofuels and sustainable materials. However, it is challenging to accurately predict the properties and yield of products formed during pyrolysis. Machine learning (ML) is a useful tool for predicting the performance of a process. In the present study, ML algorithms integrated with process simulation were explored to accurately model waste biomass pyrolysis based on properties such as H/C, O/ C, oil yield, gas yield, and char yield. Six different ML models including Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were used to model pyrolysis process. It was found that the out-of-the-box (without optimization) models for RF, XGBoost, ANN, and GBR performed the best and did not benefit from hyperparameter optimization. The GBR was identified as the most effective among various ML models. It accurately predicted yields of gas, biochar, bio-oil yields, and their H/C and O/C compositions. GBR effectively demonstrated the complex relationships between these variables. The box plot showing the root mean squared logarithmic error (RMSE) revealed that the GBR model had the best overall performance with a value less than 0.03. Also, the partial dependence plot and SHAP feature importance were evaluated to better understand each feature’s effect on the output. Lastly, a shareable graphical user interface (GUI) was created to enable researchers explore and predict pyrolysis yield
Machine Learning Model for the Evaluation of Biomethane Potential Based on the Biochemical Composition of Biomass
(BioEnergy Research, 2023-09-30) Adekunle Akanni Adeleke; Jude A. Okolie; Chukwuma C. Ogbaga; Peter Pelumi Ikubanni; Patrick U. Okoye; Olugbenga Akande
Biomethane potential (BMP) is often used to evaluate the biogas potential during anaerobic digestion (AD). However, BMP tests are complex and time-consuming. Therefore, the present study presents a hybrid model in machine learning (ML) for evaluating and predicting BMP based on biomass biochemical composition. Generative adversarial network (GAN) is combined with different ML models to model and predict BMP for different biomass materials. The models were trained on 64 experimental datasets (original datasets) and a combination of a GAN and the original datasets (augmented datasets). The gradient boost regression (GBR) model performed very well on the training set with both datasets compared to the support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). RF and GBR models performed very well when trained with the combined GAN and original datasets, with RF slightly outperforming GBR on the test datasets (R2 score of 0.9106 vs. 0.9177). This indicates that the models benefit from the additional data generated by the GAN. The GBR model trained with the GAN and original datasets combined outperformed the RF model on the test set, with an R2 score of 0.9177 vs. 0.9106. A comparison between three different hyperparameter optimization methods (grid search, particle swarm optimization, and Bayesian optimization) showed that the grid search optimized model offers a balanced performance with an R2 score of 0.9994 and a marginal improvement on the test set with an R2 of 0.9213. Feature analysis results demonstrate that cellulose has the most influence on BMP.
Pathways for the Valorization of Animal and Human Waste to Biofuels, Sustainable Materials and Value-Added Chemicals
(MDPI, 2023-03-06) Jude A. Okolie; Toheeb Jimoh; Olugbenga Akande; Patrick U. Okoye; Chukwuma C. Ogbaga; Adekunle Akanni Adeleke; Peter Pelumi Ikubanni; Fatih Güleç; Andrew Nosakhare Amenaghawon
Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods.