osa keavey | Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria (original) (raw)

Papers by osa keavey

Journal of Materials Science Research and Reviews, 2023

A cooling tower is a type of heat exchanger that uses air contact and water evaporation to chill ... more A cooling tower is a type of heat exchanger that uses air contact and water evaporation to chill water. The Port Harcourt refinery will be used as a case study in this paper's computational fluid dynamics CFD analysis of air flow in mechanical draft wet cooling towers. To achieve this, the CFD analysis of air under low Re-K-e flow conditions was done in Comsol Multi-Physics interface, The tower's schematic was created on AutoCAD. The results of the air's CFD analysis showed that the distribution profiles of velocity, temperature, relative humidity, and pressure inside the cooling tower are consistent with the air's postulated and calculated characteristics. AutoCAD was used to draw the schematic of the tower. The CFD analysis of air revealed that the velocity, temperature, relative humidity and pressure distribution profiles within the cooling tower are in accord with postulated and calculated characteristic of air within the Cooling Tower.

Cleaner Engineering and Technology, 2022

Discover Chemical Engineering, 2021

Numerical modeling of biomass pyrolysis is becoming a cost and time-saving alternative for experi... more Numerical modeling of biomass pyrolysis is becoming a cost and time-saving alternative for experimental investigations, also to predict the yield of the by-products of the entire process. In the present study, a two-step parallel kinetic model was used to predict char yield under isothermal condition. MATLAB ODE45 function codes were employed to solve a set of differential equations that predicts the %char at varying residence times and temperatures. The code shows how the various kinetic parameters and mass of pyrolysis products were determined. Nevertheless, the algorithm used for the prediction was validated with experimental data and results from past works. At 673.15 K, the numerical simulation using ODE45 function gives a char yield of 27.84%. From 573.15 K to 673.15 K, char yield ranges from 31.7 to 33.72% to 27.84% while experimental yield decreases from 44 to 22%. Hence, the error between algorithm prediction and experimental data from literature is − 0.26 and 0.22. Again, ...

UMUDIKE JOURNAL OF ENGINEERING AND TECHNOLOGY, 2020

This study evaluated the exergy analysis of the mechanical forced draft counter flow of the Port ... more This study evaluated the exergy analysis of the mechanical forced draft counter flow of the Port Harcourt Refinery cooling towers. The exergy analysis was carried out using the exergy destruction method. The results of the investigation and optimization show that the exergy destruction increases with the increases in mass flow rates of the cooling water, makeup water, and Air. Also, exergy destruction decreases with the decreases in Humidity Ratio, Heat sink Temperature, and Relative Humidity of Air. The optimal values of exergy efficiency of (0.7899[-]) and exergy destruction of (42135[W]]) obtained gave the expected minimum values of mass flows rates of Air (2185kg/s), Makeup water (79.33 (kg/s) and cooling water (4781 (kg/s).

Sciencedirect(Elsevier), 2021

The present study evaluates the extraction process parameters of grape peel oil using non-linear ... more The present study evaluates the extraction process parameters of grape peel oil using non-linear hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. The experiments were conducted at temperature (80-100 • C), and time (5-9 h) at 5 levels with output parameter as oil yield. The experimental result and factor interaction analysis shows that temperature had the most significant effect on the oil yield. The oil yield estimation performance indicators for the Fuzzy Inference (FIS) generation techniques are: Fuzzy C-means Clustering (FCM), [R 2 = 0.9102, MSE = 0.278, RMSE = 0.527], Subtractive Clustering (SC), [R 2 = 0.9090, MSE = 0.300, RMSE = 0.548] and Grid Partitioning (GP), [R 2 = 0.827, MSE = 0.668, RMSE = 0.817]. This indicates that Fuzzy C-means FIS generation fits better than Subtractive Clustering and Grid Partitioning techniques. For future research, a substantial volume of data is recommended for successful ANFIS training.