NSChE e- Journal Articles
Below are some of the selected articles in the Journal of the NSChE. By clicking on the article title, you will be able to view the abstract of the article, but you require Adobe PDF reader and be a member of the Society to Download the articles
 
COMPUTER-AIDED DESIGN OF CIRCULAR CLARIFIERS 
Odediran, E.T, *Ayo, D.B. and Akinola, A.A. Chemical Engineering Department, University of Lagos, Akoka, Lagos, Nigeria
Article Abstract
An algorithm was developed for the design of primary circular clarifier for Wastewater treatment. Three computer programs were developed using Microsoft Excel, FoxPro and MATLAB respectively, all based on the algorithm. Computational Results from the three programs were not significantly different from those carried out manually. The algorithm was validated by feeding operational data (influent and effluent conditions) from the Primary clarification unit of the City of Springfield Wastewater Treatment Plant (WWTP), Ohio, USA into the three computer programs. The results of calculations from the computer programs using data from the Springfield WWTP were not significantly different from the design data of the WWTP.
MICROSOFT EXCEL AS A TOOL FOR DATA ANALYSIS: THE GOOD AND THE BAD 
Ayoade Kuye Department of Chemical Engineering University of Port Harcourt, Port Harcourt
Article Abstract
A Microsoft Excel spreadsheet is an array of rows and columns with automatic update and display of results. It also contains libraries of mathematical and statistical functions, graphing and charting facilities, add-ins such as Microsoft Excel’s Solver, as well as tools for writing custom code in languages such as Microsoft’s Visual Basic for Applications. In this work, our emphasis is to evaluate the use of Excel for data analysis. It is demonstrated that Excel is a good tool that can be used for data analysis and can easily be adapted to solve scientific and engineering problems. From the sample data presented in this paper, it is apparent that a lot of caution should be taken when using Excel. For serious data analysis it is better to develop one’s template using built-in functions and first principles. If possible, such a template should be validated with results from other statistical software.
SIMULATION OF BATCH DISTILLATION OF LEMON GRASS USING CHEMCAD 
Osagie E.1. and Ogbeide S.E. Department of Chemical Engineering, University of Benin, Benin City, Nigeria. ebuwa.osagie@uniben.edu, samuelogbeide@uniben.edu
Article Abstract
The simulation of batch distillation of lemongrass oil from lemongrass was investigated in this study. The principal components of lemongrass oil and their percent compositions, which are tetradecylic acid–12%, 2-chloro-1,1,1,2-tetrafluoroethane– 50%, citraconic acid–20%, D-Limonene–8% and 2,6 Dimethyl Octane–10%, were taken as the input in batch distillation column in CHEMCAD environment. The results showed that 2-chloro-1,1,1,2-tetrafluoroethane distilled after one hour, DLimonene was completely distilledin two hours, while 2,6 Dimethyl Octane took an hour. The third distillate was a mixture of Tetradecylic acid and Citraconic acid (citral). Tetradecylic acid took an hour and a portion of citral which is the last component followed next.
Process Modeling and Simulation Of Natural Gas Liquids Recovery From Associated Gas 
1*B.V.Ayodele, 2E.I.Osagie, 3E.T.Akhihiero and 4S.E.Ogbeide 1, 2,4 Department of Chemical Engineering, University of Benin, Benin City, Nigeria 3Department of Chemical Engineering, Delta State University, Oleh Campus.
Article Abstract
A process modeling and simulation technique for recovery of Natural Gas Liquids (NGLs) from associated gas was evaluated using ASPEN HYSYS 2006. Two different process routes were examined in order to ascertain which is the most efficient. The first process route consists of combinations of series of Separators for the recovery of the NGLs while the second process consists of series of combination of Distillation columns. In the first process route, 96.67% (39530 kg/h) of NGLs with purity of 76% was recovered, while 81% of NGLs with 95% purity was recovered in the second process route. This implies that process route II will be preferred though with low recovery values but high Natural Gas Liquids (NGLs) Purity.
ARTIFICIAL NEURAL NETWORKS MODELING APPROACH FOR PREDICTION OF ADSORPTION OF PAHS IN SLURRY REACTOR 
C. N. Owabor and *B.V.Ayodele Department of Chemical Engineering, University of Benin, Benin City, Nigeria.
Article Abstract
Artificial Neural Network (ANN) model for the prediction of adsorption of Poly Aromatic Hydrocarbons (PAHs) in a slurry reactor was developed based on the multi-layer perceptron (MLP) feed forward network that was trained using the Levenberg-Marquadt algorithm. Experimental data were employed to design the feed forward neural networks modeling process and a total of 14 data points were used for training (8) and testing (6), respectively. The parameter estimation resulted in the following values for root mean square error (RMSE) - 1.274, 2.046 and 6.418, regression coefficient (r2) - 0.989, 0.912 and 0.999, and mean absolute deviation - 1.073, 1.677 and 0.325 for naphthalene, anthracene and pyrene respectively. Predicted values of the adsorption kinetic data were obtained from the ANNs and compared with the experimental values.
MODELLING AND PROCESS PARAMETERS OPTIMIZATION OF LUCKY NUT SEED OIL EXTRACTION BY ARTIFICIAL NEURAL NETWORK 
Ajala S. O. and Betiku E*. Biochemical Engineering Laboratory, Department of Chemical Engineering, ObafemiAwolowo University, Ile-Ife 220005, Osun State, Nigeria
Article Abstract
Lucky nut (Thevetiaperuviana) seed oil extraction process was modelled and parameters involved were optimized. The effects of three independent factors (sample weight, extraction time and solvent type) on the seed oil yield and their interactions were evaluated. The DOptimal design of response surface methodology was used to generate 24 experimental runs, which were subsequently carried out. The model that best described the extraction process was achieved by multilayer normal feedforward incremental back propagation network with sigmoid function. The predicted optimal condition wassample weight of 20 g, extraction time of 3 h and solvent type, petroleum ether. The observedlucky nutseed oil yield was 77.63% (w/w), which compared well with the value of 77.42% (w/w) predicted by Artificial Neural Network (ANN). The physiochemical properties of the extracted lucky nut seed oil (% FFA of 7.05, acid value of 14.03±0.11 mg KOH/g oil, iodine value of 98.98 g I2/100g oil and peroxide value of 4.60 meq O2/kg oil)indicated that itwas non edible and the fatty acid profile also showed that the oil was highly unsaturated (76.13%) with oleic acid as the highest (43.81%). Thus, the seed oil could serve as a feedstock in oleiochemicals and biodiesel industries.

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