PROCESSING OF TRIGLYCERIDES TO DIESEL RANGE HYDROCARBON FUELS: EASILY PRACTICABLE SMALL SCALE APPROACH

Lipid fraction of biomass has been identified as carbon neutral substitution to fuels from fossil sources in the transportation sector. Although, the diesel engine, invented by Rudolph Diesel over a century ago first ran on peanut oil, the current combustion engines are designed to run on hydrocarbon fuels derived from petroleum. Therefore, a substitute for diesel fuel from renewable source will need to have identical or closely similar properties. The most popular of the existing technology for processing vegetable or animal oils is based on the conversion of the triglycerides constituents to fatty acids methyl esters (FAME). FAME technology does not produce diesel fuel with identical properties as petro-diesel. Other alternative processing routes are dilution of the vegetable oils, emulsification, pyrolysis and hydrotreating. These routes are discussed in this paper. Appropriate technologies for small scale production of diesel range hydrocarbon fuel from vegetable oil without the need for co-reactants such methanol or hydrogen as part of the feedstock is emphased. Also alternative catalyst systems in place of the expensive precious metal supported catalysts are suggested

ELECTRICITY CONSUMPTION PREDICTION SYSTEM USING A RADIAL BASIS FUNCTION NEURAL NETWORK

The observed poor quality of service being experienced in the power sector of Nigeria economy has been traced to non-availability of adequate model that can handle the inconsistencies associated with traditional statistical models for predicting consumers’ electricity need, so as to bridge the gap between the demand and supply of the energy. This research presents Electricity Consumption Prediction System (ECPS) based on the principle of radial basis function neural network to predict the country’s electricity consumption using the historical data sourced from Central Bank of Nigeria (CBN) annual statistical bulletin. The entire datasets used in the study were divided into train, validation and test sets in the ratio of 13:3:4. By the above, 65% of the entire data were used for the training, 15% for validation and 20% for testing. The train data was presented to the constructed models to approximate the function that maps the input patterns to some known target values. The models were also used to simulate both validation and the test datasets as case data on the consistency of results obtained from the training session through the train data. Experimental results showed that RBF network model performs better than equivalent Backpropagation (BP) network models that were compared with it and provides the best platform for developing a forecast system.

PREDICTING STUDENTS«¤?? ENROLLMENT USING GENERALIZED FEED-FORWARD NEURAL NETWORK

An important obligation of educational planning is the projection of students«¤?? enrollment which forms the basis for many of the investment decisions. Enrollment projection provides information for decision making and budget planning hence, it is important to the development of higher education. As many factors have impacts on the enrollment number, and for the above reasons, students«¤?? population and enrollment number should be considered as a chaotic system. In this research, a Generalized Feed-Forward Neural Network (GFFNN) for students«¤?? enrollment prediction was proposed. The architecture of the proposed model was in-line with eight steps involved in developing a neural network model for predicting a chaotic system. The data used was obtained from Academic Planning and Quality Control Unit of Tai Solarin University of Education, Ogun State Nigeria. The results from the study showed that the mean absolute percent error of GFFNN has an average of 0.0101% unlike linear regression and autoregression models that were compared with it, with an average of 0.0570% and 0.0725% respectively. The proposed methodology is expected to assist the school management to adequately plan for the future needs of the students in the provision of facilities.ª¤

PREDICTING STUDENTS«¤?? GRADE SCORES USING TRAINING FUNCTIONS OF ARTIFICIAL NEURAL NETWORK

The observed poor quality of graduates of some Nigerian Universities in recent times has been traced to non-availability of adequate mechanism. This mechanism is expected to assist the policy maker project into the future performance of students, in order to discover at the early stage, students who have no tendency of doing well in school. This study focuses on the use of artificial neural network (ANN) model for predicting students«¤?? academic performance in a University System, based on the previous datasets. The domain used in the study consists of sixty (60) students in the Department of Computer and Information Science, Tai Solarin University of Education in Ogun State, who have completed four academic sessions from the university. The codes were written and executed using MATLAB format. The students«¤?? CGPA from first year through their third year were used as the inputs to train the ANN models constructed using nntool and the Final Grades (CGPA) served as a target output. The output predicted by the networks is expressed in-line with the current grading system of the case study. CGPA values simulated by the network are compared with the actual final CGPA to determine the efficacy of each of the three feed-forward neural networks used. Test data evaluations showed that the ANN model is able to predict correctly, the final grade of students with 91.7% accuracy.ª¤

Total Phenolic and Flavonoid Contents, Antioxidant Activity and Phytochemical Screening of Calotropis Procera Stem Bark Extracts

Communication in Physical Sciences 2020, 5(3): 233 Authors: Abdullahi Usman, Ruth O. Onore, Osebuohien A. Oforghor, Jibrin Mohammed, and Nasiru L. Usman Received 19 May 2020/Accepted 29 May 2020 In continuation of the need to search for phytochemicals in parts of some rare and native plants of Nigeria origin. This study was designed to carry out phytochemical screening, antioxidant properties and determination of total phenolics and flavonoid contents in Calotropis procera Stem. The phytochemical screening of stem bark of C. procera using aqueous and methanol extracts revealed the presence of tannins, phenols and flavonoid. The aqueous extract was also found to contain saponins while methanol extract also has steroids. Steroids was the only metabolite present in hexane extract. The anti-oxidant activity, total phenolic and flavonoid contents of aqueous and methanolic extracts of stem bark of C. procera were evaluated by using 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay, Folin-Ciocalteau and aluminium chloride colorimetric assays. From the results obtained, the methanolic extract was observed to have demonstrated a significant concentration of phenolic (81.65±0.92 mg GAE/g), and flavonoid (46.08±0.71 mg RE/g) than the aqueous extract (66.07±0.43 mg GAE/g, 31.34±0.39 mg RE/g). The aqueous and methanol extracts showed maximum activities of 28.16±0.64% and 81.65±0.71% at 1 mg/ml respectively. However, the ascorbic acid exhibited 83.12±1.02% in the DPPH assay. The results of the present study, shows that both aqueous and methanolic extracts could serve as a valuable source of natural antioxidants

Bioactive metabolites in improved cowpea seeds

The International Institute of Tropical Agriculture (IITA) has developed some pest and disease resistant cowpeas. From these the seeds of 8 cowpea cultivars were extracted with ethanol, and partitioned into chloroform and water-soluble fractions, the water-soluble fraction was further extracted with ethyl acetate. Residues from ethanol, chloroform and ethyl acetate soluble fractions for each of the 8 cowpea cultivars were screened against brine shrimp larvae. The seed extracts of cowpea cultivars IT93K – 596 – 9 – 12, IT90K – 277 – 2 and IT93K – 452 – 1 were found to be most active, indicating that they contain cytotoxic compound(s).African Journal of Biotechnology Vol. 4 (6), pp. 513-516, 200

Bias-Variance Tradeoffs Analysis Using Uniform CR Bound For a SPECT System

The authors quantify fundamental bias-variance tradeoffs for the image reconstruction problem in radio-pharmaceutical tomography using Cramer-Rao (CR) bound analysis. The image reconstruction problem is very often biased and the classical or the unbiased CR bound on the mean square error performance of the estimator can not be used. The authors use a recently developed “uniform” CR bound which applies to biased estimators whose bias gradient satisfies a user specified length constraint. The authors demonstrate the use of the “uniform” CR bound for a simple SPECT system using several different examples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85970/1/Fessler126.pd

Physical characterisation of some honey samples from North-Central Nigeria

Some physicochemical properties (water content, sugar content, viscosity, pH and conductivity) were determined for honey samples from North-Central Nigeria to evaluate their global behaviour and comparison with other honey samples. The water content and sugar content varied within the range of (18.22 - 36.82%) and (63.82 - 80.25%) respectively. The pH increased with increase in water content and the conductivities of the samples had correlation with proportion of minor constituents in the honey samples. The relationship among water content (w), temperature (t) and viscosity (�) for different honey samples of may be represented as � = 17.678× 10 3 exp (-0.32w - 0.088t). The temperature dependence of viscosity was evaluated with Arrhenius model, the activation energy with value of 70.07 kJ/g is fairly unaffected by moisture content

A review on conversion of triglycerides to on-specification diesel fuels without additional inputs

Dependence on fossil fuels for global energy supply has continued to generate concerns about climate change and sustainable development. It has motivated the search for carbon-neutral alternative resources for the production of transportation fuels to replace crude oil. Although biodiesels have recently emerged as a close substitute to petrol diesel, their use in compression ignition engines designed to run on petro-diesel fuels are linked to adverse effects on the engines' performance and life span. This informed efforts at upgrading biodiesel or direct conversion of triglycerides to hydrocarbon mixtures that are identical or similar to that of petro-diesel through hydrodeoxygenation. Moreover, it seems that commercial methods for the conversion of triglycerides to diesel fuels depends on inputs (methanol and hydrogen) derived from fossil fuels. However, it will be desirable to do so without inputs from fossil fuels. Hence, reaction paths for direct conversion of triglycerides to on-specification hydrocarbons fuels without hydrogen gas input are discussed and suggested strategies are in cognisance of green chemistry principles

Compound-Specific Radiocarbon Analysis by Elemental Analyzer–Accelerator Mass Spectrometry: Precision and Limitations

[EN]We examine instrumental and methodological capabilities for microscale (10−50μg of C) radiocarbon analysisof individual compounds in the context of paleoclimate and paleoceanography applications, for which relatively high-precisionmeasurements are required. An extensive suite of data for14C-free and modern reference materials processed using differentmethods and acquired using an elemental-analyzer−accelerator-mass-spectrometry (EA-AMS) instrumental setup at ETHZurich was compiled to assess the reproducibility of specific isolation procedures. In order to determine the precision, accuracy,and reproducibility of measurements on processed compounds, we explore the results of both reference materials and threeclasses of compounds (fatty acids, alkenones, and amino acids) extracted from sediment samples. We utilize a MATLAB codedeveloped to systematically evaluate constant-contamination-model parameters, which in turn can be applied to measurementsof unknown process samples. This approach is computationally reliable and can be used for any blank assessment of small-sizeradiocarbon samples. Our results show that a conservative lower estimate of the sample sizes required to produce relativelyhigh-precision14C data (i.e., with acceptable errors of 0.5, a precision of 2% can be achieved for alkenone and fatty acid samples containing≥15 and 10μg of C, respectivel