The effect of some operating variables on the adsorption of lead and cadmium ions on kaolinite clay

Modification of kaolinite clay mineral with orthophosphate (p-modified sample) enhanced adsorption of Pb and Cd ions from aqueous solutions of the metal ions. Increasing pH of solutions of metal ions, increasing adsorbent dose and increasing concentration of metal ion, increased the adsorption of metal ions. Adsorption of both metal ions simultaneously on both unmodified and p-modified samples indicates that adsorption of one metal ion is suppressed to some degree by the other. The presence of electrolyte and their increasing concentration reduced the adsorption capacities of both unmodified and p-modified samples for the metal ions. Ca-electrolytes had more negative effect on the adsorption capacities of the adsorbents than Na-electrolytes. Ca-electrolytes reduced adsorption capacities of the adsorbents for Pb and Cd ions. From Langmuir plots it was observed that these electrolytes increased the binding energy constant of the metal ions unto the adsorbents especially on the p-modified samples. The rate of adsorption of Pb and Cd ions on p-modified adsorbent were increased and equilibrium of metal ion solution were more quickly reached (8min for Pb ions and 12min for Cd ions) with p-modified adsorbent as against 20min for adsorption of both metal ions on unmodified adsorbent when 200mg/L of metal ion solutions were used during the kinetic studies. When adsorption data were fitted against Langmuir, Freundlich, Toth and Langmuir-Freundlich isotherms, satisfactory fits were found with the Freundlich isotherm. However, at low concentration of metal ions, data also showed satisfactory fits to Langmuir isotherm.     

Macro and micro-nutrient effects on the yield and nutrient concentration of maize (Zea mays L.) in South Western Nigeria

Despite various fertilizer recommendations, maize (Zea mays L.) yields in tropical forest and savanna zones are very low. These low yields could be attributed in part to the exclusion of some secondary and all micro nutrients in fertilizer recommendations for maize, a condition which often leads to nutrient imbalance. The inclusion of some secondary and micro nutrients in fertilizer recommendation for successful maize cultivation in this zone has been suggested [2, 16] but no one has proposed a balanced nutrient program for this purpose. Field trials were therefore conducted to investigate the potential for improving maize yields by fertilizing soils of different parent materials with both macro and micro nutrients in three ecological zones of maize production in south western Nigeria.The study has indicated that in addition to NPK, Mg was necessary for high yield of maize on forest soils derived from sedimentary materials while on forest and savanna soils formed from basement complex rocks, inclusion of Mg, Fe, Cu and Zn to NPK was required. This was further confirmed with ear leaf tissue and soil analyses. High nutrient concentration in earleaf does not necessarily guarantee high yield of maize. It is therefore concluded that there is a need for soil testing program in this region.     

A new technique for monitoring alum sludge conditioning

A technique based on measurements of fluctuations in the intensity of light transmitted through a flowing sludge suspension was used to evaluate polymer performance in alum sludge conditioning. The ratio of the r.m.s. of the fluctuations to the average transmitted light intensity exhibits a well-defined response, usually a substantial increase, to sludge particle aggregation. This aggregation reflects the improvement in sludge filterability as also indicated by concurrent CST measurements. Five polymers, cationic, anionic and nonionic were used in conditioning the sludge. Tests were conducted using a mixer set at 700 rpm with a corresponding velocity gradient of 1900 s⁻¹. The technique was shown to give a very rapid convenient indication of the conditioning effect of polymeric flocculants. It could be very easily adapted to online applications.     

Cracks emanating from a circular hole under biaxial load

The principle of linear superposition has been employed in studying cracks emanating from a circular hole in a finite sheet under biaxial loading. The series type analytical solution around the crack tip has been combined with numerical analysis for the purpose of this investigation. The method presented here makes it possible to demonstrate both analytically and numerically, the effects of applied load biaxiality on the stress intensity factor. The ratio of the hole-radius-to-crack-length (R/c) is shown to determine the effect of the applied load biaxiality on the crack emanating from a circular hole in a finite sheet.     

Multi-objective scheduling of MapReduce jobs in big data processing

Multimedia Tools and Applications - Data generation has increased drastically over the past few years due to the rapid development of Internet-based technologies. This period has been called the...     

Estimation of thermal maturity status of petroleum asphaltenes and maltenes

Asphaltenes from various bituminous sand, heavy oil and conventional oil reservoirs and maltenes from various conventional oil reservoirs have been pyrolysed at 130 °C (for the asphaltenes), and at 50 °C (for the maltenes) in the presence of hydrated aluminium chloride crystals as catalyst for the decomposition. The rates of production of gases were appreciable and measurable. Asphaltenes from bituminous sands from the same geographical location showed a clear correlation between the rate of gas production and the depth of burial. The trend indicates that the thermal maturity of the oil increases with increase in the depth of burial. For conventional oils, a correlation was also observed between the rate of the gaseous product yields and the geological age. Maltenes from the conventional oils gave similar results: here the trend indicates that the thermal maturity of the oils increases with increase in geological age. From the derived correlation curve, the geological age of unknown crude oil was determined (525 × 10⁶ years: middle Ordovician age).     

Prediction of selected biodiesel fuel properties using artificial neural network

Biodiesel is an alternative fuel to replace fossil-based diesel fuel. It has fuel properties similar to diesel which are generally determined experimentally. The experimental determination of various properties of biodiesel is costly, time consuming and a tedious process. To solve these problems, artificial neural network (ANN) has been considered as a vital tool for estimating the fuel properties of biodiesel, especially from its fatty acid (FA) composition. In this study, four ANNs have been designed and trained to predict the cetane number (CN), flash point (FP), kinematic viscosity (KV) and density of biodiesel using ANN with logsig and purelin transfer functions in the hidden layer of all the networks. The five most prevalent FAs from 55 feedstocks found in the literature utilized as the input parameters for the model are palmitic, stearic, oleic, linoleic and linolenic acids except for density network with a sixth parameter (temperature). Other FAs that are present in the biodiesels have been considered based on the number of carbon atom chains and the level of saturation. From this study, the prediction accuracy and the average absolute deviation of the networks are CN (96.69%; 1.637%), KV (95.80%; 1.638%), FP (99.07%; 0.997%) and density (99.40%; 0.101%). These values are reasonably better compared to previous studies on empirical correlations and ANN predictions of these fuel properties found in literature. Hence, the present study demonstrates the ability of ANN model to predict fuel properties of biodiesel with high accuracy.     

Effects of Dissimilar Curing Systems on the Strength and Durability of Recycled PET-Modified Concrete

Silicon - Since curing remains an essential parameter that directly influences concrete strength and durability, this research aimed at evaluating the effects of water, sodium chloride (NaCl), and...     

Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

Knowledge propagation is a necessity, both in academics and in the industry. The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups. The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms. Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation. This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups. The system workflow consists of a chronology of stages, each made of distinct steps. Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs. This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others. The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants’ performances in an examination, rather than through any external process. As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants. The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases. Real life experimentation was carried out while teaching Object-Oriented Programming to forty (40) undergraduates between February and May 2021. Empirical result showed that the performance of the learners was improved appreciably. This work will therefore be of immense benefit to the industry, academics and research community involved in collaborative learning.