Glucosesyrup obtainable from enzymatic hydrolysis of cassava and corn is widely used as sweetener in food and pharmaceutical industries. This study evaluates the potential of starch from Tacca tuber in comparison with starch from cassava tuber to produce glucose syrup using amylase from fungal isolates. Spores of amylase-producing cultures of Aspergillius niger and Rhizopus oligospora are grown on rice bran medium in solid state fermentation. Starch is obtained from Tacca (Tacca leontopetaloides) and cassava tubers using simple methods of extraction methods. Response Surface Method of the Central Composite Design version 6.0.8 is employed for optimization studies on the effect of pH, temperature, and time of hydrolysis on amylase activities of crude enzyme on the extracted starches. In comparison with cassava starch, physicochemical analysis of the glucose syrup obtained from cassava and Tacca tubers gives brix of 44 °(cloudy appearance) and 40 °(colourless in appearance) with a dextrose equivalent of 68.58 and 40.4 and specific gravity 1.200 and 1.169, respectively. An assessment of the microbiological quality of the glucose syrup produced shows no coliform or fungal growth. This study presents the potential of Tacca tuber as an economic and safe alternative to cassava starch for glucose syrup production.

Energy transition emphasizes a drastic reduction in fossil fuel energy sources and to step up the deployment of alternative and renewable energies as pathways to industrialization and development. Attention is therefore shifting from coal and crude oil to solar, wind, geothermal and natural gas sources of energies. Although the use of coal for electrical power generation has been on the decline and it is being discouraged world-wide, the Smokeless Coal Research Team at IBB University has been able to differentiate “good” from “bad” coals. Not all coals are environmentally harmful for electrical power generation. And even for the so-called “bad coals the team at the IBBU has identified processes to clean up such coals. Whether generated from dams, gas turbines or thermal sources, Nigeria has been a nation with an acute supply of electricity; yet the country has far more resources of oil, gas and coals than it has the demand for them. The country is currently challenged by series of energy crises with concomitant economic and social implications. Electricity generation and energy availability are in gross shortfall which has resulted in the shutting down of many industries with the attendant job losses and escalating unemployment. Power supply to many homes is very epileptic, covering not more than 10% of the total average domestic daily demand. With these enormous energy challenges, the government of Nigeria is working on reforms at several fronts that will embrace appropriate energy-mix, enabling power generation / energy production from nuclear, coal and renewable sources (solar, wind, biomass) in addition to the conventional hydro and thermal sources. With the use of 30% diluted benzoic and formic acids on some Nigerian coals, smoke emission reduced considerably, thus allowing the use of abundant Nigerian coal deposits as alternative clean energy source for electrical power generation in a physically sustainable environment in an era of energy transition. Furthermore, some coals (in Nigeria and elsewhere) do not need processing (cleansing) based on the inherent chemical and petrological composition as elucidated in this paper. Power (electrical) is the single major factor to social and economic transformation in Nigeria.

The need for proper design of process equipment cannot be over emphasized in view of the vast human and technical involvement in every design activity. There is therefore the need to accomplish this design accurately and as fast as possible. The conventional approach adopted in the design process is to use mathematical models to obtain relevant design parameters. Accurate computation of the design parameters of new equipment is one of the main concerns of design engineers implementing different projects. Since some of features of an equipment cannot be expressed quantitatively and there are many qualitative features in data of the available process equipment. So, a method should be applied to use these data to estimate the desired and ideal output of design engineer. The case-based reasoning (CBR) method covers the qualitative data with regard to its nature. Using CBR method which is created based on the viewpoint of using previously solved problems in order to solve new similar problem save time and therefore speed of design is increased which is very important when considering the time required to estimate output which are design parameter in this case study. This research effort aims to use a Case-Based Reasoning (CBR) approach for process equipment design and attempts to investigate its advantages over traditional design approach.

Nanotechnology is a novel technology that develops material at a size of 100 nm or less which has become beneficial in various human endeavors because of its unique characteristic features. Nano-materials are utilized in medicine, Engineering, and agricultural industries. The unique properties of these materials are applied for beneficial purposes and at the same time may also have negative toxicological and environmental impacts. Considering the impacts on the environment and human health, nanomaterials could be harmful because they are easily distributed through the environment, aquatic, and human systems. Particularly in human body system, the unique properties have made its transportation and distribution through the skin, lungs, gastrointestinal tract very easy. However, several toxicological studies have shown considerable inherent toxicity of some nano-particles to living organisms, and their negative and harmful effects on the environment and aquatic systems for which both quantitative structure activity relationship and relatively tedious animal testing procedures are available in various literatures for their characterization. Because of the large number of nanoparticles manufactured with the different intrinsic properties especially sizes and coatings, there is therefore need to explore an alternative approach that will not necessitate conducting test on every nano-particle produced. It is the apprehensions of these potentially harmful effects of nanomaterials that constitute serious setback to nanotechnology commercialization. The objective of the study is to develop intelligent models to assess, evaluate, and manage the inherent risks. In view of these side effects, there is therefore the need to design and develop classification and nanomaterials toxicity predictive models using deep learning intelligent systems. This paper, therefore, focuses on the capability of deep learning techniques to model physicochemical properties and toxic effects of nanomaterials. Hence, the main motivation of this research work is to assist the users of nanomaterials in classifying, assessing and determining the risk of nanomaterials toxicity.

The concept of Evolutionary Computing method covers the process of searching for an optimal solution inspired by natural evolution. It can also be viewed as a family of trial and error problem solvers which can be considered as global optimization methods with a metaheuristic or stochastic optimization concept, characterized by the use of a population of candidate solutions. Such methods include Genetic Algorithm, Particle Swarm Intelligence and Differential Evolution among others. The conventional approach adopted in the design process is to use mathematical models and sensitivity approach to obtain relevant optimal design parameters. Accurate computation and optimization of the design parameters of new equipment is one of the main concerns of design engineers. The goal here is to apply evolutionary computing methods to design a gas cyclone with optimum design parameters taking into cognisance that the optimization process is complicated which requires an extensive search of a very large input space. The motivation of this research effort is the avoidance of complex mathematical models and sensitivity approach for gas cyclone design. The result shows that a hybrid Differential Evolution based Particle Swarm Optimization outperformed standard Genetic Algorithm, Particle Swarm Intelligence and Differential Evolution.