This research performed a techno-economic analysis of diesel-biogas hybrid microgrid system. The paper modeled, designed, and simulated the microgrid system using MAT-LAB/SIMULINK and performed system optimization using HOMER software. The anaerobic digestion (AD) processes were designed and simulated with the aid of Simulink to obtain the methane yield from the reactor. Results show that the methane yield is 95.04 kg/day at a reactor temperature of 55 • C. The synchronous generator was modeled and simulated for the application of both diesel fuel and biogas fuel system. The HOMER software was used to optimize the hybrid micro-grid system with the diesel system taken as the base case. Biogas production was varied between 1 and 5 tons while the calculated energy demand of the village was 271925 kWh. At a biomass production of 4 tons and above, the hybrid system became powered by only the biogas system for total energy production. The energy produced by biogas is 452820 kWh and a cost of energy (COE) of $0.0484. The net present cost (NPC) of the base case system is $1141292 while that of the hybrid system is $176600 and that of the biogas system is $170085 which shows the saving cost of 84.5% and 85.1%, respectively, compared to the base case system over the project lifetime.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Biogas power generation is renewable energy made from biological materials. Biogas power production is technology which helps in development of sustainable energy supply systems. This paper develops Genetic Algorithm optimization model for Biogas electrical power generation of Ilora in Oyo, Oyo state. The production is done using co-digestion system of pig dung and Poultry dung under the process of anaerobic digestion. The pig dung and poultry dung were mixed 50:50%. MATLAB and VISUAL BASIC Software was used to carry out simulations to develop optimized Genetic Algorithm model for Biogas power production with aims to improving electricity accessibility and durability of the community. The results of the research reveal the Empirical Biogas power production without and with Genetic Algorithm optimization. The Result showed that biogas electrical power generated without and with Genetic Algorithm Optimization were 5KW and 11.18KW respectively. The biogas power generation was increased by 6.18KW, which is 38.2% increase after Genetic Algorithm optimization. The results show the application of the Genetic Algorithm optimization model which can be used to improving Biogas power generation when amount of methane gas produced from the animal dung varies with speed of thermal rotating shaft.

The hydroelectric plants flow rate always varies with time due to the speed rotation of turbines which affect the amplitude and frequency of electrical energy generated.  Hydro plants are being utilized for the purpose of peaking as well as base load, pumped storage and spinning reserve power operation etc. Especially in a system consisting of large industries, where frequency and voltage fluctuations are required to be kept minimum, their stability determines the quality of power. For efficient use of plant, complex control techniques are employed in the station automation and these involve the turbine governor in control features for which a flexible governor design is essential. MATLAB 2007 Software was used to carry out simulations analysis to develop fuzzy logic controller for hydropower Generator speed regulation with aims of stabilizing output power supply in order to increase the water flows rate through the hydropower penstock. The result of the research shows the hydropower generator speed model which can be used to stabilized power output as a result of increase in turbine speed rotation when fuzzy logic controller is applied. The result showed that hydropower Generation speed regulation with and without fuzzy logic controller were 319.8m/s and 65m/s respectively. The speed increased by 254.8m/s. the result shows that application of  fuzzy logic controller gives better result and increase the rotational speed of hydropower.

The hydroelectric plants flow rate always varies with time due to the speed rotation of turbines which affect the amplitude and frequency of electrical energy generated.  Hydro plants are being utilized for the purpose of peaking as well as base load, pumped storage and spinning reserve power operation etc. Especially in a system consisting of large industries, where frequency and voltage fluctuations are required to be kept minimum, their stability determines the quality of power. For efficient use of plant, complex control techniques are employed in the station automation and these involve the turbine governor in control features for which a flexible governor design is essential. MATLAB 2007 Software was used to carry out simulations analysis to develop fuzzy logic controller for hydropower Generator speed regulation with aims of stabilizing output power supply in order to increase the water flows rate through the hydropower penstock. The result of the research shows the hydropower generator speed model which can be used to stabilized power output as a result of increase in turbine speed rotation when fuzzy logic controller is applied. The result showed that hydropower Generation speed regulation with and without fuzzy logic controller were 319.8m/s and 65m/s respectively. The speed increased by 254.8m/s. the result shows that application of  fuzzy logic controller gives better result and increase the rotational speed of hydropower.

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This research focused on robotic ultraviolet light (RUV light) for sanitizing the environment due to the outbreak of the Coronavirus pandemic devastating the whole world. The prototype Robotics UV-light device was proposed in this research to relieve mankind of sanitization of the ecosystem since SARS-CoV-II is highly infectious. The robotic Ultra-violet system was developed using perception subsystems as well as cognition subsystems, all linked together to perform the needed functions. Nine perceptions recognition subsystems were deployed with a remote controller for the driving as well as monitoring of the system for optimum performance while in operation. The Robotics was built with four lamps of ultra-violet lights such that while in operation, the targeted environment gets sanitized at the same time. An extra lamp was attached at the top end of the robotic device which is used to fumigate the upper part of the wards where the other lights could not reach. One of the inbuilt perception subsystems collects information on the extent of sanitization and then via the cognition subsystem shuts down the system automatically. If by chance a novice approaches the ward(s) where the robotic system is working, another perception subsystem will perceive human presences and through the cognitive device raises a mimic human tone programmed, “this place is not safe now, quickly shift". If within ten nano-seconds and the novice still resist the warning, then the machine shutdown automatically. The performance of this electromagnetic light has an efficiency of 99.99 percent over both bacteria and viruses including Covid-19.

The computation minimization which is associated with neurofuzzy models and controllers may be installed in industrial programmable Logic controllers (PLC). The implementation of thermal sterilization is both model and control in order to validate the pilot plant of water canned industry. Volume control in beverage manufacturing companies requires that a certain measured volume be filled into specific container sizes to conform to already predetermined standard to ensure competitiveness in the industry. However, most industry does not have accurate and reliable monitoring mechanism capable of sensing when the canned bottles are not properly filled. This operational failure can be overcome by designing a model that will monitor and control the filling process thereby improving volume control in water canning industry using feedback Neuro-fuzzy control. MATLAB Software was used to carry out simulations to develop volume control in water canning industry with aims of improving operational mechanism of industry. The result of the research revealed the empirical data collected in Rancor Nig. Ltd., Enugu, Nigeria and feedback Neurofuzzy. This ANN model can then be trained with values generated from an already existing mathematical model to be able to monitor and control the filling of the cans. The result showed that volume control in water canning industry with and without feedback Neuro-fuzzy were 63cl and 50cl respectively. The volume increased by 13cl. With these results, it shows that using feedback Neuro-fuzzy gives a better result in terms of filling to the required volume of the bottle than when feedback Neuro-fuzzy is not used.

This research work modelled and optimized the hybrid microgrid energy system for electricity generation at the University of Abuja, Nigeria, using PV, wind, diesel, and battery renewable energy resources. The model and optimization of the system are performed through HOMER software. The estimated university average annual power consumption is 2355 kWh/day, and the optimal load demand is 313.40 kWp. The PV/wind/diesel/ converter/battery hybrid system has the lowest cost of energy (COE) of 0.1616 $/kWh, operating cost of $50,592, and net present cost (NPC) of $1,795,026 but diesel/wind/converter/battery hybrid system has highest COE of 0.4242 $/kWh and NPC of $4,710,983. The optimal total electricity generated is 1,272,778 kWh/yr while electricity generated by PV contribute the highest energy of 1,030,485 kWh/yr (81%), whereas diesel generator and wind produced energy of 93,927 kWh/yr (7.38%) and 148,366 kWh/yr (11.7%) respectively. The wind/diesel/converter/battery hybrid system produced carbon dioxide (CO2) of 557,749 kg/yr. The most environmentally friendly is the wind/PV/battery and PV/battery hybrid system without pollutants emissions, but the diesel/wind/battery hybrid system has the highest rate of pollutants emissions. The result shows that PV’s electrical power is extremely high from February to June, which causes a high rate of irradiance within the specified period.