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One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA).

This study focused on investigating the bottoming power cycles operating with CO2-based binary mixture, taking into account exergetic, economic and exergo-environmental impact indices. The main intent is to assess the benefits of employing a CO2-based mixture working fluid in closed Brayton bottoming power cycles in comparison with pure CO2 working fluid. Firstly, selection criteria for the choice of suitable additive compound for CO2-based binary mixture is delineated and the composition of the binary mixture is decided based on required cycle minimum temperature. The decided CO2-C7H8 binary mixture with a 0.9 mole fraction of CO2 is analyzed in two cycle configurations: Simple regenerative cycle (SRC) and Partial heating cycle (PHC). Comparative analysis among two configurations with selected working fluid are carried out. Thermodynamic analyses at varying cycle pressure ratio shows that cycle with CO2-C7H8 mixture shows maximum power output and exergy efficiency at rather higher cycle pressure ratio compared to pure CO2 power cycles. PHC with CO2-C7H8 mixture shows 28.68% increment in exergy efficiency with the levelized cost of electricity (LCOE) 21.62% higher than pure CO2 PHC. Whereas, SRC with CO2-C7H8 mixture shows 25.17% increment in exergy efficiency with LCOE 57.14% higher than pure CO2 SRC. Besides showing lower economic value, cycles with a CO2-C7H8 mixture saves larger CO2 emissions and also shows greater exergo-environmental impact improvement and plant sustainability index.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Power generation from river hydropower plants depends mainly on river flow. Water fluctuations in the river make the yield process unpredictable. To reduce these fluctuations, building a small reservoir at the river flow of the hydropower plant is recommended. Conventionally, classic single-pond models are commonly used to design run-of-river hydropower plants. However, such models are associated with fluctuations, sagging, and irregular power fluctuations that lead to irregular water fluctuations. This research proposes a novel idea to replace the single-pond model with a three-pond model to increase the plant’s overall efficiency. The three-pond model is developed as a three-tank nonlinear hydraulic system that contains the same amount of water as a conventional single pond. It also has the advantage of minimizing the run-of-river power plant’s dependence on river flow and increasing efficiency by trapping swell and turbulence in the water. To further increase the efficiency, the developed model was tested for smooth and effective level control using fuzzy control.

Fuzzy logic-based control systems are widely used in various fields like home appliances, medical instruments, automobiles, textile machinery, agriculture equipment and aviation for process control and data analysis. Fuzzy logic technique has shown great potential to solve the complex problems of physical world due to similarity with human understanding. Its advancements have gained widespread attention in different research areas. In several cases, it is very suitable for electronic devices which need to be precisely self-powered. In this work, an ANSYS-based simulation, fuzzy analysis, development and testing of a microelectromechanical system (MEMS)-based energy harvester have been presented. Zinc oxide (ZnO) nano rods were synthesized on an anodic aluminum oxide (AAO) template to form the MEMS energy harvester and study the effect of energy generation by applying force. The power of 5.16 nano Watts has been obtained by taking the numerical value of voltage (Voc) and current (Isc) as 3.16 mV and 0.985 µA respectively using fuzzy logic tool. Experimental testing of the harvester shows that the range of Voc is 3–6.4 mV and Isc is 0.45–1.5 μA. The results depict that this device can be used for touch screens to generate energy that can be further utilized for charging smart devices.

A high pressure oxydesulphurisation technique was investigated to reduce sulphur content, especially at ambient temperature. Prince of Wales coal was chosen for this study. The focus of the study was on the reduction of both pyritic and organic sulphur. The effects of pressure, coal slurry concentration, pH and KOH concentration in a fixed time interval on sulphur removal were studied with a series of experimental runs at ambient temperature. Heating value recovery was found to be increased with decreased pressure and with increased coal slurry concentration. It was found that sulphur removal was enhanced with an increase in pressure, with a more significant effect on the organic sulphur. With increase in the coal slurry concentration reduction, sulphur was found to be decreased.