Though modern technology of new era is mostly dependent on power sector entirely, the current energy scenario is showing a serious negative effect for the last few decades. Comparatively, Bangladesh is facing a precarious effect because of the scarcity of fossil-fuel dissipation. To accomplish the power demand resolution, a new type of power generation is proposed in this research paper. Magnetic flux and solar irradiation is combined to get maximum power outcome. The PV panel supplies the maximum power in the peak solar radiation and terminates the energy stream at night time. However, the floating generator can supply its maximum creation day or night time according to the movement of water wave tendency. For this reason, a PV-floating Generator based integrated renewable energy scheme is inspected in this venture. The experimental result shows its real-world validation (Maximum 14.5 Watt output) comparing to conventional methods.
Laser-assisted high speed milling is a subtractive machining method that employs a laser to thermally soften a difficult-to-cut material’s surface in order to enhance machinability at a high material removal rate with improved surface finish and tool life. However, this machining with high speed leads to high friction between workpiece and tool, and can result in high temperatures, impairing the surface quality. Use of conventional cutting fluid may not effectively control the heat generation. Besides, vegetable-based cutting fluids are invariably a major source of food insecurity of edible oils which is traditionally used as a staple food in many countries. Thus, the primary objective of this study is to experimentally investigate the effects of water-soluble sago starch-based cutting fluid on surface roughness and tool’s flank wear using response surface methodology (RSM) while machining of 316 stainless steel. In order to observe the comparison, the experiments with same machining parameters are conducted with conventional cutting fluid. The prepared water-soluble sago starch based cutting fluid showed excellent cooling and lubricating performance. Therefore, in comparison to the machining using conventional cutting fluid, a decrease of 48.23% in surface roughness and 38.41% in flank wear were noted using presented approach. Furthermore, using the extreme learning machine (ELM), the obtained data is modeled to predict surface roughness and flank wear and showed good agreement between observations and predictions.
With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness.
Use of lubricating/cutting fluids is crucial in machining processes to reduce friction, alleviate heat accumulation and prolong tool life. To minimize environmental and health impacts, a number of studies using vegetable oil-based cutting fluid have been investigated and reported demonstrating similar performance obtained using commercial cutting fluids. However, massive use of vegetable oil for such purposes would undeniably trigger issues of food security. In order to mitigate food waste, the primary objective of the chapter is to demonstrate the application of waste palm cooking oil as a potential lubricating fluid in laser-assisted machining of metal. By considering kinematic and dynamic viscosities of the waste cooking oil, its effects on surface roughness and tool wear are studied by predicting using extreme learning machine (ELM). The prediction results show that the average errors are only 0.51% and 1.19% for surface roughness and flank wear, respectively, suggesting good agreement between observation and prediction.
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