Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R 2 value is generally obtained which is around 5% greater than the ANN modeling results reported.However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.
The soluble oxygen content in the water is affected by oxygenation, which is a vital factor for commercial fishery fields. The soluble oxygen content is the prime factor for the fishes grown in those fields, and most probably, the fields are located in remote locations where electricity could not be accessible. Photovoltaics (PVs) are becoming more popular as a renewable resource. The electric power generated by solar panels can be utilized to run the aerators in order to aerate the fishery fields that are isolated and disconnected from the primary power system. A 100 Wp floater solar PV (SPV) modules for powering five numbers of DC aerators are used in the present investigation. The efficiency of floated solar panels was monitored by measuring sun irradiation, PV temperatures, output power, and current. The amount of oxygen in the water before aeration was around 3.2 mg/L, however after deploying floated solar panels and aerator, the level of soluble oxygen was raised to 4.4 mg/L.
In this chapter, the authors discuss the utilization of e-waste in the concrete for civil construction activities. Various tests have been used to investigate the effects of e-waste mixed with concrete. The various percentages of e-waste have been mixed with concrete to improve the strength of buildings. An e-waste concrete beam has a maximum tensile strength of 6.23 MPa under sulfuric curing conditions, and the highest flexural strength at 10% e-waste replacement during the hydrochloride curing process. The compressive strength is at its highest value when e-waste replaces 10% of it. After 28 days of curing, the concrete cylinder's maximum split tensile strength was 15%. Thus, the e-waste could be effectively utilized for civil construction purposes to reduce its environmental impacts.
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