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Since solar energy is intermittent, finding the best solutions is a challenge. This paper provides a thorough review of solar energy systems' generic optimization objectives. The intelligent optimization techniques for solar energy systems are discussed, including their functions, constraints, contributions, mathematical models, and analysis methods. Optimization studies using new and traditional generation techniques are analyzed, and a few optimization methods, including combined hybrid algorithms, are presented. New generation artificial intelligence algorithms have been most widely used during the last decade, needing less computational time. They have good convergence and better accuracy than traditional optimization methods. They can scan local and global optima and do robust calculations. Solar system optimization has demonstrated remarkable benefits in size, load demand, and electricity output. The improvements reduce operating expenditures, power losses, and peak output integration and controllability. With a 50% rise in power prices, the optimal number of solar collectors rises by approximately 25%. However, with adjustment as per optimization techniques, the solar absorption cooling system's maximum thermal efficiency can be increased up to 75%.The present study recommends using two or more algorithms to overcome the curbs of a single algorithm. The main aim of optimization strategies, according to this assessment, is to reduce capital expenditures, operation and maintenance expenses, and emissions while improving system reliability. The paper also briefly describes several solar energy optimization challenges and issues. Lastly, some practical future approaches for developing a reliable and efficient solar power system are proposed for developing the complex renewable energy-based hybrid system.

The focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavg and MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (α) is analyzed. SOO gives highest fitness of Nuavg and lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavg with MaxT in the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of α is important during training and testing. The best Nuavg obtained from SOO using MVO algorithm is around 82.06 while MaxT is 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavg and MaxT around 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxT while it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxT and Nuavg respectively.

The effect of the addition of different proportions of silver (Ag) nanoparticles and alcohols in milk scum oil methyl ester on the performance of engine and emission are studied. B20 blend is added with 5% of ethanol, n-butanol, and iso-butanol as ternary additives for the experimental analysis from no load to full load. Furthermore, at a fixed load, operating conditions such as injection pressure (12 and 15 bar) and injection timing (23° and 26°) are varied without and with the addition of 0.8 vol% of Ag (silver) nanoparticles to the fuel blends. Also, the concentrations of Ag nanoparticles are increased from 0.2 to 1 vol% and comparisons are made with diesel and B60 blend. Mathematical models are developed for selected features of engine performance which fits with the experimental values for the purpose of optimization using the Dragon fly algorithm (DA) by considering these models as the objective functions. The concentration of nanoparticles lowers the BSFC significantly and helps in reducing the emission with an increased percentage. Using full biodiesel, 16.6% reduction in BTE was obtained, while use of alcohols prevented this reduction approximately by 5%. A highest of 4.6% improvement was obtained with the addition of Ag nanoparticles. 4.5% reduction in HC and 13% in NOx emission using nanoparticles are obtained. The DA algorithm provided the same optimized value at the end of 30 iterations in different cycles of execution. Nanoparticle addition and use of pressure in the range of 20 bar gives the lowest emission from the engine.

Abstract There is a demand for economic, affordable cost manufacturing and Improvement in power conversion energy of DSSC has made most researches finding for alternative ways to optimize each components of the cell to improve its efficiency. This paper reviews alternative ways to manufacture and synthesizing purpose for DSSC. The DSSC is one of the example for renewable energy power generation. This paper reviews the manufacturing or method by utilizing the FTO coated glass (Dr. Blade method) for applying the paste on glass substrate for effective sunlight harvesting.Photoanode prepared by ZnO powder, Counter electrode prepared by the combination of Electrolyte (Ki + I2 + Acetonitrile). Scanning electron microscopy revealed a material with an crust and trough structures like morphology. With X-ray diffraction analysis, With the help of thermal imager found the emittance and temperature on the DSSC surface , X-ray Florescence’s used for finding components present in the semi conductiveoxide. We can measure the Open circuit voltage is 35.5 and Short circuit current 0.024 by connecting in series with DSSC by using Multimeter, fill factor is 1.477 and efficiency is 0.164.