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Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.

investigates the production of sustainable geopolymer concrete using industrial wastes such as Ground Granulated Blast Furnace Slag and ultra fine Rice Husk Ash (URA). The effect of partial substitution of GGBS with URA in proportions such as 0, 5, 10, 15 and 20 percent is investigated for workability, drying shrinkage, compressive and tensile strength over different ages of concrete ranging from 7 to 90 days. A micro structural investigation through Scanning Electron Microscope and X-Ray Diffraction Analysis is carried out to analyze the micro structure of matrix. Further a sustainability analysis is conducted over the geopolymer specimens through the parameters such as cost efficiency, energy efficiency and CO2 efficiency. Results from the tests indicate a significant enhancement in workability, compressive and tensile strength and decrease in the drying shrinkage values with 15 percent utilization of URA in GPC. Micro structural study also exhibited a compact and dense microstructure of the specimen. Results clearly portray the coexistence of both calcium based product and sodium based product. Sustainability analysis indicates increased cost efficiency and Eco efficiency and reduction in the energy consumption with the utilization of 15 percent of URA. The study also reported the possibility of reduction of carbon footprint by increasing the dependency over Geopolymer concrete. The findings of the study unleash hefty potential towards utilizing grounded RHA in alkali activated concrete.

This study is focused on the application of activated carbon nanoadsorbent derived from Ocimum basilicum Linn (sweet basil) leaves for the removal of methyl orange dye from an aqueous solution. The Ocimum basilicum Linn leaves are dried, powdered, cured with H2SO4, and thermally treated to form an activated carbon biosorbent. Sorbent characterization studies like scanning electron microscope (SEM) and Fourier transform infrared (FTIR) spectroscopy have revealed the adsorption of the methyl orange dye from their aqueous solution in the batch mode process. The biosorbent has shown a maximum adsorption capacity of 1.54 mg g−1 at 10 mg l−1 concentration, 1.2 g sorbent dosage, pH of 3, contact time of 180 min, and pHpzc at 3.9. Experimental results are analyzed using equilibrium models and it is found that the Langmuir isotherm model and kinetic model fit well and also the results have corresponded well with pseudo-first order. The intraparticle diffusion (IPD) mechanism has shown that pore diffusion occurs at a slower rate. The Elovich model has displayed that adsorption is affected by film diffusion. From the statistical optimization studies, it is demonstrated that Box–Behnken model can correlate the good agreement between experimental and predicted values. The highest adsorption capacity for the nanoadsorbent was found using quadrate models and optimizing the variables at a time of 237 min, initial dye concentration of 5.31 mg l−1, adsorbent dose of 1.22 g, and pH of 4.23.

The current study addresses the results of an excremental study carried out over the feasibility of utilization of flyash in the self compacting concrete with an effort to reduce the consumption of cement leading to reduced green house gas emission corresponding to the production of cement. Flyash was added on various dosages to the self compacting concrete along with the super plasticizer. Various ingredients were proportioned based on the Japanese method. The mix that passed the EFNARC guidelines were tested for the mechanical properties such as compressive strength, split tensile strength and flexural strength. Fair results have been obtained and this study unleashes a great potential for the utilization of flyash in self compacting concrete.