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Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.

The forward model design was employed in the Diffuse Optical Tomography (DOT) system to determine the optimal photonic flux in soft tissues like the brain and breast. Absorption coefficient (mua), reduced scattering coefficient (mus), and photonic flux (phi) were the parameters subjected to optimization. The Box–Behnken Design (BBD) method of the Response Surface Methodology (RSM) was applied to enhance the Diffuse Optical Tomography experimental system. The DC modulation voltages applied to different laser diodes of 850 nm and 780 nm wavelengths and spacing between the source and detector are the two factors operating on three optimization parameters that predicted the result through two-dimensional tissue image contours. The analysis of the Variance (ANOVA) model developed was substantial (R2 = > 0.954). The experimental results indicate that spacing and wavelength were more influential factors for rebuilding image contour. The position of the tumor in soft tissues is inspired by parameters like absorption coefficient and scattering coefficient, which depend on DC voltages applied to the Laser diode. This regression method predicted the values throughout the studied parameter space and was suitable for enhancement learning of diffuse optical tomography systems. The range of residual error percentage evaluated between experimental and predicted values for mua, mus, and phi was 0.301%, 0.287%, and 0.1%, respectively.

Mathematically study mass transfer phenomena involving chemical reactions in the flow of Sisko Ferro nanofluids through the porous surface. Three ferronano particles, manganese-zinc ferrite (Mn1/2Zn1/2Fe2O4), cobalt ferrite (CoFe2O4), and nickel–zinc ferrite (Ni–Zn Fe2O4) are considered with water (H2O) and ethylene glycol (C2H6O2) as base liquids. Appropriate resemblance transitions are used to convert the governing system of a nonlinear PDE to a linear ODE. The Runge–Kutta method, as extended by the shooting technique, is used to accomplish the reduction governing equations. The effects of various associated parameters on fluid concentration and mass transfer rate are investigated: magnetic criterion (M), Siskofluid material factor (A), Solid volume fraction (ϕ) for nanofluids, permeability parameter (Rp), Chemical reaction criterion (γ), Brownian motion factor (Nb), and Thermophoretic parameters (Nt). The current findings indicate that the diffusion proportion of Sisko Ferronanofluid Ni–Zn Fe2O4–H2O and CoFe2O4–H2O is higher than that of Ni–Zn Fe2O4–C2H6O2 and CoFe2O4–C2H6O2 respectively but it is opposite in the case of Mn–Zn ferrite. The comparison study was carried out to validate the precision of the findings.

Onion (Allium cepa L.) is an important bulb plant grown worldwide. Proper use of the agronomic practice has undoubtedly contributed to growing crop yields. The right level of any farming practice, like the distance between plants, plant density, date of planting, and time of harvest, can produce the wanted outcomes. Therefore, this research was piloted to evaluate the influence of plant spacing on the development of bulb harvest-related traits of onion varieties in Dambi Dollo University, Western Ethiopia, in 2021. Three onion varieties (Adama red, Monarch, Nafis) and four intra-row spaces (6 cm, 8 cm, 10 cm, and 12 cm) in factorial combinations were settled by a complete randomized block design which was simulated three times. The findings of the study indicate that all the factors related to crop growth and bulb yield of onion varieties were mainly influenced by different kinds and plant spacing. Conversely, the collaboration of these two factors did not affect all other factors, but the interaction of the two factors had a great effect on the days to maturity measured in this study. The highest plant height was registered on the onion planted at a distance of 10 cm (59.83 cm) and 12 cm (59.08 cm) distance between plants. The high commercial yields (34.44 t ha−1) and entire bulb yield (35.40 t ha−1) were found in the Nafis variety. The highest marketable yields (31.12 ha−1) and entire marketable yield (31.78 ha−1) were recorded on an onion plant planted 10 cm between plants. Therefore, in the research area, farmers can use a variety of Nafis and a 10 cm distance between plants to increase their onion production.

Mathematically study mass transfer phenomena involving chemical reactions in the flow of Sisko Ferro nanofluids through the porous surface. Three ferronano particles, manganese-zinc ferrite (Mn1/2Zn1/2Fe

There is a growing amount of plastic waste that needs to be properly disposed of in order to protect the environment from the negative effects of increasing reliance on plastic products. Recent interest has focused on chemical recycling as a means of reducing plastic's negative environmental effects. Converting waste plastics into basic petrochemicals allows them to serve as hydrocarbon feedstock or fuel oil through pyrolysis operations. Scientists have taken a keen interest in the production of bioethanol from renewable feedstocks due to its potential as a source of energy and alternative fuel. Due to its beneficial effects on the environment, ethanol has emerged as a promising biofuel. In this paper, energy recovered from low-density polyethylene and high-density polyethylene waste was converted into an alternative plastic fuel and evaluated for its environmental impact with the blending of ethanol in a diesel engine. Ternary fuel blends with 20%, 30%, and 40% waste plastic fuel and 10%, 15%, and 20% ethanol with standard diesel were tested. The study found that blending 10% ethanol with 20% plastic fuel decreased fuel consumption by around 7.9% compared to base diesel. Carbon monoxide emissions are reduced by about 10.2%, and hydrocarbon emissions are reduced by about 13.43% when using the same ternary blend. The optimum values of fuel consumption and emissions were obtained by full factorial design for a ternary fuel blend of 10% ethanol and 20% plastic fuel at the full load condition.

Dimethyl ether (DME) and its blend of refrigerants (R429A, R435A, and R510A) are considered in this study's second law analysis as potential replacements for R134a. The performance of various refrigerants in a vapour compression refrigeration system is examined using the Design package CYCLE D. The software REFPROP 9.0 is used to extract all of the thermal and physical parameters of DME and its blend of refrigerants. The Second law performance parameters such as Efficiency Defects, Entropy generation and ExergyEfficiency are discussed. The refrigerants R429A and R510A are more energy efficient than R134a across a condensing temperature range of 30 to 55 °C at - 10 °C evaporation temperature. R134a was exceeded by R429A and R510A in terms of exergetic efficiency by 2.08 and 0.43%, respectively. In comparison to other losses in different components, the compressor's exergy loss is larger at 37-40% of the total exergy loss. By employing RE170 and its blends, the Vapour Compression Refrigeration System often performs better under the second law than R134a.The result shows that the efficiency defects in the compressor are the largest, followed by the condenser and evaporator. Thus, the design improvement of a compressor is of at most importance to improve the system performance by lowering the overall irreversibility.

Titanium dioxide nanoparticles (TiO2 NPs) were prepared by Caricaceae (Papaya) Shell extracts. The Nanoparticles were analyzed by UV–Vis spectrums, X-ray diffractions, and energy-dispersive X-rays spectroscopy analyses with a scanning electron microscope. An antifungal study was carried out for TiO2 NP in contradiction of S. sclerotiorums, R. necatrixs and Fusarium classes that verified a sophisticated inhibitions ratio for S. sclerotiorums (60.5%). Germs of pea were individually preserved with numerous concentrations of TiO2 NPs. An experience of TiO2 NPs (20%, 40%, 80% and 100%), as well as mechanisms that instigated momentous alterations in seed germinations, roots interval, shoot lengths, and antioxidant enzymes, were investigated. Associated with controls, the supreme seeds germinations, roots and plant growth were perceived with the treatments of TiO2 NPs. Super-oxide dis-mutase and catalase activities increased because of TiO2 NPs treatments. This advocates that TiO2 Nanoparticles may considerably change antioxidant metabolisms in seed germinations.

Titanium dioxide nanoparticles (TiO