Entropy analysis on EMHD 3D micropolar tri-hybrid nanofluid flow of solar radiative slendering sheet by a machine learning algorithm.

The purpose of this paper is to analyze the heat transfer behavior of the electromagnetic 3D micropolar tri-hybrid nanofluid flow of a solar radiative slendering sheet with non-Fourier heat flux model. The conversion of solar radiation into thermal energy is an area of significant interest as the demand for renewable heat and power continues to grow. Due to their enhanced ability to promote heat transmission, nanofluids can significantly contribute to enhancing the efficiency of solar-thermal systems. The combination of silicon oil-based silicon (Si), magnesium oxide (MgO), and titanium (Ti) nanofluids has attracted attention for their ability to improve the performance of solar-thermal systems. The present study discloses a new approach for intelligent numerical computing solving, which utilizes an MLP feed-forward back-propagation ANN and the Levenberg-Marquard algorithm. The collection of data was conducted for the purpose of testing, certifying, and training the ANN model. The Bvp4c solver in MATLAB is utilized to solve the nonlinear equations governing the momentum, temperature, skin-friction coefficient, and Nusselt number. The characteristics of numerous dimensionless parameters such as porosity parameter [Formula: see text], vortex viscosity parameter [Formula: see text], electric field parameter [Formula: see text], thermal relaxation time [Formula: see text], heat source/sink parameter, [Formula: see text] thermal radiation parameter [Formula: see text], temperature ratio parameter [Formula: see text],nanoparticle volume fraction [Formula: see text] on Si + MgO + Ti/silicon oil micropolar tri-hybrid nanofluida are analyzed. The ANN model engages in a process of data selection, network construction, training, and evaluation of its effectiveness through the utilization of mean square error. Tables and graphs are used to show how essential parameters affect fluid transport properties. The velocity profile is decreased by higher values of the porosity parameter, whereas the temperature profile is increased. The temperature profile is inversely proportional to higher values of the electric field parameter. The micro-rotation profiles reduced by expanding values vortex viscosity parameter. It has been determined that entropy generation and Bejan number intensifications for enlarged nanoparticle volume fraction.

Promising strategies of circular bioeconomy using heavy metal phytoremediated plants - A critical review.

Phytoremediation is an excellent method for removing harmful heavy metals from the environment since it is eco-friendly, uses little energy, and is inexpensive. However, as phytoremediated plants can turn into secondary sources for heavy metals, complete heavy metal removal from phytoremediated plants is necessary. Elimination of toxic heavy metals from phytoremediated plants should be considered with foremost care. This review highlights about important sources of heavy metal contamination, health effects caused by heavy metal contamination and technological breakthroughs of phytoremediation. This review critically emphasis about promising strategies to be engaged for absolute reutilization of heavy metals and spectacular approaches of production of commercially imperative products from phytoremediated plants through circular bioeconomy with key barriers. Thus, phytoremediation combined with circular bioeconomy can create a new platform for the eco-friendly life.

Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction on residential buildings.

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy consumption. Designing a suitable energy efficient building based on their layout namely overall areas, distribution of the glazing areas, orientation, height, and relative compactness. Such components directly impact the heating load (HL) and cooling load (CL) of residential buildings. A precise predicting of load facilitates effective management of energy consumption and improves the quality of life. Lately, several studies have been implemented to predict the CL and HL. The most significant and challenging parts of predictive are defining the effective input parameter and developing a higher accuracy predictive model. The accuracy of predictive model based on machine learning algorithm must be enhanced by hybrid model. With this motivation, this article introduces an Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction (IHHOHDL-HCLP) model on Residential Buildings. The major aim of the IHHOHDL-HCLP model is to determine the CL and HL to accomplish effective energy utilization. To accomplish this, the IHHOHDL-HCLP primarily pre-processes the raw data in two levels namely min-max normalization and polynomial equation. In addition, the HDL model involves convolutional neural network (CNN) along with long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for HL and CL prediction process. Finally, the IHHO technique was applied for fine-tuning the hyperparameters related to the DL model. The IHHOHDL-HCLP model has demonstrated maximum prediction results with low RMSE values of 0.00874 and 0.00821, respectively, when applied to HL and CL, respectively. The experimental result analysis of the IHHOHDL-HCLP model demonstrates the better performance of the IHHOHDL-HCLP model over other DL models.