Solar desalination system for fresh water production performance estimation in net-zero energy consumption building: A comparative study on various machine learning models.

This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.

Entropy production with the flow of nanomaterials through the permeable stretched surface with heterogeneous-homogenous chemical reaction.

In various thermodynamic procedures and the optimisation of thermal manipulation, nanofluids flowing through porous media represent an emerging perspective. The main objective of this study, from the perspective of thermal applications, was the investigation of the flow of nanofluid over a horizontal stretched surface embedded in a porous medium. The effects of the chemical reactions on the surface, magnetic field, and thermal radiations were invoked in the mathematical formulation. The non-Darcy model examines the fluid flow in the porous media. The principles of thermodynamics were employed to integrate entropy optimisation methods with the established theoretical approach to analyse the thermal behaviour of nanomaterials in the chemical reactive diffusion processes. Using the Tiwari-Das nanofluid model, the volume fraction of the nanomaterials was merged in the mathematical equation for the flow model. Water was taken as a base fluid and nanoparticles composed of aluminium oxide (Al2O3) and silver (Ag) were used. The significance of radiation, heat production, and ohmic heating were included in the energy equation. Furthermore, an innovative mathematical model for the diffusion of the autocatalytic reactive species in the boundary layer flow was developed for a linear horizontally stretched surface embedded in a homogeneous non-Darcy porous medium saturated with the nanofluid. The computer-based built-in bvp5c method was used to compute numerically these equations for varied parameters. It is clear that the magnetic parameter has a reversal influence on the entropy rate and velocity. Temperature and velocity are affected in the opposite direction from a higher volume fraction estimate. Thermal field and entropy were increased when the radiation action intensified. The inclusion of nanoparticle fraction by the volume fraction of nanoparticles and Brinkman number also enhances the system entropy. Entropy production can be minimized with the involvement of the porosity factor within the surface.

Chitosan and silver nanoparticles are attractive auxin carriers: A comparative study on the adventitious rooting of microcuttings in apple rootstocks.

Nanocarriers for encapsulation and sustained release of agrochemicals such as auxins have emerged as an attractive strategy to provide enhanced bioavailability and efficacy for improved crop yields and nutrition quality. Here, a comparative study was conducted on the effectiveness of chitosan-as a biopolymeric nanocarrier- and silver-as a metallic nanocarrier- on in vitro adventitious rooting potential of microcuttings in apple rootstocks, for the first time. Auxins indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA) loaded silver (nAg) or chitosan nanoparticles (nChi) were synthesized. Scanning electron microscopy and transmission electron microscopy studies showed the spherical shape of the nanoparticles. The average particle size of IAA-nChi was 167.5 ± 0.1 nm while that of IBA-nChi was 123.2 ± 2.6 nm. The hydrodynamic diameter of the nAg-IAA and nAg-IBA particles were measured as 93.66 ± 5 nm and 71.41 ± 3 nm, respectively. Fourier transform infrared spectroscopy analyses confirmed the encapsulation of IAA or IBA in the chitosan nanoparticles. Meanwhile, the characteristic peaks of IAA or IBA were detected on silver nanoparticles. In-vitro adventitious rooting of microcuttings of Malling Merton 106 (MM 106) was significantly higher both in chitosan and silver nanoparticles loaded with IAA or IBA (91.7%-62.5%) compared to free IAA or IBA applications (50.0%-33.3%), except for 2.0 mg L-1 IBA (66.7%). However, the application of 2 mg L-1 IBA and IBA-nChi at all concentrations caused an undesirable large callus development.