A new approach to predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called the group method of data handling (GMDH) and is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in the literature. The model was then evaluated using statistical indices, as well as being compared with analytical models from previous studies. The results of the coefficient of determination (R²) indicate the high accuracy of the proposed model, with values of 0.9719 and 0.9513 for training and testing for the dimensionless distance from the nozzle to the return point $x_r/D$ and 0.9454 and 0.9565 for training and testing for the dimensionless terminal rise height $y_t/D$. Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of the estimate (SEE) was applied to demonstrate which model performed the best in terms of approaching the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and it was the best model, with an approximately 10% error, which was the lowest error value among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

Makkah is a holy city that attracts millions of pilgrims throughout the year. These demographic fluctuations result in significant variations in monthly waste generation up to 31%. This study adopts a multi-criteria decision-making framework to evaluate ten sustainable waste management strategies. A novel waste equalization facility is introduced to buffer peak waste loads and enhance the performance of downstream waste valorization units A fuzzy analytical hierarchy process was conducted, assigning the highest importance weight to greenhouse gas (GHG) emissions (35.2%), followed by resource recovery (25.7%), net present value (NPV) (24.7%), and land requirements (14.3%). Results revealed that the most sustainable configuration integrates mixed waste processing facility, waste equalization facility, incinerator, and landfill, reducing emissions and land requirements by 74% and 56%, improving energy generation and NPV by 25,537 GWh and $309 million, respectively. On the other hand, full incineration yielded the highest energy output (50,156 GWh), 67% land savings, and mitigated 26% of baseline GHGs, coupled with negative NPV of $-445 million. The integration of equalization facility reduced GHG emissions by 725 Gg CO2-eq, land requirements by 119,335 m2, and resulted in an overall increase in energy production of 1,136 GWh compared to the same scenario without the facility.

A new approach for predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called group method of data handling (GMDH) which is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in literature. The model was then evaluated using statistical indices as well as compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate a high accuracy of the proposed model with values of 0.9719 and 0.9513 for training and testing for the dimensionless of the distance from the nozzle to the return point xr/D, and 0.9454 and 0.9565 for training and testing for the dimensionless of the terminal rise height yt/D . Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of estimate (SEE) was applied to demonstrate which model performed the best in terms of getting closer to the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and was the best model with approximately 10% error, which was the lowest value of error among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

Recently, plastic and agricultural waste have gained attention as sustainable alternatives. Despite efforts to recycle these materials, much still ends up in landfills, raising environmental concerns. To optimize their potential, these wastes ought to be transformed into value-added products for diverse industrial applications. This work focused on producing thin composite material films using olive oil solid waste called JEFT and recycled plastic bottles. JEFT was cleaned, dried, and processed mechanically via ball milling to produce nano- and micron-sized particles. Composite films were produced via melt compounding and compression molding with a rapid cooling process for controlled crystallinity and enhanced flexibility. Their density, water absorption, tensile strength, thermal stability, water permeability, functional groups, and biodegradation were comprehensively analyzed. Results indicated that 50% JEFT in recycled plastic accelerated thermal deterioration (42.7%) and biodegradation (13.4% over 60 days), highlighting JEFT’s role in decomposition. Peak tensile strength (≈32 MPa) occurred at 5% JEFT, decreasing at higher concentrations due to agglomeration. Water absorption and permeability slightly increased with JEFT content, with only a 1% rise in water permeability for 50% JEFT composites after 60 days. JEFT maintained the recycled plastic’s surface chemistry, ensuring stability. The findings of this study suggest that JEFT/r-HDPE films show potential as greenhouse coverings, enhancing crop production and water efficiency while improving plastic biodegradation, offering a sustainable waste management solution.

Recently, plastic and agricultural waste have gained attention as sustainable alternatives. Despite efforts to recycle these materials, much still ends up in landfills, raising environmental concerns. To optimize their potential, these wastes ought to be transformed into value-added products for diverse industrial applications. This work focused on producing thin composite material films using olive oil solid waste called JEFT and recycled plastic bottles. JEFT was cleaned, dried, and processed mechanically via ball milling to produce nano- and micron-sized particles. Composite films were produced via melt compounding and compression molding with rapid cooling process for controlled crystallinity and enhanced flexibility. Their density, water absorption, tensile strength, thermal stability, water permeability, functional groups, and biodegradation were comprehensively analyzed. Results indicated that 50% JEFT in recycled plastic accelerated thermal deterioration (42.7%) and biodegradation (13.4% over 60 days), highlighting JEFT's role in decomposition. Peak tensile strength (≈32 MPa) occurred at 5% JEFT, decreasing at higher concentrations due to agglomeration. Water absorption and permeability slightly increased with JEFT content, with only a 1% rise in water permeability for 50% JEFT composites after 60 days. JEFT maintained the recycled plastic's surface chemistry, ensuring stability. The findings of this study suggest that EFT/r-HDPE films show potential as greenhouse coverings, enhancing crop production and water efficiency while improving plastic biodegradation, offering a sustainable waste management solution.