Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char:A statistical neural network approach

The goals of this study are to assess the viability of waste tire-derived char(WTDC)as a sustainable,low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network(SCNN)model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC.The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDCmodified asphalt mixtures(WTDC-MAM).The input variables comprised waste tire char content and asphalt binder content.The output variables comprised mixture unit weight,total voids,voids filled with asphalt,Marshall stability,and flow.Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures.For predictive modeling,the SCNN model is employed,incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability.The optimal network architecture,using the collected dataset,was a 2:6:5 structure,and the neural network was trained with 60%of the data,whereas the other 20%was used for cross-validation and testing respectively.The network employed a hyperbolic tangent(tanh)activation function and a feed-forward backpropagation.According to the results,the network model could accurately predict the volumetric and Marshall properties.The predicted accuracy of SCNN was found to be as high value>98%and low prediction errors for both volumetric and Marshall properties.This study demonstrates WTDC's potential as a low-cost,sustainable aggregate replacement.The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

The effect of altering crosslinker chemistry during interfacial polymerization on the performance of nanofiltration membranes for desalination, organic, and micropollutants removal

Chemistry of the polyamide active layer of a desalination membrane is critical in determining both its physical and chemical properties.In this study,we designed and fabricated three novel membranes with different active layers using the crosslinkers:terephthaloyl chloride,isophthaloyl chloride,and trimesoyl chloride.The crosslinkers were reacted with an aqueous solution of an aliphatic tetra-amine.Because these crosslinkers differ in their structures and crosslinking mechanisms during interfacial polymerization,the resultant membranes also possess different structural properties.The water contact angle of the fabricated membranes also varies;the water contact angles of 4A-3P-TPC@PSF/PET,4A-3P-TMC@PSF/PET,and 4A-3P-IPC@PSF/PET,are 68.9°,65.6°,and 53.9°,respectively.Similarly,the desalination performance of resultant membranes also showed variations,with 4A-3P-TPC@PSF/PET,4A-3P-IPC@PSF/PET,and 4A-3P-TMC@PSF/PET having a permeate flux of 17.14,25.70,and 30.90 L·m^(−2)·h^(−1),respectively,at 2.5 MPa.The 4A-3P-TPC@PSF/PET membrane exhibited extensive crosslinking with aliphatic linear amine,and cationic dye rhodamine B,MgCl_(2),and amitriptyline rejection rates of 98.6%,92.7%and 80.9%,respectively.The 4A-3P-TMC@PSF/PET membrane showed mediocre performance,while 4A-3P-IPC@PSF/PET membrane showed even lower performance,with a 35%rejection of methyl orange dye.