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The utilization of a high-volume of treated palm oil fuel ash (T-POFA) as a partial cement substitution is one of the solutions presented to reduce carbon dioxide emissions (CO2) and improve concrete sustainability. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is adapted as an artificial neural network (ANN) modeling tool to predict the compressive strength of self-compacting concrete (SCC) containing T-POFA. The ANFIS model has been developed and validated using concrete mixtures incorporating 0%, 10 wt%, 20 wt%, 30 wt%, 50 wt%, 60 wt%, and wt 70% T-POFA as a replacement of ordinary Portland cement (OPC) at a constant water/binder (W/B) ratio of 0.35. The experimental data were divided into 70% training data and 30% testing data. The experimental results of self-compacting concrete (SCC) containing T-POFA ensured comparable or higher compressive strengths, especially at later ages, when compared to the control SCC. However, the prediction results of the compressive strength of SCC samples using the ANFIS model are very close to the experimental values. The developed ANFIS model showed a highly-efficient performance to predict the SCC compressive strength. In addition, the obtained accurate predicted results using the developed ANN model will significantly affect the current experimental protocols, especially for costly and unsafe experiments.

In the current work, pure ZnO and Mn-doped ZnO nanoparticles were synthesized by the sol–gel autocombustion method. Structural analysis and phase determination were done by X-ray diffraction, and a hexagonal wurtzite structure was exhibited with disparate microstructures for all samples. Mn2+ ions were well composed, as evidenced by the fluctuation of lattice parameters, dislocation density, and lattice strain. Crystallite size decreases from 38.42 to 27.54 nm by increasing the doping concentration. Field emission scanning electron microscopy results shows the combination of evenly distributed spherical-like and hexagon-like structures. Fourier transform infrared spectra revealed that when Mn content increased, the absorption bands red-shifted. The drop in the energy band gap from 3.25 eV for ZnO to 2.99 eV for Zn0.96Mn0.04O was predicted by ultraviolet–visible absorption spectra. This red shift in the energy band gap can be explained by the sp–d exchange interaction between the band electrons of ZnO and localized d electrons of Mn. A study of magnetic properties revealed the change of the diamagnetic attribute for pure ZnO to the room-temperature ferromagnetic attribute of doped samples. In the current study, room-temperature ferromagnetism was achieved for Mn-doped ZnO nanoparticles, which can serve as a desirable option for practical applications in the future.

This study investigates the steady state sensible performance of multi pass parallel cross flow exchangers. Therein, the multi pass heat exchanger's performance was expressed through performance charts. Performance charts describe the performance of the heat exchanger in terms of pertinent dimensionless parameters such as capacity rate ratio, number of transfer units (NTU) and heat exchanger effectiveness. Previously developed matrix approach was employed to study the performance at each pass of the parallel cross flow heat exchanger. A maximum of up to ten heat exchanger passes were considered in this work. Employing performance charts, the maximum NTU that would yield the maximum heat transfer (or the maximum heat exchanger performance) was determined. Increasing NTU beyond the maximum, shall not perhaps enhance the heat transfer considerably, and this violates the common perception that increasing NTU enhances the heat transfer. This aspect shall help the engineers in optimizing the heat exchanger in terms of size, weight, material, and cost. Likewise, if the heat exchanger was in a continuous operation, the performance charts can help to detect an underperforming equipment without conducting any detailed calculations.  Index Terms-performance charts, multi-pass parallel cross-flow heat exchanger, sensible performance of heat exchanger

Prediction of new materials is crucial for the advancement of technology. Here, in this research work, the first-principle computation has been conducted utilizing the WIEN2K package to probe the structural, electronic, mechanical, and optical properties of barium-based chloroperovskites BaMCl3 (M = Ag, Cu) compounds. The optimized lattice constants are calculated for both compounds which are 9.90 Bohr for BaAgCl3 and 9.38 Bohr for BaCuCl3. To obtain better and more precise results for the electronic band’s structure, TDOS and PDOS (total and partial density of states), and the TB-mBJ potential approximation are employed. The indirect band gap (R–Γ) is found for both compounds having values of 1.173 eV and 2.30 eV for BaCuCl3 and BaAgCl3, respectively, which depicts its semiconducting nature. The calculation of elastic properties is conducted with IRelast code. The Cauchy pressure, Bulk modulus, Young’s modulus, Shear modulus, anisotropic ratio, Kleinman parameters, and Poisson’s ratio are calculated from the obtained elastic constants. The computation of elastic parameters indicates that the interested chloroperovskites are anisotropic, mechanically stable, hard to scratch, and ductile. From 0 eV to 40 eV incident photon energy ranges, the various optical parameter such as refractive index, absorption coefficient, dielectric function, reflectivity, extinction coefficient, and optical conductivity are analyzed. These compounds absorb maximum light within 5 to 25 eV incident photon energy. Hence, these materials are good light absorbers, therefore, they can be used in optoelectronic devices for high-frequency applications.