The harmful environmental impacts of fossil fuel combustion, particularly greenhouse gas (GHG) emissions, have driven global interest in developing sustainable biodiesel alternatives. Pakistan imports 294.46 million tons of high-speed diesel (HSD) annually, costing approximately USD 140.237 million. A 10% biodiesel blend could save 29.446 million tons of HSD and USD 14.023 million annually. Fish waste, a significant byproduct of Pakistan’s fishing industry, offers a promising feedstock for biodiesel production. This study explores its conversion into biodiesel and evaluates performance in diesel engines, supporting sustainability and circular economy goals. This study produced fish waste biodiesel through two-step transesterification reactions, achieving a 68% conversion yield. The biodiesel exhibited properties within ASTM D6751 standards, with a calorific value of 40.47 MJ/kg and a cetane number of 55.92. Engine performance and emission tests on LOMBARDINI 15LD225 diesel engines showed significant CO emission reductions with B10 and B20 blends compared to conventional diesel. Simulation using Ricardo Wave software 2019.1 demonstrated a 90% model accuracy for predicting CO emissions. The findings highlight the viability of fish waste-derived biodiesel as a cleaner, renewable alternative to fossil diesel, supporting sustainability and circular economy goals.

This paper presents optimization of peak temperatures achieved during friction stir welding (FSW) of Al-6061-T6 alloys. This research work employed a novel approach by investigating the effect of FSW welding process parameters on peak temperatures through the implementation of finite element analysis (FEA), the Taguchi method, analysis of variance (ANOVA), and machine learning (ML) algorithms. COMSOL 6.0 Multiphysics was used to perform FEA to predict peak temperatures, incorporating seven distinctive welding parameters: tool material, pin diameter, shoulder diameter, tool rotational speed, welding speed, axial force, and coefficient of friction. The influence of these parameters was investigated using an L32 Taguchi array and analysis of variance (ANOVA), revealing that axial force and tool rotational speed were the most significant parameters affecting peak temperatures. Some simulations showed temperatures exceeding the material’s melting point, indicating the need for improved thermal control. This was achieved by using three machine learning (ML) algorithms, i.e., Logistic Regression, k-Nearest Neighbors (k-NN), and Naive Bayes. A dataset of 324 data points was prepared using a factorial design to implement these algorithms. These algorithms predicted the welding conditions where the temperature exceeded the melting temperature of Al-6061-T6. It was found that the Logistic Regression classifier demonstrated the highest performance, achieving an accuracy of 98.14% as compared to Naive Bayes and k-NN classifiers. These findings contribute to sustainable welding practices by minimizing excessive heat generation, preserving material properties, and enhancing weld quality.

Computational fluid dynamics (CFD) simulations are carried out to examine the effect of geometricparameters of Tesla valve on flow patterns, pressure drop and flow diodicity. In forward flow, the flow is relatively smooth with less flow resistance. The low velocity regions are present at the entrance to the curved section and at thejunction of the curved section and the exit channel, where flow separation takes place. In reverse flow, the flow is quite irregular, and major recirculation zones are observed in the bottom branch, in addition to the top-curved and exit sections. The simulations with small time steps show that the flow is steady when the flow takes place in the forwarddirection. The flow is mostly transient; however, when the fluid flows in the reverse direction, particularly at higher Reynolds numbers. The effect of geometric parameters such as the angles subtended by the curved section (with thehorizontal) shows that optimal values of these angles exist. For a certain range of angles, diodicity is greater than 2.The effect of multi-staging of the Tesla valve is studied, and it is found that the flow unsteadiness and overall diodicity increase with the number of stages.

This paper includes analysis of the flow of air and solid particles in fluidized bed units using a computational fluid dynamics (CFD) technique. The CFD simulations are carried out with three geometries, one plane vessel and the other two with ribs of square and triangle shape. The results are obtained in terms of velocity, volume fraction distribution, and bed expansion. The effect of other parameters, such as coefficient of restitution, initial bed height, and particle diameter, is also examined. When the particle diameter is large, airflow in an upward direction increases the solid bed height but the flow is almost steady. In the case of small-sized particles, the velocity field and volume fraction profiles for both phases vary with time. In certain regions within the bed, the air becomes concentrated and takes the form of a bubble. The results also show that in fluidized bed units, the unsteady behavior is usually enhanced when ribs are used. The comparison in terms of root mean square values indicates that velocity variation is ~50% more when triangular ribs are used The overall results show that placement of triangle-shaped ribs can be favorable for enhancing momentum and related processes/phenomena in the fluidized bed devices.