The utilization of solid waste in useful product is becoming a great deal of worth for individuals, organizations, and countries themselves. The powder of waste glass and silica fumes are also considered major waste materials across the globe. In this paper, the physico-chemical, thermal, and morphological properties of both waste powders are investigated in order to determine their suitability for use as a partial replacement for cement in basic concrete. They are suitable for use in concrete due to their pozzolanic and other basic properties. Extensive testing, in terms of the compressive strength test, the slump test, and the flexural strength test, has been carried out to study the replacement of cement in the range of 5–15% by waste glass powder for curing ages of 7 and 28 days. The FTIR analyses of both materials are studied for determining the effect of characteristics of chemical bonding and intense bands with bending vibrations of O–Si–O bonds. Experimental results indicate towards the potential utilization of wastes in concrete in terms of green concrete.

In the present work, erosion wear due to slurry mixture flow has been investigated using a slurry erosion pot tester. Erosion tests are conducted on three different slurry pipe materials, namely, mild steel, SS202, and SS304, to establish the influence of rotational speed, concentration, and time period. In order to increase erosion wear resistance, a high-velocity oxy-fuel (HVOF) coating technique is used to deposit a WC-₁₀Co₄Cr coating on the surface of all piping materials. Experimental results show that rotational speed is a highly-influencing parameter for the erosion wear rate as compared to solid concentration, time duration, and weighted mean diameter. WC-₁₀Co₄Cr HVOF coating improved the erosion resistance of piping materials up to 3.5 times. From experimental data, the exponents of solid concentration, velocity, and the size of particles are calculated for the empirical erosion wear equation. A functional equation of the erosion wear rate is developed. The predicted erosion wear is in agreement with the experimental data and found to be within a deviation of ±12%.

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution.