The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m³, 27.89 µg/m³, 26.17 µg/m³, and 27.24 µg/m³, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

The automobile instrument is the indispensable item that is essential to keep the driver conversant of the process of the engineer and the other system. There are different parameters involved in the automobile instrument, which requires a different process involved in creating the automobile parameter. This research is designed to show the simulation development of injection molding and optimization of automobile instrument parameters using different optimization techniques. In this paper, Taguchi Orthogonal parameter design and particle swarm optimization (PSO) are used to measure the shrinkage volume rate and warpage amount rate. Most of the studies are designed to calculate the reduction and warpage for the gate location and glass material. The Taguchi orthogonal design contains five different parameters which are inversely proportional to each other. Hence, by optimizing those parameters, the injection molding procedure is passed out. In this research, the paper is designed to process the injection molding for the automobile instrument parameter using optimization techniques.

Nanoparticles are said to be active particles which are entrapped in the surface of the polymeric core. Since nanoparticles were used in medical and biotechnological fields, there is a great demand in the preparation of nanoparticles. Nanoparticles are prepared from different substances; mainly, polymer material is used in the field of preparing nanomaterials. There are different methods involved in the preparation of nanoparticles from the polymer. Various experiments and research studies were carried out on the basic preparation of nanoparticles. Emulsion polymerization could be used to make polymeric nanoparticles with a high solid concentration without the need of surfactants. To make carboxylate polystyrene beads or amidine polystyrene nanoparticles, polymeric nanocolloids containing surface functional groups were produced. In this research, the preparation of nanoparticles from emulsion polymerization is represented along with the size and distribution material.

Pyrolysis is the most important thermochemical process that can be used for the production of biofuel, from wood and wood-based lignocellulosic materials. In this study, bio-oil is produced from the bio-weed named Ficus religiosa by the thermal pyrolysis process by utilizing laboratory-scale fluidized bed reactor. This study deals with the production of maximum bio-oil by optimizing process parameters such as process temperature, particle size, and sweep gas flow rate. Further different analytical techniques were used to describe the properties of bio-oil for different applications. Wood and wood barks of Ficus religiosa were chosen as the raw material due to their higher volatile content (72.4). The maximum yield of 47.5 wt bio-oil was collected at the optimized operating conditions of 450°C temperature, 1.0mm particle size, and 2.0m³/h sweep gas flow rate. Compared with other operating parameters, temperature is observed as the most significant one to determine the product yield. Through chromatographic analysis, it was identified that the bio-oil is found with the variety of chemical compounds including alcohols, alkenes, phenols, saturated fatty acids, and esters.

Plastic is a resilient, chemically inert, lightweight, and low‐cost material. It sticks around in the environment for more than hundred years, threatening nature and spreading toxins. The current study deals with the use of waste polymeric materials and de oiled cake for the production of liquid oil and its blend on the performance and emission characteristics of diesel engine. The tests were conducted in an engine fuelled with diesel and four distinct blends such as 5% (B5), 10% (B10), 15% (B15), and 20% (B20), respectively. The liquid oil was produced by co‐pyrolysis of neem de oiled cake (NDC) and waste polystyrene (WPS) in 1 : 2 blend ratio. The raw pyrolysis oil and its different blends were tested for their physical and chemical characteristics in order to find their suitability. Brake power (BP), brake thermal efficiency (BTE), brake‐specific fuel consumption (BSFC), emissions of carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) are used to assess the performance of the engine. The experimental results reveal that BTE at all blends is lower than diesel at all loads and the BSFC increases with increasing blend ratio and falls with increasing engine load. At higher loads, the deviation of performance and emission values from baseline diesel up to B10 is very small. It is found from the results that the liquid oil derived from NDC and WPS up to 10% blend will be the promising additive for fossil fuels.

AA5052 bead‐on‐plate processing has been achieved by the friction stir processing (FSP) technique to examine the manipulation of process parameters. It also improved the base metal surfaces to analyze the microstructure. The tool spinning speed, traverse speed, and axial load were preferred to investigate the effect of friction stir bead‐on‐plate processing on the tensile strength qualities and microhardness in AA5052. An optical microscope was used to dissect the fabricated processed zones of the microstructure. By using the design of the experiment, the orthogonal array of the L9 Taguchi method was used to construct the processing experiments. The analysis of variance and the signal‐to‐noise ratio methods were employed to identify the optimum unification of process parameters and the significant benefaction of a specific parameter on the responses. The outcomes showed that the tool spinning speed was the principal factor affecting the characteristics of tensile strength and microhardness, succeeded by the traverse speed and axial load. The intermetallic compound layer had formed during the processing under specified conditions. This examination revealed that the optimum parameters could intensify the mechanical behaviour of AA5052.