In energy scarcity, particularly in Agri-based developing economies, bio-coal briquetting is the most suitable means of meeting sustainable energy needs utilizing agricultural waste. In this study, briquettes were made from an indigenously designed briquetting machine for investigating coal–biomass proportion blend using coal from Dara Adam Khel, Khyber Pakhtunkhwa in Pakistan, and pomegranate/olive waste to analyze their resulting calorific value, strength, and geochemical properties. A central composite design (CCD) and response surface methodology (RSM) were employed to design the experiments and analyze the data. Regression models were developed for each biomass type, demonstrating their adequacy and reliability for further analysis and prediction. Energy Dispersive X-ray Spectroscopy (EDX) analysis provided insights into the elemental composition of the coal briquettes. Mechanical analysis was performed to assess the strength of the briquettes, with varying compositions showing different levels of strength. Optimization using a composite desirability function yielded an optimal calorific value of 6728 kJ/kg. In summary, this study underscores the viability of bio-coal briquetting as a transformative solution to alleviate energy scarcity. Repurposing agricultural waste curtails resource depletion while mitigating waste disposal challenges. The optimized calorific value aligns with eco-friendly energy initiatives, advancing a greener path toward energy security.

The textile industry generates large volumes of dye-contaminated wastewater, posing significant environmental risks. This study aims to optimize the adsorption of textile dyes from wastewater using activated carbon through a general factorial design. The research investigates the effects of key variables, including activated carbon type (powdered and granular), contact time, carbon dosage, salinity, and temperature, on dye removal efficiency. A comprehensive analysis using ANOVA identifies the most significant factors and their interactions. The study also integrates machine learning models, such as Gaussian process regression (GPR) and artificial neural networks (ANN), to predict adsorption outcomes. The results demonstrate that granular activated carbon (GAC) and lower salinity significantly enhance dye removal efficiency. ANN models outperformed GPR in predictive accuracy, achieving an R ² value of 0.85 compared to GPR’s 0.71. The findings provide optimized operational parameters that can be applied in the textile industry for efficient wastewater treatment and environmental compliance.

The growing emphasis on sustainability and circular economy has driven extensive efforts in recycling and reusing polymer materials, widely used across various industries. While significant progress has been made in recycling polypropylene (PP), the development of polypropylene-ortho (PP-ortho) composite for prosthetics and orthotics remains underexplored. This study focuses on recycling PP-ortho waste by adding wood husk (WH) as a filler material for making PP-ortho composites for manufacturing assistive parts. Statistical analyses were performed considering PP, wood husk and particle size (PS) as input parameters to investigate the mechanical, thermal, and structural properties of the wood husk-PP composite. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the thermal stability of the composite. The results showed that mechanical properties improved with reduced wood husk content and smaller particle sizes, with optimal conditions achieved at 90 wt% PP, 10 wt% wood husks, and a particle size of 125 μm. Thermal analysis indicated a slight reduction in thermal stability with increased wood husk content. The scanning electron microscopy (SEM) further confirmed that higher wood husk content and larger particle sizes led to weaker interfacial adhesion, fiber pullout, and reduced mechanical performance. This research advances sustainable recycling of polypropylene by developing composites for assistive parts in physical rehabilitation, bridging material science with impactful healthcare solutions.

The growing emphasis on sustainability has driven research toward the recycling of polypropylene (PP) waste reinforced with natural fillers. This study investigates the development of rice husk–reinforced recycled polypropylene (RH-PP) composites for assistive applications using a cost-effective and scalable injection molding process. Nine composite formulations were developed by varying RH content (10–30 wt%) and particle sizes (125–500 µm), and their mechanical, thermal, chemical, and morphological properties were characterized. The optimal composite formulation (80 wt% PP, 20 wt% RH, 125 µm) exhibited enhanced performance with compressive strength of 57.3 MPa, bending strength of 45.3 MPa, tensile strength of 49.2 MPa, and impact energy of 4.2 KJ/mm ² . Thermogravimetric analysis (TGA) showed a maximum degradation temperature of 407.6°C, while differential scanning calorimetry (DSC) revealed crystallinity values ranging from 38.8% to 40.7%. Scanning electron microscopy (SEM) confirmed improved interfacial adhesion and uniform filler dispersion. The integration of Grey Relational Analysis (GRA) enabled multi-response optimization, identifying the most influential parameters. The findings demonstrate that RH-PP composites offer a sustainable and mechanically robust alternative to virgin PP for use in orthotic and prosthetic applications.