One of the biggest issues impeding a country’s progress is the lack of power. To overcome this issue, hybrid renewable energy systems (HRES) play an important role. Due to rising consumption and diminishing resources globally, sustainability has recently attracted more attention. Bangladesh has access to renewable energy sources, including solar, micro-hydro, biomass, wind, and others. The objective of this research is to minimize the net present cost (NPC), cost of energy (COE), and CO₂ emissions of the suggested electricity network using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) Pro Software. This investigation explores the possible use of a hybridized energy system (i.e., solar, wind, and diesel) with battery storage in Bangladesh’s northern area. Utilizing HOMER Pro software, an optimal grid-connected system is chosen after evaluating the techno-economic viability of several configuration options. For the Rangpur metropolitan region, seven distinct grid-connected solutions with stationary renewable sources are simulated. The HRES is designed to meet demands for hospital, diagnostic, school, and operation theatre loads of 3250.00 kWh, 250.00 kW maximum requirement, and 570.00 kWh, 71.25 kW maximum electricity demand, respectively. Multivariate linear regression (MLR) is used to assess the suggested optimal combination in terms of system size, cost, technical performance, and environmental stability. The findings show that the metric real-time rate (annual) has emerged as the most advantageous option since economic criteria like total NPC and COE are preferred above others.

Researchers and engineers are actively working on enhancing the efficiency of heat exchangers in engineering applications by developing novel designs, exploring new materials, and utilizing nanofluids. Three kinds of nanofluids with varying concentrations are investigated in this paper. The objective is to assess the performance of N‐shaped double‐pipe heat exchanger used in thermoelectric power plants. The performance has been evaluated using COMSOL Multiphysics software. The findings show that higher nanofluid concentrations resulted in elevated heat transfer coefficients and improved efficiency of N‐shape double pipe heat exchanger. The analysis revealed that a mere 1% rise in the volume fraction of nanofluids enhanced the efficiency of the heat exchanger on average 23% when compared to the base fluid (water). In comparison to the N‐shape double pipe (Inconel 625) heat exchangers, the N‐shape double pipe (copper) heat exchangers appear to be more efficient. The introduction of nanoparticles has a notable impact on the heat transfer coefficients. Specifically, within an N‐shaped double pipe (copper) heat exchanger, the inclusion of a 1% volume fraction results in a 2.09% enhancement in the heat transfer coefficient for Al₂O₃/water, a 1.3% improvement for Fe₃O₄/water, and a 1.15% increase for TiO₂/water. It also exposed that adding 1% Al₂O₃/water led to a significant 0.623% increase in effectiveness, while TiO₂/water showed a 0.259% rise, and Fe₃O₄/water exhibited a 0.375% improvement. Moreover, increasing the Reynolds number enhances the Nusselt number for Al₂O₃/water and Fe₃O₄/water nanofluids by 55.22%, and for TiO₂/water by 54.60% at a 6% volume concentration, leading to additional increases in exchanger efficiency. Therefore, the augmentation in nanofluid concentration leads to a reduction in the temperature pinch points both at the intake and outflow. This observation suggests that nanofluids exhibit a superior ability compared to conventional fluids when it comes to effectively lowering temperatures.

Industrial waste, leftover materials and chemical residues constitute a major environmental challenge in Bangladesh where the textile industry annually produces some 400,000 tons of fabric waste that is a significant source of pollution. Manufacturing 4.0 technologies are making possible advanced manufacturing systems that can optimize production and reduce waste, using technologies such as those supported on data analytics and the Internet of Things (IoT). The objective of this study is to build machine-learning based predictive analytics framework for minimizing textile production waste, evaluate the developed framework using a practical context in Bangladesh and finally observe the environmental and socio-economic impact caused by the approach. The design employed a mixed-methods case study. Data were collected from a medium-sized textile dye-house in Dhaka from January to March 2025, with IoT tracked measurements on fabric consumption, machine productivity, and wastewater output (n = 1,000 production cycles). Python generates a Random Forest regression model to predict waste, while simulation is carried out through a digital twin to optimize production parameters. The model obtained a mean absolute error of 5.4% and was able to accurately predict the pattern of waste. Application of the optimized parameters resulted in 20% less fabric waste (from 500 to 400 kg/day), 15% less use of water in dyeing (from 10,000 to 8,500 liters/day) and 10% lower CO₂ emission (0.5 tons/day). The greatest waste reduction was observed in the urban area, due to better cutting techniques. These findings highlight the opportunities provided by Industry 4.0 analytics for sustainable manufacturing towards UN SDG 12. Additional investigation is also required on low-cost IoT deployment and policy enablers, to achieve widespread adoption and impactful change sustainably in developing economies.