Manufacturing and municipal waste are soaring at an unprecedented rate posing an environmental threat to the global industrial sector. Conventional forms of linear disposal, landfills and incineration are neither sustainable environmentally nor economically. Even though sustainability is a pressing need, there are still major gaps in infrastructure and financial constraints. Moreover, there is still a severe gap in the comprehensive implementation of global programs on zero-waste, as the available literature does not have systematic systems to coordinate technological innovations and the over-corporate plans. The aim of the research is to explore the way in which sustainable waste management strategies can be effectively embedded in manufacturing processes to have zero-waste target and to determine the multi-level compatibility of these activities with long-term corporate strategies. Mixed methods, sequential exploratory methods, were used. The study organized information around the world with the help of a systematic literature review, studied various industrial case studies to practically prove the validity of economic and regulatory obstacles, and applied semi-structured expert interviews. Results indicated that current systems were not congruent with the principles of zero-waste, with 37% of all waste in the world kept in landfills, and operational costs of waste management reaching over 100 dollars per ton in high-income countries. Nevertheless, these approaches of incorporating the 3R concept, circular economies, and waste-to-energy indicated that there had been a deep potential of reducing the estimated 3.40 billion tons of global waste by the year 2050. This research ends up developing a holistic strategic integration framework. With the following customized suggestions in mind, manufacturing businesses will be able to sail through systemic challenges easily, diminish their impact on the environment by far, and become the chief drivers of a transition to an environmentally friendly and airtight, zero-waste future.

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.

High levels of energy poverty in emerging nations, educational infrastructure is one of the most sensitive areas that do not give students the opportunity to use digital facilities and means of communication. Traditional off-grid products, which include fossil-fuel generators and disposable batteries, are not economical or environmentally friendly. Moreover, the current hand-cranked devices are either plated into expensive military technology or cheap educational toys, which leave a gap of middle-range, durable solution. This study aims to design, analyze, and fabricate MIL Grow Crank Green, a low-cost (<$25), gear-driven, hand-cranked generator toolkit that encourages students to learn about energy and media literacy (MIL) while producing renewable electricity. SolidWorks CAD Software is used to Design the system optimizing an 80:1 compound gear train to analyze mechanical stress. A prototype permanent magnet alternator (PMA) is to be constructed and experimented with regarding voltage regulation, power output, and efficiency at varying resistive loads (10 ? -100 ?). The linear response of voltage (R2 =0.99) in operation is proven through experimental validation which provides a constant 3v-12v DC output. The system demonstrated a peak power output of 3.5W at a nominal load of 20?, with a mechanical-to-electrical efficiency of about 88% and managed to work and power LED arrays and mobile devices. The suggested model is an effective replacement to single-use batteries which are sustainable and strong. It enhances UN Sustainable Development Goals 4 (Quality Education) and 7 (Clean Energy) by bridging mechanical transparency to functional utility to enable off-grid communities to be energy literate.

Hybrid solar–thermal energy harvesting systems are an important advancement in renewable energy technology, enabling simultaneous production of electrical power and useful thermal energy within a single compact platform. However, their performance is often limited by poor heat transfer and inefficient mass transport of working fluids, leading to photovoltaic thermal degradation and significant exergy losses. This study aims to optimize heat and mass transport processes in a hybrid solar–thermal mechanical system to enhance energy recovery and ensure long-term operational reliability. A three-dimensional numerical model based on the finite volume method (FVM) was developed using the governing equations of continuity, momentum, and energy conservation. A Multi-Objective Genetic Algorithm (MOGA) was employed to determine optimal microchannel geometries by analyzing variable cross-section effects on flow behavior and thermal boundary layer disruption. At the Reynolds number of 2000, the optimized configuration increases the average Nusselt number by 43.5% compared to a smooth channel. Consequently, the photovoltaic operating temperature decreases by 12.6 °C, improving electrical efficiency by 9.3%. The system achieves a maximum thermal efficiency and net energy gain of 76.8%, while maintaining an acceptable 16.3% increase in pumping power. The results confirm that optimizing mass transport is essential for effective thermal regulation and improved energy conversion performance, providing a strong foundation for high-efficiency solar collector design.