In a world grappling with environmental challenges and the need for sustainable manufacturing practices, the convergence of 3D printing and recycling emerges as a promising solution. This research paper explores the potential of combining these two technologies and comprehensively analyses their synergistic effects. The study delves into the printability of recycled materials, evaluating their suitability for 3D printing and comparing their performance with conventional materials. The environmental impact of 3D printing with recycled materials is examined through a sustainability analysis and a life cycle assessment of recycled 3D printed objects. The findings reveal significant benefits, including enhanced resource efficiency, waste reduction, and customisation possibilities. The research also identifies challenges and opportunities for scaling up the use of recycled materials in 3D printing, highlighting the importance of collaboration, innovation, and regulations. With potential applications spanning various industries, from prototyping to construction and healthcare, the implications of this research are far-reaching. By embracing sustainable practices, industry collaboration, and innovation, the integration of 3D printing and recycling can pave the way for a more sustainable future, where resource conservation, circularity, and customised production are at the forefront of manufacturing.

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO₂ emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.