Strategic load management: Enhancing eco-efficiency in mining operations through automated technologies

Abstract

In the face of escalating global demands for sustainable practices within the construction and mining sectors, this paper investigates the transformative impact of automated load analysis technologies. Focused on bridging the gap between traditional operational methodologies and the forefront of automation technology, the study provides an in-depth examination of the integration of onboard weighing systems, the Internet of Things (IoT), and machine learning into mining operations. Through a series of detailed case studies, the research showcases how these technological innovations contribute to substantial improvements in operational efficiency, notably through enhanced load management, reduced fuel consumption, and optimized resource allocation, thereby fostering a decrease in the environmental footprint of mining activities. Furthermore, the paper addresses critical sustainability issues, including workforce transformation, stakeholder engagement, and the broader environmental implications of adopting automated technologies in mining processes. Concluding with strategic policy recommendations, the study advocates for widespread adoption of automated systems within the construction sector to achieve improved environmental and economic outcomes. By emphasizing a multidisciplinary approach, this research highlights the essential role of technological innovation in aligning mining operations with sustainable development goals, positioning automated load analysis as a pivotal strategy for advancing eco-efficiency in the construction and mining industries. • Innovates mining operations with IoT and machine learning for eco-efficiency. • Demonstrates fuel savings and operational efficiency via automated analysis. • Enhances mining safety and reduces environmental impact through technology. • Advocates for policy changes supporting sustainable mining practices. • Provides a framework for industry-wide adoption of automated load analysis.