Wear failure of pipelines transporting cemented paste backfill: A review

Cemented paste backfill (CPB) in metal mining offers substantial advantages in safety, environmental protection, economic viability, and operational efficiency. However, pipeline transport of CPB faces serious challenges due to pipe wear. This article provides a comprehensive review of advances related to pipeline wear. CPB-induced pipeline wear arises from the synergistic interactions of erosion, abrasion, and corrosion. Various wear test apparatuses are recommended for different study scenarios. The effects of particle characteristics, CPB properties, particle-wall contact conditions, and pipe material properties on wear in CPB transport pipelines are summarized. A detailed comparison is conducted of the similarities and differences in the effects of these four categories of factors on the wear of pipelines transporting CPB vs low-concentration slurry. The synergistic effects of abrasion-corrosion and erosion-corrosion interactions contribute significantly to the overall wear process. Methodologies for risk assessment (including machine learning algorithms such as BP neural network and multi-criteria evaluation approaches) and wear rate prediction (e.g., Finnie's erosion model, Archard's abrasion model, and phenomenological models) are outlined. Strategies for pipe wear control are listed in detail. Three specific areas are identified for advancing the understanding of wear mechanisms in CPB transport pipelines. A multifunctional Slurry Pot Tester capable of simulating pipe wear under high-pressure conditions has been developed. Notably, predicting the wear rate of full-flow pipelines based on CPB rheology theory shows substantial potential. A logical framework for wear rate prediction has been established, and two critical challenges associated with this approach have been identified. These insights inform practical applications, including optimized CPB mix designs, strategic pipeline layouts, wear-resistant materials, operational strategies, and real-time wear-risk assessment utilizing machine learning algorithms. This review provides a basis for understanding CPB pipeline wear failure and supports the development of cost-effective, durable backfill systems in mining operations.