Prof. Chongchong Qi, A/Prof Qiusong Chen and other collaborators announce the idea of “Machine-learning aided design for cemented paste backfill”
NEW YORK, Sept. 10, 2019 /PRNewswire/ — Along with other collaborators, especially Prof. Andy Fourie at The University of Western Australia, they proposed the idea of machine-learning aided design for cemented paste backfill (also known as intelligent mining for backfill). A series of papers have been written and published in leading journals like Journal of Cleaner Production, Minerals Engineering, and Powder Technology, to promote this novel idea. Mineral processing tailings (MPT) are inevitable by-products of hard rock mining. They are considered a major source of contaminants to the environment, especially in global arid and semiarid regions. A study has shown that more than 25 billion tons of mineral processing tailings have been produced in China. Poor disposal of the same can be observed in other nations like Australia, Brazil, etc. The right steps must be taken for its disposal as the surface-disposed tailings destroy mining land resources and pose other environmental hazards, which eventually leads to limited cleaner production of the mining industry. Hence, recognized researchers provided a solution. They suggested recycling MPT as cemented paste backfill (CPB), which proved to be an ideal measure for the safe and environmentally friendly disposal of MPT.
CPB is a mine composite material produced using powder tailings, a hydraulic binder, and mixing water. It typically comprises of dewatered tailings (70-85% solids by weight), a hydraulic binder (3-7% by dry paste weight), and mixing water (fresh or mine processed). CPB will cater to the safe disposal of mine tailings. “It eliminates the need for constructing tailing dams at the surface, enabling the waste tailings to be effectively used to fill underground voids.” Other benefits of CPB include reduced surface subsidence and rehabilitation costs. Further, it can provide secondary ground support for mining operations to improve the underground working environment. Therefore, based on the varied advantages of CPB, it is being increasingly used in underground mining as it has technical, economic and environmental benefits. Based on a series of papers, the machine-learning aided design for cemented paste backfill has been validated on three major processes during the application of CPB.
The research paper titled, “Data-driven modelling of the flocculation process on mineral processing tailings treatment” aims to propose a data-driven method for modelling the flocculation process on mineral processing tailings treatment. The model was composed of gradient boosting machine (GBM) and firefly algorithm (FA), in which GBM was used for non-linear relationship modelling whereas FA was used for GBM hyper-parameters tuning. The modelling performance was evaluated and the relative importance of influencing variables was investigated. The supplementary study in this paper showed that there was room for improvement in the predictive performance of the GBM with the chemical characteristics of MPT being considered. Furthermore, the research team is hopeful for more accurate findings and a better representation of the MPT. This could be made possible by using more influencing variables such as particle size distribution (PSD) from laser scattering methods, the chemical compositions and the temperature. In addition to the above, the need for more investigation of other types of flocculants and the dosing method on the flocculation process has been suggested too. Finally, the work is concluded by recommending the use of an enlarged dataset with more influencing variables. Further, a proposal to use more advanced machine learning (ML) algorithms to attain more accuracy has been suggested as well.
View original content to download multimedia:http://www.prnewswire.com/news-releases/prof-chongchong-qi-aprof-qiusong-chen-and-other-collaborators-announce-the-idea-of-machine-learning-aided-design-for-cemented-paste-backfill-300915601.html
SOURCE Prof. Chongchong Qi and A/Prof Qiusong Chen