The research work, “An intelligent modelling framework for mechanical properties of cemented paste backfill” proposed an intelligent modelling framework for the mechanical properties prediction of cemented paste backfill (CPB).

Research work in the past was observed and it was seen that only a small number of studies have used machine learning (ML) algorithms to estimate the mechanical properties of CPB. For example, a study has proposed the uniaxial compressive strength (UCS) prediction model for CPB using boosted regression trees and particle swarm optimization. Although such studies have shown great promise, some shortcomings cannot be neglected. To compensate for the limitations in the literature, an intelligent modelling framework was proposed to accurately predict the mechanical properties of CPB. This was able to provide a fast estimation of CPB mechanical properties. Further, it reduced the time and cost allocated for the mechanical properties in the minerals industry to a great extent. Experiments were conducted and “based on the results, a user-friendly software package, named the intelligent mining for backfill (IMB) was developed in python programming for a wider application in the minerals industry”. It was also concluded that the recommended modelling framework and the IMB might be of great help for CPB design by saving time, reducing trial tests and cutting costs in the future.

The research study titled, “Pressure drop in pipe flow of cemented paste backfill: Experimental and modelling study” presents a framework for investigating and modelling the pressure drop of the CPB during pipe transportation with complex circuit shapes. The idea behind the study was to accelerate the efficiency of mining operations, which could be made possible if the pressure drop during the pipe transport of CPB can be predicted accurately. The need to design the transportation of fresh CPB from the surface plant to the underground voids was important for the mining industry. After trying multiple transport methods, it was found that the hydraulic transport of CPB through pipes is becoming more and more popular. This was because of the year-round availability, low technical maintenance and its environment-friendly property. More studies revealed the constant need to investigate the pressure drop of fresh CPB during pipe transportation. In the present study, the team of researchers used a test loop system to examine the pressure drop of CPB under the influence of solids content, cement-tailings ratio, inlet pressure, and circuit shape. The pressure drop during the pipe transport of CPB was studied. In addition to this, it signifies “a fundamental change in the process of CPB pipe transportation by teaming researchers and practitioners with gradient boosting regression tree (GBRT) modelling for a reliable prediction of the pressure drop”.

 

The findings indicated that the pressure drop had a positive correlation with the solids content, cement-tailings ratio, and inlet velocity. It was also seen that the GBRT technique had great potential for pressure drop modelling. The combination of both loop test experiments and GBRT modelling proved to be an effective method to determine pressure drop, which could eventually be of monumental significance in engineering applications of pipe transportation. Observations also pointed out that such a modelling approach could provide fruitful benefits and provide the research and practitioner community with a reliable and cost-effective way for analysing the pressure drop during engineering applications. However, the team has suggested continuing the quest for more influencing variables for the pressure drop. Besides, “other advanced AI techniques should be investigated “as well.

The three brilliant research papers, together with other papers about the machine-learning aided design for cemented paste backfill, advocate the potential of artificial intelligence (AI) in the sphere of mineral engineering. Prof. Chongchong, A/Prof. Qiusong and other team members are now calling for international collaborations on this topic and a database project has been initiated.

Moreover, Prof. Chongchong, A/Prof. Qiusong, and other collaborators also proposed innovative findings in recycling solid wastes, such as phosphogypsum, construction demolition waste, different ore tailings, smelting waste, etc., as materials for CPB. Research papers have been written and published in related journals like Journal of Cleaner Production, Construction and Building Materials, and Journal of Environmental Management. They are now running several special issues of SCI journals about CPB materials (details can be obtained upon contact). Detailed information about above contributions can be found at: www.chongchongqi.com, which must be highly appreciated by the research community.

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