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With the production of a large amount of metal tailings, backfill is one of the best models for waste management and has been widely used in many mines. Metal deposit, especially lead-zinc mines, usually contain sulfide minerals, mainly pyrite. Sulfide attack would cause the strength degradation of cemented materials and decrease the structural stability. This study aimed to understand the effect of sulfide on the long-term strength of lead-zinc tailings cemented paste backfill (CPB). CPB specimens with four sulfur contents (2 wt%, 5 wt%, 10 wt%, 15 wt%) were prepared to investigate their physicochemical characteristics in 28–360 days. Scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) analyses of the CPB specimens were also conducted to obtain valuable information. The results showed that the highest strength of CPB was achieved at curing 90 days. Sulfide showed early strength agent property and the early strength of CPB with sulfur content of 5 wt% was improved. The internal system of CPB was alkaline, and sulfate mainly involved in cement hydration reaction. SEM observed that CPB specimens had compact structures, and calcium silicate hydrate (C-S-H) was produced. After 90 days, under the influence of sulfide, the strength of all the CPB specimens decreased, and the strength of CPB specimen with the highest sulfur content decreased the most. The pH value of the solution continuously decreased and eventually became acidic. A large amount of sulfate accumulated. The hydrated calcium silicate decomposed in the acidic environment. The number of expansive ettringite and gypsum products increased, and the number of large pores increased. Sulfide reactivity includes sulfate and acid attack. Large internal defects lead the strength and stability deterioration. This study compared the differences between sulfide erosion and sulfate attack, then provided some advice for the disposal of high sulfide tailings.

The microeconomic framework for data mining assumes that an enterprise chooses a decision maximizing the overall utility over all customers. In item selection problem, the store wants to select J item set S that maximizes the overall profit. Based on the microeconomic view, we propose a novel algorithm ItemRank to solve the problem of item selection with the consideration of cross-selling effect which has two major contributions. First, we propose customer behavior model, and demonstrate it with the data of customer-oriented business. Second, we propose the novel algorithm ItemRank which is implemented on the basis of customer behavior model. According to the cross-selling effect and the self-profit of items, ItemRank algorithm could solve the problem of item order objectively and mechanically. We conduct detailed experiments to evaluate our proposed algorithm and experiment results confirm that the new methods have an excellent ability for profit mining and the performance meets the condition which requires better quality and efficiency