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Multi-objective mixture design of cemented paste backfill using particle swarm optimisation algorithm

机译:使用粒子群优化算法的水泥浆料回填的多目标混合设计

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摘要

In order to achieve a successful cemented paste backfill (CPB) mixture design, multiple project requirements such as strength, flowability and cost should be met. For this achievement, the key design parameters, solid content (SD) and cement percentage (C), should be well adjusted. With increasing the amount of cement in the mixture, CPB strength and production cost increase together, whereas the workability decreases. In order to reduce the cost, more tailings can be added while keeping the cement amount the same but this will reduce both strength and workability. Therefore, CPB design is in fact a multi-objective optimisation problem. In this study, the particle swarm optimisation (PSO) algorithm is used to design CPB mixture meeting multiple objectives. PSO identifies the optimum set of SD and C yielding in desired strength and workability with a minimum cost. The proposed workflow can be a useful and practical for multiple decision making where CPB designers face strength-workability-cog paradox. In addition to reducing the number of trial experiments, the multi objective mixture design of CPB also provides the optimum use of materials to reduce the incurred costs and ensure cleaner and more sustainable production.
机译:为了实现成功的胶泥浆料回填(CPB)混合设计,应满足多种项目要求,如强度,流动性和成本。对于此成就,应良好调整关键设计参数,固体含量(SD)和水泥百分比(C)。随着混合物中的水泥量,CPB强度和生产成本在一起,而可加工性降低。为了降低成本,可以添加更多的尾矿,同时保持水泥量相同,但这将减少强度和可加工性。因此,CPB设计实际上是一种多目标优化问题。在本研究中,粒子群优化(PSO)算法用于设计满足多个目标的CPB混合。 PSO识别最佳的SD和C,产生所需的强度和最低可加工性。所提出的工作流程可以是多重决策的有用而实用的,其中CPB设计人员面临强度 - 作者COG Paradox。除了减少试验实验的数量之外,CPB的多目标混合物设计还提供了最佳使用材料,以降低所产生的成本并确保更清洁,更可持续的生产。

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