Abstract
Reducing mineral processing water costs and freshwater consumption is a challenging task in the mineral processing water distribution (MPWD). The work presented in this paper focuses on two aspects of the MPWD optimization model and the MPWD optimization method. To achieve MPWD optimization effectively, a nonlinear constrained multiobjective model is built. The problem is formulated with two objectives of minimizing the mineral processing water costs and maximizing the amount of recycled water. In this paper, an optimization method named enhancing the multiobjective artificial bee colony (EMOABC) algorithm is proposed to solve this model. The EMOABC algorithm uses four strategies to obtain the Pareto-optimal solutions and to achieve the MPWD optimal solutions. With the three benchmark functions, the EMOABC algorithm outperforms the other two widely used algorithms in solving complex multiobjective optimization problems. The EMOABC algorithm is then applied to two cases. Results have shown that the proposed algorithm has the ability to solve the MPWD optimization model. The developed model and the proposed algorithm provide decision support for the actual MPWD problem.
Highlights
Mineral processing is the process of obtaining the raw materials for smelting and consumes large amounts of water
The decision-makers in mineral processing plants need to make decisions concerning different objectives, such as water costs and freshwater minimization, to maximize the comprehensive benefits of mineral processing plants. e solution to the multiobjective optimization problem often results from both an optimization model and an optimization algorithm. us, this paper focuses on solving mineral processing water distribution (MPWD) optimization problems from these two perspectives
MPWD problems are mainly based on manual scheduling. erefore, building MPWD model and applying heuristic methods to solve the model represent an important issue of our current concern
Summary
Mineral processing is the process of obtaining the raw materials for smelting and consumes large amounts of water. Many works have examined the water distribution optimization problem and presented many mathematical models and conventional methods such as linear programming [1, 2], nonlinear programming [3], and integer linear programming [4], which have been applied to solve the problem. These methods suffer from severe limitations in handling discreteness, nonlinearity, complex constraints, and local convergence [5].
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