Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Abstract

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

Trends in Modeling, Design, and Optimization of Multiphase Systems in Minerals Processing

“…These tools need training algorithms to learn, some of which are shown in Figure 8 (SO and EC). These algorithms, as seen later, can be used for optimizing and designing metallurgical processes [93,97]. Modeling can be divided into data-driven, fault detection and/or diagnosis, and machine vision.…”

“…The latter considers three ingredients: first, a superstructure for representing the alternatives for design, second, a mathematical model for modeling the alternatives for design, included goals, constraints, and objective function, and third, an optimization algorithm [76]. Lucay et al [97] considered a stage superstructure composed of five stages of flotation, which were modeled using a bank model. Here, the single-objective function was of the economic type, and the Tabu-Search algorithm was used for solving the design problem.…”

Intelligent Malware Detection Using a Neural Network Ensemble Based on a Hybrid Search Mechanism

“…The local search procedure implemented in this work is the tabu search metaheuristic, and one of the prominent hallmarks of tabu search is its use of the adaptive memory function to store solutions as the search progresses through various iterations. This is important because it makes the information readily available for decision-making at any point in the course of the search (Lucay, Galvez & Cisternas, 2019). The search can then be strategically directed to promising areas where optimal solutions are most likely to be found, based on search information collected by tabu search.…”

Design of Cell-Based Flotation Circuits under Uncertainty: A Techno-Economic Stochastic Optimization

“…Over the past 40 years, several strategies were implemented for the optimal design of flotation circuits following different technical and financial objectives [8][9][10][11][12][13][14][15][16][17][18][19][20]. Given the conflicting trends between the grade and recovery factors, several researchers have attempted to find the flotation circuit with the optimal levels of global recovery and concentrate grade [8][9][10][11][12][13].…”