A hybrid evolutionary performance improvement procedure for optimisation of continuous variable discharge concentrators

Date

2015-05-05

Author

Sakuhuni, Givemore
Klein, Bern
Altun, Naci Emre

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

276
views

0
downloads

An iterative hybrid performance improvement approach integrating artificial neural network modelling and Pareto genetic algorithm optimisation was developed and tested. The optimisation procedure, code named NNREGA, was tested for tuning pilot scale Continuous Variable Discharge Concentrator (CVD) in order to simultaneously maximise recovery and upgrade ratio of gold bearing sulphides from a polymetallic massive sulphide ore. For the tests the CVD was retrofitted during normal operation on the flotation tailings stream. On the basis of mineralogical data showing strong pyrite-gold association in the flotation tailings, iron assays were used as an indicator of CVD performance on recovery of gold bearing sulphides. Initially, 17 pilot scale statistically designed tests were conducted to assess metallurgical performance. The Matlab 2010a software was used to train and simulate back propagation ANNs on experimental results. Regression models developed from simulation data were used to formulate the objective functions used to optimise the CVD using the NSGA-II genetic algorithm. Results show that the NNREGA procedure provides an efficient way of exploring the design space to learn the relationship between interacting variables and outputs and is capable of generating the operating line, which is a non-dominated recovery/grade line. The paper forms a basis for future work aiming to model and scale up processing equipment.

Subject Keywords

Artificial neural networks, Optimisation, Genetic algorithm, Continuous Variable Discharge Concentrator (CVD)

URI

https://hdl.handle.net/11511/43301

Journal

SEPARATION AND PURIFICATION TECHNOLOGY

DOI

https://doi.org/10.1016/j.seppur.2015.02.030

Collections

Department of Mining Engineering, Article

Suggestions

OpenMETU
Core

A parallel ant colony optimization algorithm based on crossover operation Kalınlı, Adem; Sarıkoç, Fatih (Springer, 2018-11-01) In this work, we introduce a new parallel ant colony optimization algorithm based on an ant metaphor and the crossover operator from genetic algorithms.The performance of the proposed model is evaluated usingwell-known numerical test problems and then it is applied to train recurrent neural networks to identify linear and nonlinear dynamic plants. The simulation results are compared with results using other algorithms.
Advanced Models for Predicting Aggregate Rutting Behavior Ceylan, Halil; Güçlü, Alper; Tutumluer, Erol; Pekcan, Onur (null; 2005-06-29) Artificial neural network (ANN) based advanced aggregate rutting models have been developed and compared for performance using laboratory test data. The primary goal has been to properly characterize the loading stress path dependent permanent deformation behavior from advanced repeated load triaxial tests that can simulate in the laboratory the varying stress states under actual moving wheel load conditions. The aggregate specimens tested were the Federal Aviation Administration (FAA) specified P209 base a...
A Novel Neural Network Method for Direction of Arrival Estimation with Uniform Cylindrical 12-Element Microstrip Patch Array Caylar, Selcuk; Dural, Guelbin; Leblebicioğlu, Mehmet Kemal (2008-01-01) In this study a new neural network algorithm is proposed for real time multiple source tracking problem with cylindrical patch antenna array based on a previous v reported Modified Neural Multiple Source Tracking Algorithm(MN-MUST). The proposed algorithm, namely Cylindrical Microstrip Patch Array Modified Neural Multiple Source Tracking Algorithm (CMN-MUST) implements W-MUST algorithm on a cylindrical microsttip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip...
A Meta-Heuristic Paradigm for solving the Forward Kinematics of 6-6 General Parallel Manipulator Chandra, Rohitash; Frean, Marcus; Rolland, Luc (2009-12-18) The forward kinematics of the general Gough platform, namely the 6-6 parallel manipulator is solved using hybrid meta-heuristic techniques in which the simulated annealing algorithm replaces the mutation operator in a genetic algorithm. The results are compared with the standard simulated annealing and genetic algorithm. It shows that the standard simulated annealing algorithm outperforms standard genetic algorithm in terms of computation time and overall accuracy of the solution on this problem. However, t...
An interactive preference based multiobjective evolutionary algorithm for the clustering problem Demirtaş, Kerem; Özdemirel, Nur Evin; Karasakal, Esra; Department of Industrial Engineering (2011) We propose an interactive preference-based evolutionary algorithm for the clustering problem. The problem is highly combinatorial and referred to as NP-Hard in the literature. The goal of the problem is putting similar items in the same cluster and dissimilar items into different clusters according to a certain similarity measure, while maintaining some internal objectives such as compactness, connectivity or spatial separation. However, using one of these objectives is often not sufficient to detect differ...

Citation Formats

G. Sakuhuni, B. Klein, and N. E. Altun, “A hybrid evolutionary performance improvement procedure for optimisation of continuous variable discharge concentrators,” SEPARATION AND PURIFICATION TECHNOLOGY, pp. 130–138, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43301.