Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies

Abstract

Data-driven models are constructed for leaching processes of various low grade manganese resources using various nature inspired strategies based upon genetic algorithms, neural networks, and genetic programming and subjected to a bi-objective Pareto optimization, once again using several evolutionary approaches. Both commercially available software and in-house codes were used for this purpose and were pitted against each other. The results led to an optimum trade-off between maximizing the recovery, which is a profit oriented requirement, along with a minimization of the acid consumption, which addresses the environmental concerns. The results led to a very complex scenario, often with different trends shown by the different methods, which were systematically analyzed.

Keywords:

• Evolutionary algorithm
• Genetic algorithms
• Genetic programming
• Leaching
• Manganese
• Multiobjective optimization
• Ocean nodules
• Optimization
• Pareto frontier

ACKNOWLEDGMENT

AB, NC, PKS, acknowledge financial support from the Ministry of Earth Sciences, India. AB also acknowledges financial support from the Science and Technology Service of French Embassy in India through the Sandwich Ph.D./Post-doc scholarship program.