Showing papers by "Nirupam Chakraborti published in 2009"

Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms

Abstract: Existing acid leaching data for low-grade manganese ores are modeled using an evolving neural net. Three distinct cases of leaching in the presence of glucose, sucrose and lactose have been considered and the results compared with an existing analytical model. The neural models are then subjected to bi-objective optimization, using a predator–prey genetic algorithm, maximizing recovery in tandem with a minimization of the acid concentration. The resulting Pareto frontiers are analyzed and discussed.

Identification and Optimization of AB2 Phases Using Principal Component Analysis, Evolutionary Neural Nets, and Multiobjective Genetic Algorithms

Abstract: Available data for a large number of AB2 compounds were subjected to a rigorous study using a combination of Principal Component Analysis (PCA) technique, multiobjective genetic algorithms, and neural networks that evolved through genetic algorithms. The identification of various phases and phase-groups were very successfully done using a decision tree approach. Since the variable hyperspaces for the different phases were highly intersecting in nature, a cumulative probability index was defined for the formation of individual compounds, which was maximized along with Pauling's electronegativity difference. The resulting Pareto-frontiers provided further insight into the nature of bonding prevailing in these compounds.

A genetic algorithms based multi-objective optimization approach applied to a hydrometallurgical circuit for ocean nodules

Abstract: Several hydrometallurgical processes have been studied for the extraction of metals from lean ores utilizing various flow sheet options. Of particular significance is the grade of the ore being treated, the energy consumed and associated costs, options for byproduct recovery, and the relative price of the products. A process scheme needs to be optimized for simultaneously maximizing metal throughput and minimizing the direct operating costs incurred within constraints set for the operating variables. This leads to a multi-objective optimization problem. The range of input grades for raw material, which a flowsheet can handle, needs to be worked out based on an optimization exercise. A lean manganese-bearing resource such as polymetallic sea nodules has been chosen in this article for the development of an optimization approach based on which the input raw nodules grades are to be treated by a particular flowsheet. Only the chemical consumption costs have been adopted in this article as a measure of direct...

Designing Ionic Materials Through Multiobjective Genetic Algorithms

Abstract: The present work deals with the design of ionic materials as an “inverse problem” where we determine suitable interionic distance to arrive at the desired properties. Specifically, we design ionic materials with high fracture toughness, low density, and high thermodynamic stability. Fracture toughness of the material is determined through molecular dynamics simulations, and the three conflicting objectives are optimized using multiobjective Genetic Algorithms. Two typical lattice systems, namely, the NaCl (B1) structure and the CsCl (B2) structure, are studied. The interionic potential is modeled by a combination of Born–Mayer and Coulomb potentials which represent the electron orbital repulsion and unlike ion attraction, respectively. Attempt has been made to develop a general framework for the design of ionic materials by Genetic Algorithms.

Multi-Objective Materials Design by Genetic Algorithms—Generalized for B1 and B2 Ionic Structures

Abstract: The design of NaCl(B1) and CsCl(B2) lattices are carried out in this work using multi-objective genetic algorithms, generalizing it for all the compounds possessing B1 and B2 crystal lattices. Three objectives, fracture toughness, density and energy of the system, were taken into the purview of this investigation, where the first objective is maximized and the remaining two are minimized. The interionic potential model used is a combination of Born Mayer and Coulomb potentials. The objectives are computed as a function of the ionic masses along with the inter-ionic distance. The fracture toughness studies are carried out by molecular dynamics studies (in which dependence on loading rate, crack size and electronegativity are also studied) and multi-objective optimization problem is solved by the well known multi-objective optimization algorithm NSGA II.

Use of Process Optimization And Cost Model For Metal Recovery From Manganese Nodules:The Role of Manganese Recovery

Abstract: Little attention has been paid during process development studies to possible changes in cost of metal production when a process plant is exposed to varying degrees of metal grades emanating from a sea nodules mining area. Study of these aspects requires process optimization. An approach for optimization of a process technology at a given throughput of nodules has been developed. This uses a robust multi-objective process optimization technique coupled with process simulation and indicative cost economics. A case study illustrates the approach; minimization of process chemical cost with simultaneous maximization of metal values produced is considered. The approach may easily be extended to include other objectives, including energy, for any process technology. laboratory scale studies were conducted leading to solubilization of all metals including manganese. Other potentially novel reagents for manganese nodules have been reviewed by Mukherjee et.al. (2004). Whereas these studies were directed at improving the performance of sulfuric acid leaching, the results cannot be analyzed from the point of view of flow sheet development and subsequent process design. A major challenge in process design is to assess the viability of processing such a widely varying grade of an oceanic low grade manganese resource for recovery of manganese and other valuable metals. Any process scheme developed for multi metal recovery would need to be assessed from the angle of sensitivity of the proposed scheme to metal throughput and direct chemical costs incurred with input ore grade variation. As any developed process scheme would have certain constraints with regard to the operating variables, the proposed scheme could be optimal only for a certain range of input grades of the raw materials. Chemical composition of these nodules changes from location to location across the vast sea bed. Manganese, which is present as manganese dioxide, is the major metallic component, varying over a wide range of 17 % to 30 %. Other associated metals present in the nodule matrix are of lower proportions in comparison to manganese. The composition of medium grade sea nodules on average vary between Mn: 17 - 28%, Cu: 0.5 - 1.3%, Ni: 0.5 - 1.3% and Co: 0.1- 0.26%. Higher grade sea nodules, as is typically displayed for Pacific sea nodule, have the range of Mn: 24-30%, Cu: 0.8-1.4%, Ni: 0.8-1.4% and Co: 0.16%-0.26%. The present paper brings into focus the methodology for choosing an appropriate ore grade range for a given process flow sheet. As any developed process scheme would have certain constraints with regard to the operating variables, the proposed scheme could be optimal only for a certain range of input grades of the raw materials. Use of a process optimization strategy would be a vital requirement for that. Pareto optimal solutions can be developed and appropriate decisions regarding the varying grades of raw material to be used for a given flow sheet can then be arrived at. Only use of different optimal solutions, however, may not permit choice of the input grade of nodules; indicative profitability for the different input grades for a given flow sheet under known recovery conditions would be useful to arrive at decisions with respect to ore grades. Thus, the present paper would be dealing additionally with the development of an indicative cost model.

How multi-objective genetic algorithms handle lack of data, sparse data and excess data: evaluation of some recent case studies in the materials domain

Abstract: An overview of multi-objective optimization and the associated concept of Pareto-optimality are elucidated in detail, keeping the biologically inspired genetic algorithms in perspective. The effective role of the genetic algorithms in handling three different kinds of data driven models where the decision has to be made from (i) no data (ii) excess data or (iii) sparse data are elaborated through three materials engineering applications, where other strategies like inverse modeling, neural network and data mining have worked in tandem with the multi-objective genetic algorithms. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 000-000, 2009