Exploring process options to enhance metal dissolution in bioleaching of Indian ocean nodules

Polymetallic Indian Ocean nodules offer a lucrative resource for valuable strategic metals such as Cu, Co and Ni. A novel bioleaching process using cell-free spent growth medium from a fully-grown culture of a marine organism isolated from the nodules (Bacillus M1) dissolved about 45% Co, and 25% Cu and Ni at a the pH of 8.2 in 4 h. To enhance metal dissolution, different modifications in the bioleaching process, such as increasing the pH of the spent growth medium, carrying out leaching in multiple steps, and introducing organic reductant in the leach pulp, were investigated in this study. Increasing the initial pH of the spent growth medium to above 12 resulted in a 25-30% increase in dissolution of Cu, Co and Ni. The pKa value for the spent growth medium was observed to be in the range of 11.5-12.5. UV-visible spectroscopy of the growth medium at pH values above 10.0 suggested a change in the structure of complexing phenolic substances present therein. A four-step leaching process using the spent growth medium, each step lasting for about 4 h, was able to bring around 60% Cu and Ni and 85% Co in solution. About 85% Co, 90% Cu and 60% Ni were dissolved in two-stage leaching, in which the bioleached residue was treated with the spent growth medium from Acidithiobacillus thiooxidans in the second cycle. The effects of concentration of starch (0.1-10%) as an organic reductant to the spent growth medium were also studied. The dissolution of Cu, Co and Ni stabilized at about 80-85% at a starch concentration of 3% and did not increase much thereafter

Software Review

Extend from Imagine That Inc. is simulation software which the company advertises as software for the next millennium. I had not seen this software before, and therefore, was not sure of what to expect from it. But I was pleasantly surprised with its abilities after working with it for a few days. Extend is supplied on a CD, accompanied by a Users Manual which covers various topics such as building a model, enhancing the model and running the model with the blocks provided with the model. It also has extensive discussion on the programing language ModL with which new blocks can be created. Software can run on both Windows as well as Macintosh platforms. The requirements for Windows version are: 486, Pentium or Pentium Pro computer, 8 MB RAM (16 MB recommended), 20 MB hard disk space and Windows 3.1, Windows 95 or above, or Windows NT 3.5+. The requirements for Macintosh are 68020+ or Power Macintosh, 8 MB RAM, 25 MB hard disk space. The installation itself is extremely simple, straightforward and fast. I tried it out on a Pentium MMX 200 MHz/32 Mb with Windows 95, and it took less than 5 minutes

Genetic algorithms with filters for optimal control problems in fed-batch bioreactors

When using a genetic algorithm (GA) to solve optimal control problems that can arise in a fed-batch bioreactor, the most obvious direct approach is to rely on a finite dimensional discretization of the optimal control problem into a nonlinear programming problem.Usually only the control function is discretized, and the continuous control function is approximated by a series of piecewise constant functions. Even though the piecewise discretized controls that the GA produces for the optimal control problem may give good performances, the control policies often show very high activity and differ considerably from those obtained using a continuous optimization strategy. The present study introduces a few filters into a realcoded genetic algorithm as additional operators and investigates the smoothing capabilities of the filters employed. It is observed that inclusion of a filter Significantly smoothens the optimal control profile and often encourages the convergence of the algorithm. The applicability of the technique is illustrated by solving two previously reported optimal control problems in fed-batch bioreactors that are known to have singular arcs

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved

ANNSA: a hybrid artificial neural network/simulated annealing algorithm for optimal control problems

This paper introduces a numerical technique for solving nonlinear optimal control problems. The universal function approximation capability of a three-layer feedforward neural network has been combined with a simulated annealing algorithm to develop a simple yet e5cient hybrid optimisation algorithm to determine optimal control proles. The applicability of the technique is illustrated by solving various optimal control problems including multivariable nonlinear problems and free nal time problems. Results obtained for the di6erent case studies considered agree well with those reported in the literature

Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables

The determination of optimal feed rate profiles for fed-batch bioreactors with more than one feed rates is a numerically difficult problem involving multiple singular control variables. A solution strategy based on genetic algorithm approach for the determination of optimal substrate feeding policies for fed-batch bioreactors with multi-control variables is proposed. The multiplier updating method is introduced in the proposed method to handle inequality constraints on state variables. The efficiency of the algorithm is demonstrated for two case studies on fed-batch bioreactors with two control variables taken from literature. The control policies obtained retain the characteristics of the profiles generated through rigorous application of control theory

Scientometric analysis of chemical engineering publications

The objective of this work was to analyse the scientometric parameters for chemical engineering publications. We have compared the number of journal publications and citations by various countries and institutions. The publication record in terms of quantitative aspects of the number of publications from China has increased exponentially over the last decade and has overtaken USA.However, the citation analysis indicates that there is ample scope for improvement. Thus, USA continues to maintain its leadership position with regard to impact in the field. Analysis of the output of selected Indian universities/organizations against that of the top universities in the world, indicated that the records of top institutions from India are not comparable to the best universities in USA, but are comparable to the best in Asia and are significantly better than the best universities in China

Optimal design of multiproduct batch chemical plant using NSGA-II

The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective function

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm.

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems