Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel tec...

Genetic algorithms in materials design and processing

Genetic algorithms (GAs) belong to the broader family of generic population based metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the biology inspired computing techniques that emulate the basic Darwinian concept of natural selection. Because of their many positive attributes such as ease of use, versatility and robustness, GAs have become important in materials research very rapidly since their inception. Though going through a major stage of theoretical development, GAs have been successfully applied in many fields such as polymer production, polymer design, polymer processing, polymer laminates and other polymers, as well as non-polymer applications in recent times. The present review attempts to present the state of the art in this area. The review is broadly divided into three sections: fundamentals of GAs, the use of GAs in polymeric materials and the use of GAs in non-polymer applications. First, the fundamentals of GAs are presented to provide some gl...

Genetic algorithms in polymeric material production, design, processing and other applications: a review

A genetic algorithms-based multiobjective optimization study has been carried out for the bottom gas injection process practiced in the steelmaking vessels. Two objective functions, one dealing with the efficacy of mixing and the second one characterizing the stress level at the ladle walls, have been evaluated from a numerical solution of coupled Reynolds averaged transient Navier-Stokes equations, energy and species conservation equations, as well as governing equations for turbulent kinetic energy and its dissipation rate. A Pareto front has been constructed by using the recently proposed Strength-Pareto Evolutionary Algorithm and the corresponding flow fields have been computed by using a pressure-based finite volume methodology. It has been inferred that judicious combinations of the two objective functions can be obtained from the simulation results, leading to an optimal process control.

Gas Injection in Steelmaking Vessels: Coupling a Fluid Dynamic Analysis with a Genetic Algorithms-Based Pareto-Optimality

Genetic algorithms are biologically inspired state of the art computing techniques, which are now very efficiently used in every aspect of ferrous metallurgy. A brief overview is presented highlighting recent research activities with relevance to the steel industry.

Genetic algorithms in these changing steel times

The aim of this work was to develop a prediction model for hot metal desulfurization. More specifically, the study aimed at finding a set of explanatory variables that are mandatory in prediction of the kinetics of the lime-based transitory desulfurization reaction and evolution of the sulfur content in the hot metal. The prediction models were built through multivariable analysis of process data and phenomena-based simulations. The model parameters for the suggested model types are identified by solving multivariable least-squares cost functions with suitable solution strategies. One conclusion we arrived at was that in order to accurately predict the rate of desulfurization, it is necessary to know the particle size distribution of the desulfurization reagent. It was also observed that a genetic algorithm can be successfully applied in numerical parameter identification of the proposed model type. It was found that even a very simplistic parameterized expression for the first-order rate constant provides more accurate prediction for the end content of sulfur compared to more complex models, if the data set applied for the modeling contains the adequate information.

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Data-Driven Mathematical Modeling of the Effect of Particle Size Distribution on the Transitory Reaction Kinetics of Hot Metal Desulfurization

The AOD process of making stainless steel is very popular because of its fast rate of decarburization and excellent control of composition and temperature. Both static and dynamic models are available for process control. The objective of the present study is to demonstrate the applicability of GA in adapting a process control model to a particular plant environment and subsequently optimize the blowing scheme to reduce overall costs. Deviation of the predicted results of the process control model, from the actual plant data, is first minimized by adjusting the chemical reaction rate parameters and heat balance equation. It is found that the adaptation of the process control model to an actual situation is feasible only below 0.8% carbon. After adapting the model, optimization is done through GA wherein the objective function contains the cost of refractory, process time, chromium loss etc. It is thus possible to devise a blowing scheme to arrive at the desired end point composition and temperatu...

Process Control and Optimization of the AOD Process Using Genetic Algorithm

Bioaerosols have gained importance in atmospheric sciences in the past few decades. Their exposure upsurges the health problems in sensitive population and is also known to participate in atmospheric chemistry. The present study assesses the monthly distribution of bioaerosol load at four different microenvironments inside an academic institute in Kanpur situated in Indo-Gangetic Plain (IGP). The study duration was from November 2017 to April 2018. The measured total bioaerosol concentrations were highest during April (418–610 CFU/m3) at all sites followed by the post-monsoon month of November (369–577 CFU/m3). The winter period showed low concentrations with lowest concentration obtained in January (251–419 CFU/m3). The indoor–outdoor (I/O) bioaerosol ratio was mostly less than one for bacteria depending on microenvironment, but for fungi it was always less than one. The Gram-positive bacteria (GPB) comprised 70–80% of total bacterial concentration, while Gram-negative bacteria (GNB) contributed 20–30% to bacterial concentration in ambient air. The bacterial genera were mostly dominated by Bacillus, Staphylococcus, Micrococcus, Streptococcus, and Enterococcus, while the fungal genera of Aspergillus, Penicillium, Rhizopus, Cladosporium, Basidiobolus, and Epidermophyton were seen in the campus. The statistical analysis showed a weak positive correlation of bioaerosol concentration with meteorological parameters: temperature and relative humidity (RH).

Vivek Srivastava

Papers

Process Control and Optimization of the AOD Process Using Genetic Algorithm

Seasonal bioaerosol load and statistical analysis within different microenvironments of an academic institute situated in the Indo-Gangetic Plain