Promise of multiobjective genetic algorithms in coating performance formulation

107

A new data-driven reference vector guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using twelve process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.KEYWORDS: blast furnace, ironmaking, metamodeling, multi-objective optimization, model management, data-driven optimization, Pareto optimality INTRODUCTIONIron blast furnace is an immensely complex reactor and running it in an optimized fashion is a very complex task [1] . Although analytical models exist for this type of 2 reactors that produces hot metal [2] , such models are often quite cumbersome and of limited applicability in a real-life industrial scenario. In addition, a complete understanding of the blast furnace process involves handling several objectives together, which so far has been only marginally successful [3] . Thus, it is extremely complex, if not impossible, to build a simulator for blast furnace optimization and one has to rely upon limited amount of noisy data collected in daily operations to perform optimization.Another challenge in optimization of blast furnaces is that it involves multiple conflicting objectives, which is often known as multiobjective optimization [4] . The evolutionary algorithms have been widely used to solve multiobjective optimization problems [5] .However, the efficacy of most multiobjective evolutionary algorithms deteriorates as the number of objectives becomes more than four [4] , which makes them less suited for blast furnace optimization. Fortunately, many-objective optimization to solve problems with more than three objectives, has received increasing attention recently and many evolutionary algorithms have been developed for such problems [3,6] .Purely data-driven evolutionary optimization has received little attention with few exceptions. Most recently, Wang et al. [7] have also categorized data-driven optimization into two types: on-line and off-line. In on-line optimization, small amount of new data is available during the optimization while in off-line optimization, no extra data other than those in hands is available. The authors have also proposed a surrogate-based data-driven approach, capable of optimizing a trauma system involving two conflicting objectives in an evolutionary way. Although trauma system optimization belongs to offline data-driven 3 optimization [7] , there are a large amount of data available. By contrast, as indicated by Guo et al. [8] , off-line optimization becomes extremely challenging, when amount of historical data is small and noisy. Unfortunately, blast furnace optimization that is being...

Section: Discussionmentioning

confidence: 90%

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

Chugh

Chakraborti

Sindhya

et al. 2017

107

“…A set of feasible solutions is Pareto-optimal if no other feasible solution could be constructed that would be at least as good as all the members in that set in terms of all the objectives, while been strictly better in terms of at least one objective. A simple elaboration of this concept, devoid of mathematical complexity, can be found in [21]. The data (chemical concentrations of each of the alloying elements) used in this case was generated using Sobol's quasi-random sequence generation algorithm [13].…”

Section: Methodsmentioning

confidence: 99%

“…The optimized models are created in EvoNN by applying a predator-prey GA [15] on this population of neural nets, which searches for a tradeoff between the training error of the network and its corresponding complexity expressed through the number of connections used. The Pareto-frontier [20,21] obtained through this strategy contains the optimized models, out of which a preferred network is usually picked up by applying a suitable information criterion [16]. Further details of EvoNN are available elsewhere [15,16].…”

Section: Methodsmentioning

confidence: 99%

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Jha

Pettersson

Dulikravich

et al. 2014

Self Cite

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.

“…Genetic algorithms and evolutionary computation have been used in optimization problems in materials and manufacturing processes successfully ranging from carbon nanotubes to the process optimization [8,11,12]. Recently, a review was provided on the soft computing techniques used in designing metal alloys based on composition-processmicrostructure-property relations by Datta and Chattopadhyay [13] and a critical assessment of this field was given by Chakraborti [14].…”

Section: Genetic Algorithms Based Optimizationmentioning

confidence: 99%

Machine Learning Based Predictive Modeling of Machining Induced Microhardness and Grain Size in Ti–6Al–4V Alloy

Arısoy

Özel

2014

Titanium and its alloys are today used in many industries including aerospace, automotive and medical device and among those Ti-6Al-4V alloy is the most suitable because of favorable properties such as high strength-to-weight ratio, toughness, superb corrosion resistance and bio-compatibility. Machining induced surface integrity and microstructure alterations size play a critical role in product fatigue life and reliability.Cutting tool geometry, coating type, and cutting conditions can affect surface and subsurface hardness as well as grain size. In this paper, predictions of machining induced microhardness and grain size are performed by using 3D finite element simulations of machining and machine learning models. Microhardness and microstructure of machined surfaces of Ti-6Al-4V are investigated. Hardness measurements are conducted at elevated temperatures to develop a predictive model by utilizing FEM based temperature fields for hardness profile. Measured hardness, grain size and fractions are utilized in developing predictive models. Predicted microhardness profiles and grain sizes are then utilized in understanding the effect of machining parameters such as cutting speed, tool Downloaded by [Gazi University] at 18:51 03 February 20152 coating and edge radius on the surface integrity. Optimization using genetic algorithms is performed to identify most favorable tool edge radius and cutting conditions.