Gianluca Nastasi

A multivariate fuzzy system applied for outliers detection

The paper presents an application of fuzzy logic to the problem of outliers detection. The overall purpose of the work is to point out anomalous data due different causes through a combination of several traditional methods for outliers detection in multivariate datasets and such combination is achieved through a fuzzy inference system. Moreover, the proposed solutions aims to be automatic and self-adaptive, as some parameters which are required for the combination of the different approaches are automatically evaluated by exploiting the available data, without the need of a-priori assumptions or information on a subset of the available data. The proposed method therefore belongs to the class of the unsupervised outliers detection methods. In order to demonstrate the effectiveness of the developed method, extensive tests have been performed on both a simple case study and a database coming from a real industrial context, where the data have to be filtered before their exploitation for process control purposes. The achieved numerical results are presented and discussed.

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.

Simulation of an Automated Warehouse for Steel Tubes

The paper presents a software which simulates an existing automated warehouse of steel tubes, including all the movements of the tubes packs conveyors as well as the management of all the operations for their storage and recovery for delivery to customers. The reason to develop such simulator lies in the needing to test new stocking strategies, that could overcome the drawback of the ones which are already implemented in the current system. Such strategies and their possible improvements are presented and discussed. As the simulator elaborates as inputs the real data of the tubes production and customer orders exactly in the format in which they are processed by the real automated warehouse management, realistic simulations are possible. Numerical results are shown, which demonstrates that the proposed improvements in the tube packs allocation and reordering successfully increase the warehouse capacity and efficiency through an improved exploitation of the available space.

Process Integration in Energy and Carbon Intensive Industries Through Exploitation of Optimization Techniques and Decision Support

Process industries show an ever-increasing interest in reducing their environmental impact and energy consumption as well as maintaining an acceptable profit. This is particularly true for industries such as the steel one, which is among the highest energy consumers worldwide. Process modelling and optimization are techniques by which this problem can be effectively addressed, particularly if the overall system is optimised as a whole. In this article we describe a model for a discrete dynamic optimization of the process gas network in an integrated steel plant. The main sub-plants are modelled in order to calculate mass and energy balances in different scenarios of operation. The scenarios are then exploited within a multi-objective optimization problem, where cost and CO2 emissions are simultaneously minimised. The optimization is carried out by exploitation of evolutionary algorithms that enable a flexible problem formulation and to effectively generate a set of different trade-off solutions. Application of the model to an industrial case study results in an interesting potential for reduction of CO2 emissions and costs. The described optimisation model is embedded in a more general software tool to help the plant managers in their daily decision-making process.

GADF — Genetic Algorithms for distribution fitting

Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to simultaneously identify the distribution type and tune its parameters by exploiting a dataset sampled from the observed random variable and a set of distribution families. The strength of this approach lies in the easiness of the expansion of this set: in fact distributions are simply described by means of their probability density functions and cumulative distribution functions, which are well-known. This approach employs two different score metrics, the Mean Absolute Error and the Kolmogorov-Smirnov test, that are linearly combined in order to find the best fitting distribution. The results obtained in an industrial application are presented and discussed.

Genetic Algorithms Applied to Discrete Distribution Fitting

A common problem when dealing with preprocessing of real world data for a large variety of applications, such as classification and outliers detection, consists in fitting a probability distribution to a set of observations. Traditional approaches often require the resolution of complex equations systems or the use of specialized software for numerical resolution. This paper proposes an approach to discrete distribution fitting based on Genetic Algorithms which is easy to use and has a large variety of potential applications. This algorithm is able not only to identify the discrete distribution function type but also to simultaneously find the optimal value of its parameters. The proposed approach has been applied to an industrial problem concerning surface quality monitoring in flat steel products. The results of the tests, which have been developed using real world data coming from three different industries, demonstrate the effectiveness of the method.

Prediction of under pickling defects on steel strip surface

An extremely important part of the finishing line is the pickling process, in which oxides formed during the hot rolling stage are removed from the surface of the steel sheets. The efficiency of the pickling process is mainly dependent on the nature of the oxide present at the surface of the steel, but, also, on process parameters such as bath composition and time duration are relevant. When acid concentration, solution temperatures and line speed are not properly balanced, in fact, sheet defects like under pickling or over pickling may happen and their occurrence does have a very serious effect on cold-reduction performance and surface appearance of the finished product. Furthermore, product damage from handling or improper equipment adjustment can render the steel unsuitable for further processing. This is the reason why it is important that process significant parameters are controlled and maintained as accurately as possible in order to avoid these undesired phenomena. In the present work, a control algorithm, composed by two different modules, i.e. decision tree and rectangular Basis Function Network, has been implemented to aim of predicting pickling defects and suggesting the optimal speed or the admissible speed range of the steel strip in the process line. In this way the most suitable line speed value can be set in an automatic way or by the technical personnel.

GA-based solutions comparison for warehouse storage optimization

The paper analyses the issues behind allocation and reordering strategies optimization for an existing automated warehouse for the steelmaking industry. Genetic Algorithms are employed to this purpose by deriving custom chromosome structures as well as ad-hoc crossover and mutation operators. A comparison between three different solutions capable to deal with multi-objective optimization are presented: the first approach is based on a common linear weighting function that combines different objectives; in the second one, a fuzzy system is used to aggregate objective functions, while in the last one the Strength Pareto Evolutionary Algorithm is applied in order to exploit a real multi-objective optimization. These three approaches are described and results are presented in order to highlight benefits and pitfalls of each technique.

An Ensemble Classification Method Based on Input Clustering and Classifiers Expected Reliability

In this paper a novel ensemble method (EM) for classification tasks is described. The proposed approach is based on the use of a set of classifiers, each of which is trained by exploiting a different subset of the available training data, which are created by partitioning the input space by means of a self organizing map (SOM) based clustering algorithm. Subsequently, the reliability of each classifier belonging to the ensemble is measured according to the classification accuracy on whole dataset and each classifier is associated to a feed forward neural network, which is able to self-estimate the reliability of single classifiers when coping with a new data. The estimated reliabilities are used in the ensemble aggregation phase in order to provide the final classification of new patterns. The method, tested on literature datasets coming from the UCI repository, achieved satisfactory results improving the classification accuracy with respect to other popular ensemble techniques.

SOM-Based Analysis to Relate Non-uniformities in Magnetic Measurements to Hot Strip Mill Process Conditions

The paper describes the application of a Self-Organising Map for the analysis and the interpretation of measurements taken by a Non-Destructive Testing system named IMPOC\({}^\circledR \) and related to the hardness of steel coils after the hot rolling mill. This work addresses the problem of understanding whether distinct process conditions may lead to non-uniform mechanical properties along the coil. The proposed approach allows to point out, for each specific steel grade, some process conditions that are more frequently associated to disuniformities.