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The purpose of this paper is to present the main experimental results obtained on the first prototype of an innovative induction heating system. MAGNHEAT was a LIFE project, funded by EU Commission, proposed to demonstrate the possibility of industrial application of a new technology for the induction heating of aluminum billets before extrusion. This technology uses permanent magnet heaters (PMHs), which constitute a high efficiency solution for the heating of high conductive metals.

The paper briefly describes the main steps of the project: the design of the PMH, the realization and installation of the demonstrator on an extrusion production line of Pandolfo Alluminio SpA and, mostly, the performance of the system.

The main results achieved during the preliminary tests on an industrial line have been summarized by evaluating some key performance indicators, as reported in the paper.

The new technology allows a significant reduction of the energy consumption and guarantees the same performance of a classical induction heater.

An inductor for the uniform heating of the extremity of a ferromagnetic steel tube for stress relieving is considered. The main goal of the study is to investigate the possibility to achieve a reasonable design of the inductor when dealing with many design variables.

Genetic optimization algorithms are used for this purpose, demonstrating the applicability of these techniques to the design of induction heating inductors. Genetic algorithms provide to the designer several optimal solutions belonging to Pareto Front, and this way they allow choosing the solution that better fits the technological requirements. In any case, the designer has to adapt the chosen solution to fit in with the real possibilities in industrial application.

The study demonstrates that automatic optimization methods may help the designer of the induction heating system to solve complex problems with very conflicting technological requirements.

In the paper, a problem with a high number of design variables is solved. Moreover, the goals of the optimization process are strongly conflicting, and the proposed problem is a challenging one.

This paper aims to investigate different multi-objective optimization (MOO) approaches for design and control of electromagnetic devices. The main goal of MOO is to find the set of design variables or control parameters which will provide the best possible values of typical conflicting objective functions.

In the research studies, standard genetic algorithm (GA), non-dominated sorting GA (NSGA-II), migration NSGA algorithm and alternance method of optimal control theory are discussed and compared.

The test practical problems of multi-criteria optimization of induction heating processes with respect to chosen quality criteria confirm the effectiveness of application of considered MOO approaches both for the problems of design and control.

This paper represents and investigates different MOO approaches for design and control of electrotechnological systems.

The purpose of this paper is to describe main ideas and demonstrate results of the research activities carried out by the authors in the field of design concepts of induction mass heating technology based on multiple-criteria optimization. The main goal of the studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the multi-objective optimization problem that is mathematically formulated in terms of the most important optimization criteria, for example, maximum temperature uniformity, maximum energy efficiency and minimum scale formation.

Standard genetic algorithm (GA), non-dominated sorting genetic algorithm (NSGA) and alternance method of parametric optimization based on the optimal control theory are applied as effective tools for the practice-oriented problems for multiple-criteria optimization of induction heaters’ design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into the automated optimization procedure.

Optimization procedures are tested and investigated for two- and three-criteria optimization problems solution on the examples of induction heating of a graphite disk, induction heating of aluminum and steel billets prior to hot forming.

Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design and control of different electromagnetic devices and systems.

The paper describes main ideas and results of the research activities carried out by the authors during past years in the field of multiple-criteria optimization of induction heaters’ design based on numerical coupled electromagnetic and temperature field analysis. Implementing the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. The paper also demonstrates that different optimization strategies used (standard GA, NSGA-II and the alternance method of optimal control theory) are effective for real-life industrial applications for multiple-criteria optimization of induction heaters design.

The purpose of this paper is to present a calculation optimization method that is able to achieve the best induced power profile (and subsequent temperature distribution) in a disk or billet workpiece processed by induction heating.

A volume integral method, also known as the mutually coupled circuits method, is implemented in MatLab^® environment to solve axial‐symmetrical induction systems. It is completed with an optimization procedure based on Nelder‐Mead simplex algorithm, with the goal of obtaining a specified distribution of the induced power in the load. In this way, it is possible to predict current amplitudes for implementing the so‐called “zone controlled induction heating” (ZCIH) process.

Some examples of calculation results are given, both for disc and billet loads. By the excitation of the inductor coils with a set of currents of appropriate amplitude and phase values, it is possible to achieve an optimized profile of induced power distributions.

This paper validates a method to predict currents and phases in a load‐inductor ZCIH system, confirming the possibility of obtaining specified induced power density distributions, according to the process requirements, e.g. for compensation of the load edge‐effect.

This paper aims to present the optimal design of an inductor used to heat a magnetic nanoparticle fluid injected in a cell culture inside a Petri dish.

The inductor design is driven by means of a multi-objective optimization algorithm that generalizes the migration-non-dominated sorting genetic algorithm (NSGA); it is called self-adapting migration-NSGA.

The optimized device is able to synthesize a uniform magnetic field in a nanoparticle fluid, substantially helping its heating capability. The ultimate scope is to assist the cancer therapy based on magnetic fluid hyperthermia (MFH).

The optimal design of an inductor for MFH applications has been carried out by applying an improved version of migration-based NSGA-II algorithm including automatic stop and a self-adapting concept. The modified optimization algorithm is suitable to find better optimal solutions with respect to a standard version of NSGA-II.

The purpose of this paper is to present a modified version of the non-dominated sorted genetic algorithm with an application in the design optimization of a power inductor for magneto-fluid hyperthermia (MFH).

The proposed evolutionary algorithm is a modified version of migration-non-dominated sorting genetic algorithms (M-NSGA) that now includes the self-adaption of migration events- non-dominated sorting genetic algorithms (SA-M-NSGA). Moreover, a criterion based on the evolution of the approximated Pareto front has been activated for the automatic stop of the computation. Numerical experiments have been based on both an analytical benchmark and a real-life case study; the latter, which deals with the design of a class of power inductors for tests of MFH, is characterized by finite element analysis of the magnetic field.

The SA-M-NSGA substantially varies the genetic heritage of the population during the optimization process and allows for a faster convergence.

The proposed SA-M-NSGA is able to find a wider Pareto front with a computational effort comparable to a standard NSGA-II implementation.

In electrochemotherapy, flexible electrodes, composed by an array of needles, are applied to human tissues to treat large surface tumors. The positioning of the needles in the tissue depends on the surface curvature. The parallel needle case is preferred, as their relative inclinations strongly affect the actual distribution of electric field. Nevertheless, in some case, small inclinations are unavoidable. The purpose of this paper is to study the electric field distribution for non-parallel needles.

The effect of electrode position is evaluated systematically by means of numerical models and experiments on phantoms for two different angles (5° and 30°) and compared with the case of parallel needles. Potato model was used as phantom, as this tissue becomes dark after few hours from electroporation. The electroporation degree was gauged from the color changings on the potatoes.

The distribution of electric field in different needle configuration is found by means of finite element analysis (FEA) and experiments on potatoes. The electric field level of inclined needles was compared with parallel needle case. In particular, the electric field distribution in the case of inclined needles could be very different with respect to the one in the case of parallel needles. The degree of enhancement for different inclinations is visualized by potato color intensity. The FEA suggested that the needle parallelism has to be maintained as possible as if the tips are closer to each other, the electric field intensity could be different with respect to the one in the case of parallel needles.

This paper analyzes the effect of inclined electrodes considering also the non-linearity of tissues.

The purpose of this paper is to investigate a bi-objective optimization problem characterized by coupled field analysis. The optimal design of a pancake inductor for the controlled heating of a graphite disk is considered as the benchmark problem. The case study is related to the design of industrial applications of the induction heating of graphite disk.

The expected goal of the optimization process is twofold: to improve temperature uniformity in the disk and also electrical efficiency of the inductor. The solution of the relevant bi-objective optimization problem is based on multiphysics field analysis. Specifically, the direct problem is solved as a magnetic and thermal coupled problem by means of finite elements; a mesh-inspired definition of thermal uniformity is proposed. In turn, the Pareto front trading off electrical efficiency and thermal uniformity is identified exploiting evolutionary computing.

By varying the problem targets, different Pareto fronts are identified trading off thermal uniformity and electrical efficiency of the induction-heating device.

These results suggest how to improve the design of this kind of device for the epitaxial growth of silicon wafer; the advantage of using a magnetic concentrator placed close to the inductor axis is pointed out.

The coupling of a multiphysics direct problem with a multiobjective inverse problem is presented as a benchmark problem and accordingly solved. The benchmark provides a simple analysis problem that allows testing various optimization algorithms in a comparative way.