Michele Forzan

Technologies that use electricity to treat and transform materials, particularly induction, dielectric, and microwave heating methods, as well as traditional electric heating with resistors, play a key role in the transition to production systems with reduced environmental impact

Electromagnetic stirring is an established strategy for improving the quality of continuously cast metals. In thin aluminium slabs, however, its implementation is constrained by short solidification length, limited installation space, and magnetic-field distortion caused by surrounding metallic components. This work presents a staged design assessment of three compact electromagnetic stirring configurations conceived for the Wheel and Belt continuous casting line of Continuus-Properzi S.p.A. Harmonic electromagnetic simulations were performed for all three configurations, whereas a coupled steady-state, isothermal electromagnetic–fluid-dynamic analysis, based on a fully liquid melt assumption, was carried out only for the axial-flux configuration. Qualitative experiments on a low-melting-point GaInSn alloy were used as proof-of-concept support for the predicted stirring patterns, rather than as a full quantitative validation of the industrial process. Because the three configurations were investigated at different levels and under different operating conditions, the results do not establish a quantitative performance ranking. The axial-flux configuration was selected for further development based on its compactness, integration feasibility, and the circular flow topology obtained numerically and observed qualitatively. Future work will focus on quantitative velocity measurements, transient and thermo-solidification modelling, and industrial-scale validation.

Electroporation outcomes are governed by the local electric field distribution and transmembrane voltage, both of which may be altered by nanoscale elements positioned near the cell membrane. In this study, we developed a two-dimensional finite-element electromagnetic model to investigate the effect of a membrane-proximal silica-coated superparamagnetic iron oxide nanoparticle cluster during a trapezoidal electroporation pulse. The model couples electric and magnetic field components with a membrane electroporation formulation based on Smoluchowski-type pore-density dynamics. Simulations were performed with and without a nanoparticle positioned 5 nm from the membrane, considering different cytosol and extracellular medium conductivities. The results show that the nanoparticle induces a highly localized perturbation of the electric field, whose magnitude depends on the sampling region and conductivity contrast. Transmembrane voltage is modestly and transiently modulated during pulse rise time, whereas the effect is limited during the pulse plateau. Pore-density analysis further indicates that the nanoparticle does not induce a generalized increase in electroporation-related parameters and may locally reduce pore density near the nanoparticle–membrane interface. Overall, the model identifies transient and conductivity-dependent nanoscale field redistribution caused by membrane-proximal silica-coated magnetic nanoparticles, while highlighting the need for three-dimensional modeling and experimental validation before inferring electroporation enhancement.

Induction heating is widely used in industrial processes such as forging, hardening, and additive manufacturing, but its accurate numerical simulation requires coupled electromagnetic and thermal finite element analyses with nonlinear, temperature-dependent material properties. This work proposes a deep learning surrogate model based on a convolu-tional neural network for TEAM Workshop Problem 36, a reference benchmark for nonlinear magneto-thermal induction heating. A database of more than 40,000 finite element solutions was generated by varying the supply current from 2 to 6 kA and the frequency from 2 to 6 kHz, while accounting for transient nonlinear effects, including the Curie transition. The network, composed of 24 layers with transposed convolutions, batch normalization, and dropout, maps current, frequency, and time to radial temperature distributions in the steel billet. For most operating conditions, the model achieves mean absolute percentage errors of about 6–7% for radial line in the middle of the billet and about 10% for radial line close to the billet end. Larger discrepancies occur during the early heating stage and near the Curie temperature. Prediction times are reduced by three to four orders of magnitude with respect to a single finite element analysis. The results indicate that the proposed surrogate enables fast temperature estimation for optimization, digital twins, and closed-loop control of induction heating systems.

Industrial production still relies heavily on thermal processes that predominantly use fossil fuels for energy. This has significant consequences for primary energy use and greenhouse gas emissions. Meanwhile, rapid advances in electrotechnologies—defined as processes that use electrical energy to transform materials through internal heat dissipation (inductive, conductive, or dielectric/microwave) or heat transfer via resistance and infrared systems—are paving the way for a transition to a non-fossil fuel-based energy supply across a wide range of temperatures and power densities. However, replacing fuel with electricity is not simply a case of making a straightforward substitution; the feasibility of this change is determined by process requirements, constraints on installation space and grid connection, the reliability and volatility of the electricity supply, and economics. This paper therefore proposes a simple, decision-oriented methodology to assess the feasibility of defossilisation from energetic and economic perspectives. The methodology centres on a “substitution coefficient” that compares the amount of fossil energy substituted by a given amount of electrical energy and benchmarks this against the primary energy intensity of electricity generation. The methodology is demonstrated using case studies from energy-intensive sectors such as cement production (using resistance and microwave methods), steel strip processing (with inductive boosting combined with resistive holding) and metal melting for cast iron and aluminium. The case studies show under which conditions electrification can be implemented as a drop-in substitute, a hybrid booster or an enabler of new production models. The results indicate where electrotechnologies can deliver primary energy savings and CO₂ reductions today and outline the conditions under which their advantages will increase as power systems become more decarbonised.

A cascade deep learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on deep neural networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through finite element (FE) analysis. This deep learning-based cascade approach reduces computational time and multiphysics simulations compared to classical methods by reducing the dimensionality of parameter mapping. Therefore, the proposed method proves to be effective in solving high-dimensional multiphysics inverse problems. From the application point of view, achieving thermal uniformity (±7% fluctuation at 1100 °C) improves layer quality, increases efficiency, and reduces operating costs of epitaxy reactors.

With the increasing volumes of spent lithium-ion batteries from electric vehicles and the concurrent increase in raw materials cost for cathode production, finding effective methods for recycling battery materials has become critically important. This study investigated a pyrometallurgical approach using microwave irradiation to achieve carbothermal reduction of LiCoO₂. FactSage thermodynamic calculations were performed for process simulation and an infrared thermal camera was employed for temperature measurements, allowing the authors to optimize the process parameters to obtain metallic cobalt. Specifically, the research included microwave experiments on mixed black mass samples of anode and cathode materials in different proportions, treated at varying power levels and exposure times under air atmosphere. The effect of the process parameters and therefore of the temperature on microstructure was studied with SEM-EDS and XRD analysis. The feasibility of a wet magnetic separation method between cobalt and lithium compounds formed during the reaction was also evaluated. The results obtained from the final separation process indicated that individual compounds can be obtained at the end of the cycle; moreover, the optimization of time, temperature, and graphite additions during the tests allowed the authors to obtain promising results.

A cascade deep-learning approach is proposed for optimizing the design and control of a dual-frequency induction heating system used in semiconductor manufacturing. The system is composed of two independent power inductors, fed at different frequencies, to achieve a homogeneous temperature profile along a graphite susceptor surface, crucial for enhancing layer quality and integrity. The optimization process considers both electrical (current magnitudes and frequencies) and geometrical parameters of the coils, which influence the power penetration and subsequent temperature distribution within the graphite disk. A two-step procedure based on Deep Neural Networks (DNNs) is employed. The first step, namely optimal design, identifies the optimal operating frequencies and geometrical parameters of the two coils. The second step, namely optimal control, determines the optimal current magnitudes. The DNNs are trained using a database generated through Finite Element (FE) analysis. The optimized system successfully achieved significant thermal uniformity across the susceptor surface, such that the temperature fluctuations over the entire disk radius were kept within ±7% of the target temperature (1100 °C). This significant improvement confirms the effectiveness of the deep learning-based cascade approach for solving high-dimensional Multiphysics inverse problems.

Induction heating processes involve complex multiphysical interactions between electromagnetic and thermal models, often characterized by nonlinear materials. The finite element method is a common approach to tackling such problems, but its computational complexity may become a burden for optimization loops or real-time monitoring. This work proposes a surrogate modeling approach that combines proper orthogonal decomposition and Gaussian process regression, suited for nonlinear and temperature-dependent magnetic materials. The approach is applied to reduce the complexity of the electromagnetic model of the testing electromagnetic analysis method (TEAM) problem 36, consisting of a copper coil heating through induction a steel billet. Results show how the non-intrusive machine learning approach can accurately reconstruct the field distribution, offering a viable first step towards fast multiphysical simulations involving nonlinear magnetic materials.