Digital current control of electric arc furnace by parallel modular three-phase IGBT inverters

Electric Arc Furnaces (EAF) have been traditionally fed by means of a medium to low voltage transformer (between grid and electrodes), where the main control action consists of modifying the electrodes vertical position. This simple and robust arrangement suffers from grid \u201cpollution\u201d and poor current control. Current control of EAF by means of three phase modular inverters is reported here for the first time. Several control issues are analyzed and addressed, ranging from pure control (e.g. addressing load imbalance and limits due to low switching frequency) to system management. Two different current regulation methods are proposed and compared, namely a scalar approach and a vector method using \u201cquasi zero phase lag\u201d sampling and gains adaptation. The second technique achieves very good control, leading to lower oversizing of the converter stage and process optimization. Simulations have been performed including digital controller architecture, converters and arc electrical behavior. Preliminary experimental measurements are reported based on an actual plant

On the discrete equation model for compressible multiphase fluid flows

The modeling of multi-phase flow is very challenging, given the range of scales as well as the diversity of flow regimes that one encounters in this context. We revisit the discrete equation method (DEM) for two-phase flow in the absence of heat conduction and mass transfer. We analyze the resulting probability coefficients and prove their local convexity, rigorously establishing that our version of DEM can model different flow regimes ranging from the disperse to stratified (or separated) flow. Moreover, we reformulate the underlying mesoscopic model in terms of an one-parameter family of PDEs that interpolates between different flow regimes. We also propose two sets of procedures to enforce relaxation to equilibrium. We perform several numerical tests to show the flexibility of the proposed formulation, as well as to interpret different model components. The one-parameter family of PDEs provides an unified framework for modeling mean quantities for a multiphase flow, while at the same time identifying two key parameters that model the inherent uncertainty in terms of the underlying microstructure

Editorial: state of the art body composition profiling: advances in imaging modalities and patient outcomes

In the last years, body composition (BC) analysis has emerged as a ground-breaking tool that can provide helpful data about nutritional status, in addition to more conventional indicators, as albumin value and body mass index. Recent data disclosed that distinct patterns of BC are associated with different outcomes, in particular in oncologic patients. Hence, we proposed this research topic, on one side, to expand knowledge about the imaging techniques to evaluate BC assessment, ranging from the most conventional ones to the most advanced (including artificial intelligence-aided techniques); on the other, to assess the effects of BC on different outcome indicators, as post-operative or chemotherapy-related complications and survival. In our research topic, we published eight papers: 3 focused on liver diseases, 1 on pancreatic cancer, 1 on ovarian cancer, 1 on lung cancer, 1 on breast cancer and 1 on radiotherapy, thus highlighting the wide variety of fields in which the clinical role of BC is under evaluation

Self-adaptive high-frequency injection based sensorless control for interior permanent magnet synchronous motor drives \u2020

Abstract: An auto-tuning and self-adaptation procedure for High Frequency Injection (HFI) based position and speed estimation algorithms in Interior Permanent Magnet Synchronous Motor (IPMSM) drives is proposed in this paper. Analytical developments show that, using conventional approaches, the dynamics of the high-frequency tracking loop varies with differential inductances, which in turn depend on the machine operating point. On-line estimation and adaptation of the small signal gain of the loop is proposed here, allowing accurate auto-tuning of the sensorless control scheme which does not rely on a priori knowledge of the machine parameters. On-line adaptation of Phase-Locked Loop (PLL) gains and of the injected voltage magnitude is also possible, leading to important advantages from the performance, loss and acoustic point of view. The theoretical basis of the method has been introduced first and the main concept demonstrated by means of simulations. Implementation has been carried out using the hardware of a commercial industrial drive and two Interior Permanent Magnet Synchronous Motors, namely a prototype and an off-the-shelf machine. Experimental tests demonstrate the feasibility and effectiveness of the proposal

Factor demand linkages, technology shocks, and the business cycle

This paper argues that factor demand linkages can be important for the transmission of both sectoral and aggregate shocks. We show this using a panel of highly disaggregated manufacturing sectors together with sectoral structural VARs. When sectoral interactions are explicitly accounted for, a contemporaneous technology shock to all manufacturing sectors implies a positive response in both output and hours at the aggregate level. Otherwise there is a negative correlation, as in much of the existing literature. Furthermore, we find that technology shocks are important drivers of the business cycle