Electric arc furnaces (EAFs) contribute to almost one third of the global steel production. Arc furnaces use a large amount of electrical energy to process scrap or reduced iron and are relevant to study because small improvements in their efficiency account for significant energy savings. Optimal controllers need to be designed and proposed to enhance both process performance and energy consumption. Due to the random and chaotic nature of the electric arcs, neural networks and other soft computing techniques have been used for modeling EAFs. This study proposes a methodology for modeling EAFs that considers the time varying arc length as a relevant input parameter to the arc furnace model. Based on actual voltages and current measurements taken from an arc furnace, it was possible to estimate an arc length suitable for modeling the arc furnace using neural networks. The obtained results show that the model reproduces not only the stable arc conditions but also the unstable arc conditions, which are difficult to identify in a real heat process. The presented model can be applied for the development and testing of control systems to improve furnace energy efficiency and productivity.

Improving the strategies employed to control robotic arms is of great importance because of the increase in their use in advanced supervisory control strategies, such as digital twins. The inverse kinematic (IK) control of manipulators requires an IK solution and an awareness of the singular configurations. This work presents a complete IK calculation system with singularity analysis for the UR5 robotic arm created by Universal Robots. For a specific robot pose, different angle solution sets are obtained, and one of these solution sets has to be selected to achieve movement continuity and avoid singularities. Two methods for this double purpose are proposed: one calculates all the solution possibilities, and the other obtains only one solution set by following a sequence of decisions and calculations clearly stated by a finite state machine (FSM). Both methods are effective in managing singularities. The FSM-based method complements the IK solution procedure with advantages in the number of computations and performance by producing results that would not lead the joints to move abruptly. The results prove that the presented methods select an IK solution that does not result in a singular configuration, and that most of the time, they lead to the same valid IK solution.

Sustainable, stable, and cost-optimized operation of power grids must be the main objectives of power grid operators and electric utilities. The energy transition towards a preponderant green energy economy requires innovative solutions to enhance the power grid economic dispatches looking for a better allocation of the energy demand among the diverse renewable and fossil fuel energy plants. Green renewable energy systems must be preferred over fossil fuel generators when they are available. However, fossil plants are still required to be kept operational due to the variability and uncertainty of renewable energy plants. This study proposes a hybrid rational economic dispatch model that combines a cost minimization linear model enhanced with a fuzzy logic system for decision-making on wind and hydropower minimum and maximum generation levels. The model considers the intermittency of wind energy and recognizes the strategic value of hydropower as energy storage. The results of the model with real data taken from wind, hydroelectric, geothermal, nuclear, bioenergy, and fossil fuel power plants in the eastern region of Mexico show that a fairer, rational, and cost-optimized power grid economic dispatch can be achieved with the proposed approach.

Electric Arc Furnaces (EAFs) are processes with an intensive consumption of energy, in modern arc furnaces the specific energy conception ranges is around 0.5 MWh per ton of produced liquid steel. In such furnaces small improvements in efficiency represents significant savings. Electric arcs are highly nonlinear and chaotic, and their use in the arc furnaces for scrap melting introduces a very high harmonic distortion in the power systems. To improve the understanding of power phenomena at the arc furnaces, this paper proposes the application of the Current's Physical Components (CPC) power theory, and based on this analysis an interpretation of each the current components is provided. Real EAF voltage and current measurements are computed for a complete melting and heating process. This study proposes the use of the current components as performance indicators and gives some ideas about how to improve the operation and control to optimize the electrical energy used at the arc furnaces.