Hydrogen is gaining recognition as a clean energy source, with numerous production methods available for its generation. This paper presents a detailed thermodynamic analysis of the steam-ethane pyrolysis reaction, focusing on optimizing the reaction enthalpy to maximize hydrogen output. A succinct review of the current hydrogen production technologies is provided, with a focus on ethane pyrolysis integrated with steam as the chosen methodology. The study employs a Lagrange multiplier method to minimize the reaction enthalpy under specific constraints, ensuring thermodynamic equilibrium. Simulation results demonstrate that the optimal operating conditions for maximum hydrogen production are achieved at a temperature of 840°C and a pressure of 3.8 bar, with a steam-to-ethane ratio of 3:1. The research offers the framework to design an efficient automation solution for the pyrolysis process, underscoring the significance of maintaining an ideal steam-to-ethane ratio of roughly 3:1, as well as the precise control of temperature and pressure. Simulation outcomes support the thermodynamic analysis's efficacy in boosting hydrogen production, highlighting its potential for large-scale applications with low-cost raw materials in the petrochemical industry.

In this paper a technique of handling Gaussian stochastic optimization problems is presented. These optimization problems are experienced in various industrial processes, where random disturbances take action directly on the constraints defining the operational process domain and on the parameters of the criterion function. The objective of our scientific work aims to find a solution to the stochastic problem reformulated as an equivalent deterministic optimization problem with totally admissible solutions. This problem is referred to as a risk problem, and it is handled using nonlinear mathematical programming approaches and represents the optimal decision for efficient exploitation of the industrial applications. A numerical Gaussian stochastic optimization case study for an optimal solar energy system exploitation is considered to validate the theoretical results.

Direct Drive Wind Turbines (DDWTs) are used in newer wind farm installations. They are equipped with Permanent Magnet Synchronous Generators (PMSGs), which offer high efficiency for energy generation, but which are also prone to faults. They account for almost a quarter of the downtime of a DDWT. Machine Current Signature Analysis (MCSA) is often used to look for generator problems, as these impairments introduce supplementary harmonics into the generated currents. The Fast Fourier Transform (FFT) is utilized to compute the spectrum of the currents and to look for these additional harmonics. However, the FFT computes the whole spectrum, while the number of possible faults and the number of introduced harmonics is very low. The Goertzel Algorithm (GA), implemented as a filter - the Goertzel Filter (GF), is presented in this paper as a more efficient alternative to the FFT. The GF was tested in Matlab/Simulink and simulation results prove that it can return the squared magnitude of these harmonics. This information can be used to set thresholds for fault detection within a FDI algorithm.