The study aims to estimate metal foam microstructure parameters for the maximum sound absorption coefficient (SAC) in the specified frequency band to obtain optimum metal foam fabrication. Lu’s theory model is utilised to calculate the SAC of metallic foams that refers to three morphological parameters: porosity, pore size, and pore opening. After Lu model validation, particle swarm optimisation (PSO) is used to optimise the parameters. The optimum values are obtained at frequencies 250 to 8000 Hz, porosity of 50 to 95%, a pore size of 0.1 to 4.5 mm, and pore opening of 0.07 to 0.98 mm. The results revealed that at frequencies above 1000 Hz, the absorption efficiency increases due to changes in the porosity, pore size, and pore opening values rather than the thickness. However, for frequencies below 2000 Hz, increasing the absorption efficiency is strongly correlated with an increase in foam thickness. The PSO is successfully used to find optimum absorption conditions, the reference for absorbent fabrication, on a frequency band 250 to 8000 Hz. The outcomes will provide an efficient tool and guideline for optimum estimation of acoustic absorbents.

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model.