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This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. This proposal is devised to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe Index (WI) and the Higher Heating Value (HHV)) within admissible limits. The model has been applied to a gas network case study with three distinct scenarios and implemented using Python. The findings from the case study show the maximum permissible volume of hydrogen in the network, quantify the total savings in natural gas, and estimate the reduction in carbon dioxide emissions. © 2023 IEEE.

Abstract
In this paper, a different Fiber Loop Mirror (FLM) configuration with two circulators is presented. This configuration is demonstrated and characterized for sensing applications. This new design concept was used for strain and torsion discrimination. For strain measurement, the interference fringe displacement has a sensitivity of (0.576 +/- 0.009) pm.mu epsilon(-1). When the FFT (Fast Fourier Transformer) is calculated and the frequency shift and signal amplitude are monitored, the sensitivities are (-2.1 +/- 0.3) x 10(-4) nm(-1) mu epsilon(-1) and (4.9 +/- 0.3) x 10(-7) mu epsilon(-1), respectively. For the characterization in torsion, an FFT peaks variation of (-2.177 +/- 0.002) x 10(-12) nm(-1)/degrees and an amplitude variation of (1.02 +/- 0.06) x 10(-3)/degrees are achieved. This configuration allows the use of a wide range of fiber lengths and with different refractive indices for controlling the free spectral range (FSR) and achieving refractive index differences, i.e., birefringence, higher than 10(-2), which is essential for the development of high sensitivity physical parameter sensors, such as operating on the Vernier effect. Furthermore, this FLM configuration allows the system to be balanced, which is not possible with traditional FLMs.

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Metaheuristic optimization algorithms are increasingly used to reach near-optimal solutions for complex and large-scale problems that cannot be solved in due time by exact methods. Metaheuristics’ performance is, however, deeply dependent on their effective configuration and fine-tuning to align the algorithm’s search process with the specific characteristics of the problem that is being solved. Although the literature already offers some solutions for automatic algorithm configuration, these are usually either algorithm-specific or problem-specific, thus lacking the capability of being used for diverse metaheuristic models or diverse optimization problems. This work proposes a new approach for the automatic optimization of metaheuristic algorithms’ parameters based on a multi-agent system approach. The proposed model includes an automated fine-tuning process, which is used to optimize a given function in an algorithm- and problem-agnostic manner. Results show that the proposed model is able to achieve better optimization results than standard metaheuristic algorithms, with a negligible increase in the required execution time.

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