This research on biodiesel production aims to improve energy processes to advance towards a sustainable economy. This study focuses on improving the biodiesel conversion efficiency in a helical tubular reactor coupled with a static mixer. A 2³ factorial design was used to evaluate how variables such as the molar ratio of alcohol–oil (4:1–8:1), residence time (4–8 min), and catalyst concentration (0.5–1 wt%) affect the transesterification process. Soybean oil and methanol were used, with NaOH as a catalyst at 60 °C. The results show that the residence time and catalyst concentration are key factors in increasing biodiesel production by up to 10%. An experimental yield of 84.97% was obtained with a molar ratio of 6:1 alcohol–oil, 0.9 wt% NaOH, and a reaction time of 6 min. The experimental design predicted a yield of 91% with a molar ratio of 4:1 alcohol–oil, 1 wt% NaOH, and a reaction time of 8 min, with a deviation of 1.88% from the experimental values. The fit of the experimental model was R² = 0.9632. These findings are valuable for improving the transesterification process and the development of biodiesel in continuous flow reactors.

Short-term load forecasting is essential for the reliable, secure, efficient, and economic operation of modern power systems and electricity markets. Among many forecasting strategies, the similar day (SD) approach for short-term load forecasting was among the earliest used to assess power demand and remains one of the most intuitive and widely adopted techniques worldwide. However, over time, increasing system complexity, richer datasets, and advances in computational intelligence have led to the evolution of SD methodologies beyond heuristic-based rule formulations. This work presents a study of the relevant literature on short-term load forecasting using SD methods reported between 2000 and 2025. This study analyzes how similarity is defined, how forecasts are generated, and how both stages interact within the complete forecasting process in the reviewed literature. Based on these criteria, a unified taxonomy is proposed to classify SD methods into conventional, intelligent, and hybrid formulations. This study provides insight into the methodologies, their performance, and the systems in which they have been tested. The results show that SD-based approaches remain competitive for short-term forecasting and that incorporating artificial intelligence techniques can further enhance their accuracy.

Electrochemical potential noise signals from LiFePO₄ batteries often contain a slowly varying trend component that overlaps and distorts the intrinsic low-frequency electrochemical noise and must therefore be removed to enable reliable downstream analysis. To address this issue, this work presents a new segment-wise wavelet-based detrending (SWBD) method, applied to commercial rechargeable LiFePO₄/graphite batteries during discharge. The approach combines signal segmentation, dyadic discrete wavelet decomposition, and the joint optimal selection of the mother wavelet and decomposition level under a regularized SNR–RMSE–parsimony criterion, and it is validated against the most widely used detrending procedures: high-order polynomial fitting, empirical mode decomposition (EMD), and fixed wavelets. Quantitative evaluation across discharge segments shows that SWBD attains higher signal-to-trend ratios and lower reconstruction errors, ensuring improved temporal and spectral fidelity. According to the power spectral density (PSD) analysis, SWBD produces a smooth and continuous spectral slope with α ≈ 1, consistent with 1/f-type electrochemical noise. The Shannon energy analysis confirms a uniform and gradual multiscale energy distribution, reflecting efficient energy dispersion. Overall, the results demonstrate that SWBD provides a robust framework for adaptive trend removal, preserving the intrinsic stochastic dynamics of electrochemical fluctuations and enabling accurate spectral and statistical characterization in lithium-ion energy storage systems.

Noise elimination in current signals of three-phase induction motors, considered as energy systems for electromechanical conversion, is a critical preprocessing step for reliable condition monitoring and fault diagnosis. However, conventional wavelet-based denoising approaches often treat noise suppression as a generic filtering task, which may distort diagnostically relevant spectral components and inter-phase relationships. To address this limitation, this paper presents a physically constrained denoising framework that integrates the Clarke transformation with dyadic wavelet analysis to enable diagnostic-safe noise attenuation. The proposed method explicitly preserves frequency bands associated with supply harmonics, mechanical phenomena, and fault-related sidebands, while enforcing inter-phase coherence and zero-sequence stability in the Clarke domain. Wavelet parameters are selected through a diagnostic-oriented multi-criteria framework that jointly balances disturbance attenuation, harmonic fidelity, coherence retention, zero-sequence stability, and time-domain waveform integrity. Experimental validation using real three-phase induction motor current measurements under steady-state conditions shows that the proposed framework achieves noise reduction ratios of approximately 8–10 dB, while preserving the amplitudes of the main harmonic components with deviations below ${10}^{-3}$ dB. These results demonstrate that the proposed method provides a robust and physically consistent preprocessing stage for current-based monitoring of three-phase AC machines.