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.

This article presents the implementation of an adaptive observer to validate a falling film evaporator mathematical model. The evaporator consists of four coils, and each coil has four tubes. The heating flow in the first and third coils flows from bottom to top. Meanwhile, the heating flow in the second and fourth coils flows from top to bottom. The mathematical model of the evaporator is parameterized with the geometry data of the experimental device. Since the mathematical model depends on the film breakdown onset Reynolds number ($R{e}_{Onset}$) to estimate the evaporator temperatures, an adaptive observer is applied to estimate this unknown parameter ($R{e}_{Onset}$). The observer design is developed through the evaporator mathematical model. The research aims to estimate the $R{e}_{Onset}$ at different operating conditions to accurately estimate the evaporator temperatures since there is no general correlation for estimating it or a sensor to measure this parameter. Once the $R{e}_{Onset}$ is estimated at different operating conditions, the $R{e}_{Onset}$ results are injected into the model for validation. The results of implementing the observer showed that the temperature estimation errors are between 0.00003% and 0.02815%. Moreover, the temperatures simulated with the model using the $R{e}_{Onset}$ estimated with the observers had errors between 0.04012% and 0.14160%.

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.