The production of biofuels (biogas, ethanol, methanol, biodiesel, and solid fuels, etc.), beginning with cocoa pod husk (CPH), is a way for obtaining a final product from the use of the principal waste product of the cocoa industry. However, there are limitations to the bioconversion of the material due to its structural components (cellulose, hemicellulose, and lignin). Currently, CPH pretreatment methods are considered a good approach towards the improvement of both the degradation process and the production of biogas or ethanol. The present document aims to set out the different methods for pretreating lignocellulosic material, which are: physical (grinding and extrusion, among others); chemical (acids and alkaline); thermochemical (pyrolysis); ionic liquid (salts); and biological (microorganism) to improve biofuel production. The use of CPH as a substrate in bioconversion processes is a viable and promising option, despite the limitations of each pretreatment method.

This study introduces a new method to locate cracks in wind turbine blades using the support vector machine algorithm and the tangential vibration signal measured at the root blade in static conditions. The method was implemented in hardware and experimentally validated on 200 W wind turbine blades. The blade conditions were healthy, and transverse cracked at the root, midsection, and tip. The experimental procedure is easy, and only one low-cost piezoelectric accelerometer is needed, which is affordable and straightforward to install. The machine learning technique used requires a small dataset and low computing power. The results show exceptional performance, achieving an accuracy of 99.37% and a precision of 98.77%. This approach enhances the reliability of wind turbine blade monitoring. It provides a robust early detection and maintenance solution, improving operational efficiency and safety in wind energy production. K-nearest neighbors and decision trees are also used for comparison purposes.