###### Abstract
A multi-layer feed-forward neural network has also been identified as a promising alternative to present statistical techniques for data fitting and functional approximations [12; 13]. Over the last ten years, the application of ANN in mechanical engineering has been discussed, and its potential uses are beginning to gain more attention. As a result of the literature studies, it has been determined that the use of ANN in renewable energy studies has become widespread in recent years and the research on these issues has increased rapidly [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. However, studies on briquetting/pelletizing and their quality in biomass energy with ANN are quite new and few. The ANN structure combines and deduces the impact of the process element on the output function to simulate linear and non-linear systems. In contrast to complex models that were physically related, it regarded a quick and easy way to anticipate the performance of various processes [20]. [PERSON] et al. developed an ANN model to predict the performance of millet bran briquettes [11]. With the capacity of the developed multilayer ANN, the effects of moisture content, temperature, and applied pressure on density, durability, and impact strength were estimated. At the end of the study, it was emphasized that the physical parameters were estimated efficiently with high accuracy and the least deviation. Machine learning is mostly used to anticipate the production of renewable energy sources. Making predictions using historical data is one of the operations of artificial intelligence [15]. [PERSON] et al. created new models based on the final analysis using linear regression and artificial neural network (ANN) to predict biomass waste's higher heating value. The study found that ANN is one of the most applicable and widely used software in the field of energy from waste conversion [17]. [PERSON] and [PERSON] emphasized that in their study, the ANN model provided slightly better performance than the Adaptive Neuro-Fuzzy Inference System (ANFIS). The results show that the developed ANN model is a useful tool to obtain the desired torrefied biomass [18]. In a study by [PERSON] et al. [32] ANN was used to determine the relationship between individual parameters of the briquette production process. They designed a decision support system in the form of a web application to be opened in a web browser that can be used on all kinds of devices and is modular, with the help of ANN to allow modification and expansion of the application in the future. [PERSON] et al. [33] reported that the relationships between the variables related to the density of the biomass pellets are very complex and non-linear, which makes it almost impossible to develop a single, general, and accurate mathematical model, and that one of the most suitable methods for solving such problems is intelligent methods. The ANN model was determined to have a higher predictive ability than the statistical model. As a result, they announced that a properly trained neural network can be used to predict the effect of an input variable on pellet density. [PERSON] et al. [34] presented a model formulation for the briquette-making process by analyzing the Total Briquette Weight after mixing. The entire weight of the briquettes after mixing is taken into account in the model, along with optimization, sensitivity, reliability, and ANN simulation. Genetic algorithms (GA) and artificial neural networks (ANNs) were effectively employed by [PERSON] et al. [35] to comprehend and improve an extrusion process. It is emphasized that the work carried out shows how the combination of RSM and GA can be successfully used for precise optimization of extrusion process variables within experimental limits. For a better knowledge of the extrusion palletization process, they combined response surface methodology (RSM) and GA in their investigations. In another study, [PERSON] et al.