# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 1 Introduction
### Literature Review
[PERSON] and [PERSON] (2023) compared various models for HHV forecasting, including multiple linear regression (MLR), genetic programming (GP), elastic reverse propagation (RP), Levenberg Marquardt (LM), and Deep Support Vector Machine (DSVM), as well as Optimal Deep Learning-Based HHV Prediction (ODL-HHVP), to identify the most accurate method [15]. The input data consisted of the content of oxygen, water, hydrogen, carbon, nitrogen, sulfur, and ash in waste. The study found that ODL-HHVP was the most accurate method for HHV forecasting [15]. [PERSON] et al. (2019) conducted a study to compare the accuracy of three models--programming of gene expression (GEP), support vector machine (SVM), and the Feed-Forward neural network (FFNN)--in predicting HHV. The input data used were the content of C, H, N, and S in municipal waste. The statistical performance of the developedGEP, SVM, and FFNN models for R2 were 0.966, 0.973, and 0.978, respectively, and for RMSE were 1.57, 1.44, and 1.25, respectively [6]. In the authors' previous studies, a machine learning model for HHV prediction of different types of biomass was developed [16]. The input data used were the results of elemental biomass analysis, carbon, nitrogen, and hydrogen analysis. The best results were obtained for a neural network with three input neurons and nine hidden layer neurons. The model was characterized by r = 0.988 and MSE = 0.3. The research has shown that the ANN model can estimate the HHV value based on the elemental composition of the biomass. The models that use waste chemical analyses as input data are presented above. These models are employed to develop and optimize specific waste processing technologies [16]. In addition to the models based on physical and chemical analysis, it is possible to create models based on energy statistics. Statistical models enable large-scale analysis and forecasting, covering entire energy systems, economic sectors, or regions. The models based on statistics enable easy integration with other types of data, such as economic growth, changes in energy policy, or trends in waste production [17]. This provides a broader understanding of the impact of these factors on the energy potential of waste. Statistical models are also more adaptable to changing trends, new processing technologies, or environmental policy changes, which is particularly important in the rapidly evolving waste management and energy sectors. Statistical models are frequently used to support political and strategic decisions. They provide the information necessary for infrastructure planning, investments in energy recovery technologies, and sustainable development policies. Decision-makers and energy planners can use them to develop long-term strategies for waste and energy management, identifying potential growth and investment areas. Machine learning models are also used to forecast energy efficiency and waste drilling efficiency. In a comparative study conducted by [PERSON] and [PERSON] (2022), mathematical models, multiple linear regression, and artificial neural networks (ANNs) were used to predict humidity ratio and drying speed of pineapple cubes. The ANN model was found to be suitable for predicting MR of the drying rate of pineapple cubes [18]. [PERSON] et al. (2021) investigated the impact of drying on rice quality in a fluidized bed dryer under different fluidization regimes and temperatures [19]. They developed a multi-layered artificial feed-forward type perceptron neural network to predict drying time, head rice yield, white index, water absorption index, and elongation index based on fluidization speed and temperature [19]. [PERSON] et al. (2024) reviewed the potential of machine learning algorithms in improving energy and operational efficiency in drying processes [20]. Their findings suggest that these algorithms hold promise for more balanced and economic drying practices.