Advances in machine learning applications to resource technology for organic solid waste

Machine learning (ML) techniques, with their advanced data analysis and pattern recognition capabilities, are highly effective for addressing the complexities of organic solid waste (OSW) treatment and resource recovery. As global waste generation continues to increase, the need for efficient and sustainable OSW management solutions is growing. Traditional waste treatment technologies often face challenges in managing the heterogeneous and complex nature of OSW, which varies widely in composition. In contrast, ML can optimize treatment processes, improve resource recovery rates, and enhance decision-making. This study explores a range of commonly used ML models, including artificial neural network (ANN), support vector machine (SVM), decision tree, random forest, and extreme gradient boosting (XGBoost). These models have been used to predict waste characteristics, classify diverse types of OSW, and optimize treatment parameters across various processes, such as thermochemical conversion, anaerobic digestion, and aerobic composting. A key focus of this work is the combination of ML models with optimization algorithms like Genetic Algorithm, which improves the performance of ML models by optimizing hyperparameters and enhancing prediction accuracy. This approach is particularly useful in complex processes such as biological treatment and resource recovery, where ML models can predict waste characteristics and optimize treatment conditions. This work also presents a comprehensive analysis of the application frequency of these ML models in various stages of OSW treatment, including source generation, classification, and treatment processes like pyrolysis, gasification, and composting. This analysis identifies the strengths and weaknesses of each model, highlighting the importance of selecting the most appropriate ML approach based on the specific characteristics of the OSW treatment task. ANN, for example, is particularly useful for complex, nonlinear relationships within biological treatment processes, while SVM is effective for small datasets and high-dimensional data. Despite the promise of ML in OSW management, there are key challenges that remain unresolved. These include issues related to data quality, such as missing or incomplete datasets, and the generalization ability of ML models across different treatment scenarios. Furthermore, selecting the right ML model for a specific task requires careful consideration of the data structure, the complexity of the problem, and the desired outcomes. The full potential of ML in OSW treatment may not be realized without addressing these challenges. This work proposes strategies for overcoming these challenges and improving the effectiveness of ML in OSW treatment. One strategy involves developing integrated models that combine multiple ML techniques to leverage their respective strengths. For example, the ensemble learning method, which integrates the outputs of multiple models, has been demonstrated to improve prediction accuracy and robustness. Another strategy is the use of reinforcement learning and transfer learning, which effectively address dynamic environments and small datasets, respectively. Finally, this work highlights the need for future research to focus on the integration of ML models with real-time process monitoring and control systems. By linking ML with data-driven control strategies, such as model predictive control, it may be possible to develop fully automated, intelligent OSW treatment systems that optimize resource recovery and minimize environmental impact. The work concludes by recommending that researchers continue exploring the combinations of ML with advanced control techniques to push the achievement boundaries in sustainable waste management.