The use of refuse-derived fuel (RDF) in cement kilns offers a multifaceted approach to sustainability, addressing environmental, economic, and social aspects. By converting waste into a valuable energy source, RDF reduces landfill use, conserves natural resources, lowers greenhouse gas emissions, and promotes a circular economy. This sustainable practice not only supports the cement industry in meeting regulatory requirements but also advances global efforts toward more sustainable waste management and energy production systems. This research promotes the integration of RDF as fuel in cement kilns to reduce the use of fossil fuels by improving the control of the combustion. Addressing the variable composition of RDF requires continuous monitoring to ensure operational stability and product quality, traditionally managed by operators through visual inspections. This study introduces a real-time, computer vision- and deep learning-based monitoring system to aid in decision-making, utilizing existing kiln imaging devices for a non-intrusive, cost-effective solution applicable across various facilities. The system generates two detailed datasets from the kiln environment, undergoing extensive preprocessing to enhance image quality. The YOLOv8 algorithm was chosen for its real-time accuracy, with the final model demonstrating strong performance and domain adaptation. In an industrial setting, the system identifies critical elements like flame and clinker with high precision, achieving 25 frames per second (FPS) and a mean average precision (${mAP}^{50}$) of 98.8%. The study also develops strategies to improve the adaptability of the model to changing operational conditions. This advancement marks a significant step towards more energy-efficient and quality-focused cement production practices. By leveraging technological innovations, this research contributes to the move of the industry towards sustainability and operational efficiency.

Although mass is one of the most relevant process variables, industries may lack an inline monitoring of mass, which has a high cost in some cases. Due to their availability in sorting processes, cameras have potential as a low-cost alternative for the estimation of mass in recycling applications. Nevertheless, further research is needed to transform image information into mass. This work tackles this challenge by proposing a novel method of converting image information into mass of particles, complementing size measures with intensity and texture features extracted from the whole picture. Models were adjusted, employing machine learning techniques, using an industrial waste sample of post-consumer plastic film. The visual properties showed a dependency on mass labels, and the models achieved an error of 9 g for subsamples between 2 and 82 g. The analysis and validation of this image processing method provide a new alternative for the estimation of particle mass.