Deep Learning-based Image Caption Generator for Real-time Monitoring and Predictive Control of Concentration of Polluting gases

The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas concentrations. The proposed system leverages advanced machine learning techniques to analyze images captured during gas capture processes, generating semantically rich and grammatically accurate captions that describe the visual content. At the core of the system is a hybrid architecture that integrates a Convolutional Neural Network (CNN) for high-level feature extraction from input images and a Gated Recurrent Unit (GRU) for sequential caption generation. The CNN effectively identifies and extracts relevant features from the images, while the GRU models the temporal dependencies inherent in the data, allowing for the generation of coherent and contextually appropriate captions. This dual approach not only enhances the accuracy of the captions but also facilitates a deeper understanding of the processes being monitored. In addition to caption generation, the system incorporates a predictive control module that utilizes the generated captions to forecast future behaviors of the gas capture processes. This predictive capability enables operators to make informed decisions, optimizing the efficiency and effectiveness of pollutant gas management in industrial applications. The proposed system demonstrates significant potential for real-time applications, providing a robust tool for environmental monitoring and control. By enabling the efficient and sustainable utilization of gases, this innovative approach contributes to the broader goal of reducing environmental impact and promoting cleaner industrial practices. The results indicate that deep learning techniques can significantly enhance the capabilities of image captioning systems, paving the way for their application in various domains beyond environmental monitoring.