Data Science and Search Engines for Green Technologies and Sustainable Industries

This thesis introduces an innovative digital platform designed to advance industrial symbiosis (IS) and circular economy (CE) practices in sustainable waste management. Addressing critical challenges of resource scarcity and environmental sustainability, this platform leverages modern web technologies to connect waste producers with potential users, streamline waste-to-resource conversion processes, and provide consultancy services. The platform’s architecture combines a robust backend powered by NestJS and MongoDB with a responsive frontend built on Vue.js, enabling sophisticated functionalities for waste management and resource optimization. The platform orchestrates interactions among companies within the industrial ecosystem. Industrial companies participate both as waste producers and potential consumers, managing their waste outputs and resource needs through adding site locations on their profiles. Environmental consultants provide expertise in waste management, while specialized service providers facilitate waste-to-resource conversion processes. The marketplace workflow optimizes resource matching through an advanced system that considers material properties, geographical proximity, and quantity requirements. Waste producers create detailed listings of their materials, while potential consumers can search and filter using different criteria. The system implements advanced search functionalities through Elasticsearch, enabling efficient discovery of symbiotic relationships. The data model, built on MongoDB’s document-oriented architecture, ensures integrity through comprehensive schema validation while maintaining flexibility for future extensions. Security is implemented through multiple layers: JWT-based authentication manages user sessions and role-based access control governs resource access. The platform provides messaging capabilities to facilitate stakeholder collaboration. The thoroughly discusses future possible enhancements and their feasibility, including machine learning for predictive analytics, blockchain for improved traceability, and IoT integration for real-time monitoring. This research contributes to technology-driven CE solutions, offering a practical tool for industries transitioning toward sustainable resource management practices, while demonstrating the transformative potential of digital innovation in fostering industrial symbiosis.