(479c) Chemistry-Driven of Industrial Symbiosis for Implementing Circular Economy in the Emerging Pharmaceutical Industries

The pharmaceutical industry generates complex and diverse waste streams, including organic solvents, active pharmaceutical ingredients (APIs), heavy metals, and hazardous by-products, which require effective treatment to prevent environmental contamination and regulatory non-compliance. The improper selection of waste treatment technologies can result in increased carbon footprint, energy consumption, and secondary pollution, posing significant risks to ecosystems and human health. Developing a decision-support framework for selecting the most sustainable waste treatment technology is crucial for minimizing environmental impact and promoting a circular economy. A robust and scientifically driven selection process can enhance resource recovery, enable industrial symbiosis, and support the transition toward a sustainable pharmaceutical manufacturing system.

This study introduces a Multi-Criteria Decision-Making (MCDM) framework that systematically evaluates pharmaceutical waste treatment technologies based on environmental impact metrics, treatment efficiency, economic feasibility, qualitative attributes, and stakeholder preferences. The approach incorporates chemistry-driven scenario classification, leveraging Lipinski’s physicochemical properties (molecular weight, LogP, TPSA, HBA, HBD) to characterize pharmaceutical waste and optimize the technology selection process. Each waste stream is classified into distinct scenarios based on its chemical composition, solubility, volatility, and biodegradability, enabling a scientific basis for treatment selection.

To identify the most appropriate treatment technology, we apply a hybrid AHP-TOPSIS model, where the Analytic Hierarchy Process (AHP) determines the relative importance of waste reduction efficiency, energy consumption, CAPEX, OPEX, scalability, regulatory compliance, carbon footprint, and secondary pollution. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then used to rank technologies based on their proximity to an ideal sustainable solution. Given the multi-stakeholder nature of sustainability decision-making, we further employ game theory, specifically the Nash Bargaining Solution (NBS), to fairly allocate decision weights between pharmaceutical manufacturers and sustainability experts. This ensures a balanced trade-off between economic feasibility, environmental responsibility, and industrial scalability.

By integrating chemical property-driven decision modeling and stakeholder-driven game theory optimization, this research presents a comprehensive framework for selecting sustainable pharmaceutical waste treatment technologies. The framework facilitates industrial symbiosis by promoting resource recovery, solvent recycling, and circularity within the pharmaceutical supply chain. Ultimately, this approach aims to reduce ecological footprints, optimize waste valorization strategies, and contribute to a more sustainable pharmaceutical industry.