Artificial Intelligence for a Circular Solvent Economy: An Environmental Decision-Support Architecture for Sustainable Pharmaceutical Manufacturing
Abstract
Industrial decarbonization requires integrated digital strategies that combine artificial intelligence (AI), circular economy principles, and life-cycle thinking to improve the environmental sustainability of manufacturing processes. Pharmaceutical manufacturing is particularly solvent-intensive, with organic solvents contributing substantially to material consumption, energy demand, greenhouse gas emissions, and hazardous waste generation. Although green chemistry, solvent substitution, life-cycle assessment (LCA), and digital manufacturing have advanced sustainable process design, their integration into a unified environmental decision-support architecture remains insufficiently developed. This communication presents an Artificial Intelligence for Circular Solvent Economy (AICSE) framework that connects AI-assisted prediction, digital process simulation, circular solvent management, and life-cycle sustainability assessment within a structured environmental decision-support architecture. The framework retains Process Mass Intensity (PMI), Circular Process Mass Intensity (cPMI), Global Warming Potential (GWP), and Cumulative Energy Demand (CED) as complementary sustainability indicators within an LCA-oriented assessment structure. A literature-based Sertraline active pharmaceutical ingredient (API) manufacturing process is used as an illustrative application to demonstrate how the proposed architecture can organize solvent evaluation, virtual process assessment, circular solvent recovery, and environmental decision-making. Building upon our previously reported solventcentric sustainability framework, this work extends its scope by explicitly integrating Environmental Artificial Intelligence, life-cycle thinking, Digital Twin, and circular economy principles into a transferable digital sustainability architecture for solvent-intensive manufacturing. In this work, Environmental Artificial Intelligence (Environmental AI) refers to the application of AI and machine-learning methods to environmental prediction, assessment, optimization, monitoring, and decision support within manufacturing systems. The principal contribution is architectural rather than algorithmic: AICSE connects previously independent components into a structured environmental decision-support pathway and introduces implementation-oriented readiness and maturity models. The framework is presented as a conceptual architecture supported by an illustrative literature-based application rather than as an empirical validation study. AICSE provides a practical pathway toward evidence-based sustainability governance, resource-efficient manufacturing, and progressively integrated environmental intelligence.
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BibTeX
@misc{shaikh2026artificial,
title = {{Artificial Intelligence for a Circular Solvent Economy: An Environmental Decision-Support Architecture for Sustainable Pharmaceutical Manufacturing}},
author = {ISAK RAJJAK SHAIKH},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008515/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008515/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Artificial Intelligence for a Circular Solvent Economy: An Environmental Decision-Support Architecture for Sustainable Pharmaceutical Manufacturing
AU - ISAK RAJJAK SHAIKH
PY - 2026
DA - 2026/09/08
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008515/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008515/v1
AB - Industrial decarbonization requires integrated digital strategies that combine artificial intelligence (AI), circular economy principles, and life-cycle thinking to improve the environmental sustainability of manufacturing processes. Pharmaceutical manufacturing is particularly solvent-intensive, with organic solvents contributing substantially to material consumption, energy demand, greenhouse gas emissions, and hazardous waste generation. Although green chemistry, solvent substitution, life-cycle assessment (LCA), and digital manufacturing have advanced sustainable process design, their integration into a unified environmental decision-support architecture remains insufficiently developed. This communication presents an Artificial Intelligence for Circular Solvent Economy (AICSE) framework that connects AI-assisted prediction, digital process simulation, circular solvent management, and life-cycle sustainability assessment within a structured environmental decision-support architecture. The framework retains Process Mass Intensity (PMI), Circular Process Mass Intensity (cPMI), Global Warming Potential (GWP), and Cumulative Energy Demand (CED) as complementary sustainability indicators within an LCA-oriented assessment structure. A literature-based Sertraline active pharmaceutical ingredient (API) manufacturing process is used as an illustrative application to demonstrate how the proposed architecture can organize solvent evaluation, virtual process assessment, circular solvent recovery, and environmental decision-making. Building upon our previously reported solventcentric sustainability framework, this work extends its scope by explicitly integrating Environmental Artificial Intelligence, life-cycle thinking, Digital Twin, and circular economy principles into a transferable digital sustainability architecture for solvent-intensive manufacturing. In this work, Environmental Artificial Intelligence (Environmental AI) refers to the application of AI and machine-learning methods to environmental prediction, assessment, optimization, monitoring, and decision support within manufacturing systems. The principal contribution is architectural rather than algorithmic: AICSE connects previously independent components into a structured environmental decision-support pathway and introduces implementation-oriented readiness and maturity models. The framework is presented as a conceptual architecture supported by an illustrative literature-based application rather than as an empirical validation study. AICSE provides a practical pathway toward evidence-based sustainability governance, resource-efficient manufacturing, and progressively integrated environmental intelligence.
ER -