Open-Source Modelling Tools in Chemical Engineering: Opportunities and Adoption in Malawi and Africa
DOI:
https://doi.org/10.47941/ijce.3492Keywords:
Open-source modelling, Chemical engineering education, Computational modelling tools, Decision-support framework, Resource-constrained institutionsAbstract
Purpose: Computational modelling is central to chemical engineering education, research, and process design, yet sustained access to modelling capabilities in many low-resource institutions remains limited by high licensing costs and dependence on proprietary software ecosystems. This study examines the potential of open-source modelling tools to provide technically robust and institutionally sustainable alternatives, addressing persistent gaps in tool selection, curriculum integration, and long-term adoption. Methodology: A systematic review and synthesis of open-source computational modelling tools across molecular, continuum, and process scales is conducted. Based on this analysis, a decision tree is developed to link modelling objectives and physical-fidelity requirements to appropriate open-source tools. In parallel, a decision-driven institutional adoption framework is proposed to guide phased implementation in resource-constrained chemical engineering environments. Findings: The review shows that mature open-source tools now exist across the full modelling hierarchy, enabling core chemical engineering workflows without reliance on proprietary platforms. The proposed decision tree supports transparent and reproducible software selection, while the adoption framework highlights the central role of infrastructure readiness, skills development, curriculum maturity, and governance in sustaining open-source uptake. Explicit decision points and feedback loops are identified as critical for managing heterogeneous infrastructure and evolving human capacity. Unique contribution to theory, practice and policy: This work delivers an integrated, decision-based approach to open-source modelling adoption in chemical engineering, linking technical capability with institutional capacity building. It provides actionable guidance for educators and institutions seeking equitable and sustainable digital modelling ecosystems, with relevance beyond the Malawian and Sub-Saharan African context.Downloads
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