Applying the significant-digit law to simplify grading of chemical engineering students design projects

Evaluating capstone design reports in chemical engineering is a big academic challenge because the student data sets are very complex. These reports usually contain many calculations: material and energy balances, unit‑operation sizing, process‑control logic, and economic feasibility studies. Students rely heavily on spreadsheets and simulation software, so checking every number by hand takes a lot of time. This project uses a systematic framework based on the significant‑digit rule, also called Benford’s Law, to speed up data validation. By looking at how often each leading digit appears in the students’ design calculations, the method gives a quick statistical check for odd results. When the observed digit distribution strays from the expected

logarithmic pattern, it can point to calculation mistakes, data tampering, or inconsistent simulation settings. The statistical verification model provides a reliable, automated way to audit complex chemical‑process design spreadsheets. It cuts down the workload for evaluators while still meeting strict academic and professional standards. The project also includes practical guidance for building Python tools that parse numbers from design spreadsheets and run automated compliance checks.

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