Enzymatic　biodiesel　production　systems　represent　complex　chemical　dynamical　processes　that　traditional　modeling　approaches　struggle　to　effectively　capture　the　inherent　nonlinearity　and　dynamics.　We　propose　a　novel　framework　that　fuses　prior　physical　knowledge　and　data-driven　methods　to　enhance　parameter　inference　capabilities　for　such　systems.　Grounded　in　the　concept　of　physics-informed　machine　learning　(PIML),　we　combine　the　prior　knowledge　from　traditional　physical　models　with　the　powerful　approximation　abilities　of　data-driven　neural　networks.　This　framework　enables　the　physical　laws　to　guide　the　training　of　the　data-driven　model　while　utilizing　data　to　correct　model　biases,　achieving　tight　integration　of　physical　constraints　and　data-　driven　approaches　in　the　modeling　process.　To　address　potential　challenges　faced　by　PIML　methods　in　practical　applications,　such　as　multiscale　features　and　violations　of　time　causality,　we　introduce　several　improved　strategies.　Evaluating　our　framework　on　real　experimental　data　from　biodiesel　production,　we　validate　its　accurate　estimation　of　system　parameters.　This　work　provides　a　promising　new　approach　to　enhance　the　interpretability　and　generalization　of　data-driven　modeling　by　incorporating　prior　physical　knowledge,　holding　potential　applications　in　modeling　and　control　of　complex　dynamical　systems.