Modeling Material Flow Dynamics in Industrial-Natural Systems: Machine Learning and Causal Analysis for Resilience Evaluation and Sensor Minimization

This dissertation builds a unified toolkit for analyzing coupled industrial-natural systems through four integrated advances. First, it extends Sparse Identification of Nonlinear Dynamics (SINDy) by adding higher-order input derivatives, allowing parsimonious surrogate models for both chemical transesterification and watershed hydrology while retaining physical interpretability​​. Second, it introduces a hybrid strategy that injects SINDy-derived error terms into established Cardinal Temperature Models, sharply improving microalgal growth forecasts without sacrificing mechanistic meaning​​. Third, it develops a causality-guided sensor-minimization method that combines Liquid Time-Constant neural networks with perturbation analysis to identify the smallest measurement set that still reconstructs system states; the approach is validated across mechanical, chemical, and ecological testbeds​​. Fourth, the work integrates these node-level surrogates into a material-flow network simulator to evaluate resilience of a soybean-biodiesel supply chain under RCP 4.5 and 8.5 climate scenarios, revealing nonlinear production failures, recovery dynamics, and tipping thresholds linked to farm size and climate forcing​​. Collectively, the framework delivers interpretable equations for planners, lightweight soft sensors for operators, and quantitative resilience metrics for decision-makers—providing a transferable blueprint for sustainability assessment in complex material-flow networks.