Data Reconciliation under Fuzzy Constraints in Material Flow Analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least squares minimization. Here we show that the setting of fuzzy sets provides a generalized approach that is more flexible and less dependent on oftentimes debatable probabilistic justifications. Moreover the proposed setting also encompasses constraint-based formulations using intervals.