The goal of this project is to develop statistical techniques to monitor problems encountered during the operation of chemical plants. In the past, process measurements and data acquisition were carried out by plant operators and engineers who checked the accuracy and consistency of the data based on their knowledge and experience. These functions are now performed automatically by computers and techniques to properly analyze this data are needed. The researchers will develop algorithms that reconcile measurement differences, estimate unmeasured variables, detect and identify gross errors, and efficiently monitor the process of interest. Process measurements are subject to two types of errors: random errors which are commonly assumed to be normally distributed with a mean of 0; and gross errors which are caused by non-random events such as equipment failure (e.g. leaks) as well as measurement errors due to malfunctioning instruments. Gross errors have to be eliminated before data reconciliation is attempted.