Application of stochastic search for gross error detection and data reconciliation

Gross error detection and data reconciliation are important problems in operating chemical plants. Typically, constrained nonlinear optimization techniques combined with statistical methods are used to solve these problems. In this study, we explore the use of stochastic search for these purposes. One significant advantage of the method is that it does not depend on any model structure information and only needs simple algebraic calculation. Therefore, it is especially suitable for gross error detection and data reconciliation of complicated connected processes.