Xiangzhong Xie

Robust Design of Chemical Processes Based on a One-Shot Sparse Polynomial Chaos Expansion Concept

Abstract The application of robust model-based design concepts for complex chemical processes is limited due to the repeated cpu-intensive uncertainty quantification step for any new tested process design configuration. Therefore, an efficient One-Shot Sparse Polynomial Chaos Expansion (OS 2 -PCE) based process design framework is introduced in this work. The key idea is to define the process design variables as uncertain quantities as well and, in consequence, they become an integral part of the robust optimization routine. Moreover, by utilizing the sparsity feature of the PCE approach, the implementation of a least angle regression (LAR) concept leads to a significant reduction in computational costs. The overall performance of the novel OS 2 -PCE approach is illustrated by a robust process design study of a jacketed tubular reactor. In comparison to state-of-the-art concepts, the proposed framework shows promising results in terms of efficiency and robustness.

An Efficient Polynomial Chaos Expansion Strategy for Active Fault Identification of Chemical Processes

Abstract To gain profit from complex chemical processes, it is essential to ensure its proper operation, i.e. to avoid costly unexpected downtimes of underlying processing units. This paper explores a highly efficient active fault detection and isolation (FDI) framework, which facilitates the discriminability of a set of analysed model candidates including the reference model (nominal behaviour) as well as pre-defined failure models (faulty behaviour). Practically, an auxiliary, model-discriminating input is derived by solving a dynamic optimization problem. While using a model-based approach, the active FDI implementation has to be robustified against the inherent model parameter uncertainties. To this end, a non-intrusive polynomial chaos expansion (PCE) is used to address these uncertainties. To guarantee a computationally feasible performance, the original PCE setting has been considerably improved. Here, the basic idea is to render the design variables (auxiliary inputs) into random variables as well. Thus, the derived PCE results are not only sensitive to the model parameters but also to the design variables. To lower the computational burden further, a least angle regression strategy is applied utilizing the sparsity property of the PCE approach. The overall effectiveness of this One-Short Sparse Polynomial Chaos Expansion (OS 2 -PCE) concept for FDI is illustrated conceptually by analysing a tubular plug flow reactor.