Application of dynamic partial least squares to complex processes

Abstract

Multivariate statistical modelling and monitoring is an active area of research and development in both academia and industry. This is due to the economic and safety benefits that can be attained from the implementation of process modelling and monitoring schemes. Most industrial processes in the chemistry-using sector exhibit complex characteristics including process dynamics, non-linearity and changes in operational behaviour which are compounded by the occurrence of non-conforming data points. To date, modelling and monitoring methodologies have focussed on processes exhibiting one of the aforementioned characteristics. This Thesis considers the development and application of multivariate statistical methods for the modelling and monitoring of the whole process as well as individual unit operations with a particular focus on the complex dynamic nonlinear behaviour of continuous processes. Following a review of Partial Least Squares (PLS), which is applicable for the analysis of problems that exhibit high dimensionality and correlated/collinear variables, it was observed that it is inappropriate for the analysis of data from complex dynamic processes. To address this issue, a multivariate statistical method Robust Adaptive PLS (RAPLS) was proposed, which has the ability to distinguish between non-conforming data, i.e. statistical outliers and a process fault. Through the analysis of data from a mathematical simulation of a time varying and non-stationary process, it is observed that RAPLS shows superior monitoring performance compared to conventional PLS. The model has the ability to adapt to changes in process operating conditions without losing its ability to detect process faults and statistical outliers. A dynamic extension, RADPLS, using an autoregressive with exogenous inputs (ARX) representation was developed to model and monitor the complex dynamic and nonlinear behaviour of an Ammonia Synthesis Fixed-bed Reactor. The resultant model, which is resistant to outliers, shows significant improvement over other dynamic PLS based representations. The proposed method shows some limitations in terms of the detection of the fault for its full duration but it significantly reduces the false alarm rate. The RAPLS algorithm is further extended to a dynamic multi-block algorithm, RAMBDPLS, through the conjunction of a finite impulse response (FIR) representation and multiblock PLS. It was applied to the benchmark Tennessee Eastman Process to illustrate its applicability for the monitoring of the whole process and individual unit operations and to demonstrate the concept of fault propagation in a dynamic and nonlinear continuous system. The resulting model detects the faults and reduces the false alarm rate compared to conventional PLS.