Non-linear Model-Based Process Control:  Applications in Petroleum Refining

  Rashid M. Ansari, BEng, MEng, MEngSc, PhD CEng FIChemE CPEng FIEAust. MAIChE

Senior Advanced Process Control Engineer, Saudi Aramco

Moses O. Tadé, BEng (Hons), PhD CEng FIChemE CPEng MIEAust.

Professor, Curtin University Australia

The work in this book entails the development of non-linear model-based multivariable control algorithms and strategies and their use in an integrated approach to control strategy, which incorporates a process model, an inferential model and a multivariable control algorithm in one framework. This integrated approach has been applied to various refinery processes that exhibit strong non-linearities, process interactions and constraints and has been shown to produce good results by improving closed-loop quality control and maximizing the yield of high-value products. The non-linear model-based control structure is further extended to permit the use of inferential models in non-linear multivariable control applications. A wide range of inferential models has been developed, implemented in real-time and integrated with non-linear multivariable control applications. These inferential models demonstrate the improvement in the performance of closed-loop quality control and the dynamic response of the system in reducing long time delays. A complex multivariable control problem is solved by formulating the non-linear, constrained optimisation strategy for a crude distillation and a semi-regenerative catalytic reforming process. A non-linear constrained optimization strategy is proposed and applied to a fluid catalytic cracking reactor-regenerator section using a simplified fluid-catalytic-cracking-process model. A dynamic parameter update algorithm is developed and used to reduce the effect of larger modelling errors by updating the selected model parameters regularly.

This book was brought about, primarily, in response to industrial interest in the improvement of operating efficiency and profitability using the non-linear model-based technology, which it discusses. A second motivation of more academic interest was the implementation of model-based methods in real-time for control of complex processes with strong non-linearities and process interactions and a third, more practical, was the reduction of the gap between theoretical work and the industrial practice of advanced process control.

Contents: 1. Introduction 2. Literature Review 3. Inferential Models in Non-linear Multivariable Control Applications 4. Non-linear Model-based Multivariable Control of a Debutanizer 5. Non-linear Model-based Control of a Crude Distillation Process 6. Constrained Non-linear Multivariable Control of a Catalytic Reforming Process 7. Non-linear Multivariate Control of a Fluid Catalytic Cracking Process 8. Conclusions and Recommendations