Abdelrahman Noureldin Idris, E: Optimal Process Operation by

Nonlinear model predictive control (NMPC) is perhaps the most advanced feedback control technique that introduced a significant impact on the industrial chemical processes since it is capable to optimize and control the system simultaneously. Its abilities to handle multivariable control systems, to incorporate constraints into the control problem formulation and to enable the integration of economics optimizing criteria into the feedback control design are the most outstanding advantages among many others.Different nonlinear model predictive techniques were applied successfully to different control structures of a simulated nonlinear continuous stirred tank reactor with feed concentration disturbances and sensor noise, and to different control structures of a simulated pilot-scale continuous catalytic distillation column with plant-model mismatch and input and output noise. Both of the considered case studies exhibit complex phenomena such as nonlinearity, non-minimum phase behaviour (NMP), input multiplicity and local minima. Input multiplicities for instance may lead to multiple solutions and to wrong control actions and the steady-state may be shifted to a sub-optimal operating point during the operation of the plant when the standard class of controllers with a regulation objective (conventional reference tracking NMPC) is used.Economics optimizing NMPC controllers to optimize and control the operations of a non-minimum phase continuous stirred tank reactor (CSTR) and the operations of a complex dynamics pilot-scale continuous catalytic distillation (CCD) process are intensively investigated. This direct economic optimization scheme is compared with a compromise scheme, the economics-oriented tracking controller. Different control structures are used to explore the potential benefits of the dynamic online economicsbased nonlinear control strategies. Online state and disturbance estimation is an integral part of the investigated NMPC control schemes particularly