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Recent results in the development of efficient large-scale nonlinear programming (NLP) algorithms have led to fast, on-line realizations of optimization-based methods for nonlinear model predictive control (NMPC) and dynamic real-time optimization (D-RTO), with predictive nonlinear dynamic (e.g., first principle) models. For NMPC, optimization-based controllers are developed that lead to well-understood stability and robustness properties, even for large, complex plant models. The realization of NMPC requires the application of a fast NLP solver for time-critical, on-line optimization, as well as efficient NLP sensitivity tools that require 2–3 orders of magnitude less computation than the NLP solution. This leads to advanced step NMPC (asNMPC), which essentially eliminates computational delay. We also extend these capabilities to dynamic real-time optimization (D-RTO) with more general stage costs that are economically based. This overview also extends input to state stability (ISS) properties for asNMPC to handle active set changes, and also for D-RTO through convex regularizations. Two large scale distillation case studies, based on nonlinear first principle models, are presented that demonstrate the effectiveness of these approaches. [ABSTRACT FROM AUTHOR]

Gradient-based optimization may not be suited if the objective and constraint functions in a nonlinear model predictive control (NMPC) optimization problem are not differentiable. Some well-known derivative-free optimization (DFO)-algorithms are investigated, and a novel warm-start modification to the Wedge DFO-algorithm is proposed. Together with a gradient-based SQP-algorithm these are applied to the NMPC problem and compared in a single-shooting NMPC formulation to a subsea oil-gas separation process. The findings are that DFO is significantly more robust against the numerical issues, compared to a gradient-based SQP tested. Moreover, the warm-start modification reduces the computational complexity. [ABSTRACT FROM AUTHOR]

Abstract: The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools. The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed. [Copyright &y& Elsevier]

Abstract: In this paper, we present nonlinear model predictive control (NMPC) techniques to control and optimize the thermo-mechanical pulping (TMP) process. The TMP process considered in this work has two-stages of refining with a primary and a secondary refiners. TMP processes are, inherently, multi-input multi-output (MIMO) processes with complex dynamics and severe interactions among process variables. We formulate a dynamic optimization problem to simultaneously regulate and optimize the TMP process and compare with regulatory control in the presence of disturbances. Potential economical benefits of the proposed method are demonstrated, through a reduction in total specific energy. The computational burden of the resulting nonlinear programming problem (NLP) is handled with the IPOPT (Interior Point OPTimizer) solver. This is further improved with the advanced step NMPC (asNMPC) controller concept, a sensitivity-based approximation to the solution of the resulting NLP problem in NMPC. [Copyright &y& Elsevier]

Abstract: A kW-class molten carbonate fuel cell with direct internal reforming is presented. Within an additional preheating framework, the estimation and control technique is implemented to this kind of nonlinear systems. By the proposed algorithm, the steady-state optimization algorithm subject to prescribed physical constraints is used to determine the desired operating condition, an open-loop observer design provides effective state estimation, and the nonlinear feedback control ensures the bounded stack temperature regulation in spite of unknown inlet perturbations. Finally, the proposed control methodology is successfully verified by numerical simulations. [Copyright &y& Elsevier]

Abstract: This paper investigates constrained Bayesian state estimation problems by using a Particle Filter (PF) approach. Constrained systems with nonlinear model and non-Gaussian uncertainty are commonly encountered in practice. However, most of the existing Bayesian methods are unable to take constraints into account and require some simplifications. In this paper, a novel constrained PF algorithm based on acceptance/rejection and optimization strategies is proposed. The proposed method retains the ability of PF in nonlinear and non-Gaussian state estimation, while take advantage of optimization techniques in constraints handling. The performance of the proposed method is compared with other accepted Bayesian estimators. Extensive simulation results from three examples show the efficacy of the proposed method in constraints handling and its robustness against poor prior information. [Copyright &y& Elsevier]

Abstract: In this paper, a moving horizon state and parameter estimation scheme for chromatographic simulated moving bed SMB processes is proposed. The simultaneous state and parameter estimation is based on a high-order nonlinear SMB model which incorporates rigorous models of the chromatographic columns and the discrete shiftings of the inlet and outlet ports. The estimation is performed using sparse measurement information: the concentrations of the components are only measured at the two outlet ports (which are periodically switched from one column to the next) and at one fixed location between two columns. The goal is to reconstruct the full state of the system, i.e. the concentration profiles along all columns, and to identify critical model parameters reliably such that the estimated model can be used in the context of online optimizing control. The state estimation scheme is based upon a deterministic model within the prediction horizon, state noise is only present in the state and the parameters prior to and at the beginning of the horizon. By solving the optimization problem with a multiple-shooting method and applying a real-time iteration scheme, the computation times are such that the scheme can be applied online. Numerical simulations of a validated model for a separation problem with nonlinear isotherms of the Langmuir type demonstrate the efficiency of the algorithm. [Copyright &y& Elsevier]

Abstract: A new support vector machine based nonlinear predictive functional control design method has been developed and applied to an industrial coking furnace, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection compared with traditional PFC and PID control strategies. The nonlinear process is first treated into a linear part plus a nonlinear part, then a convergent overall linear predictive functional control law is designed. The method gives a direct and effective multi-step predicting method and uses linear methods to get the control law which avoids the complicated nonlinear optimization. Comparison results and application to the temperature control of the industrial heavy oil coking furnace are presented in the article showing the efficiency of the method. [Copyright &y& Elsevier]

Abstract: This paper presents a Nonlinear Model Predictive Control (NMPC) algorithm utilizing a deterministic global optimization method. Utilizing local techniques on nonlinear nonconvex problems leaves one susceptible to suboptimal solutions at each iteration. In complex problems, local solver reliability is difficult to predict and dependent upon the choice of initial guess. This paper demonstrates the application of a deterministic global solution technique to an example NMPC problem. A terminal state constraint is used in the example case study. In some cases the local solution method becomes infeasible, while the global solution correctly finds the feasible global solution. Increased computational burden is the most significant limitation for global optimization based online control techniques. This paper provides methods for improving the global optimization rates of convergence. This paper also shows that globally optimal NMPC methods can provide benefits over local techniques and can successfully be used for online control. [Copyright &y& Elsevier]

This paper investigates application of SQP optimization algorithms to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs. full space methods. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem. [Copyright &y& Elsevier]