State and Parameter Estimation in Dynamic Real-time Optimization with Closed-Loop Prediction

Coordination of distributed MPC systems using a nonlinear dynamic plant model with closed-loop prediction

Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

Process Dynamic Optimization Using ROMeo

A dynamic real-time optimization (D-RTO) methodology has been developed and applied to a batch reactor where polymer grafting reactions take place. The objective is to determine the on-line reactor temperature profile that minimizes the batch time while meeting terminal constraints on the overall conversion rate and grafting efficiency. The methodology combines a constrained dynamic optimization method and a moving horizon state estimator within a closed-loop control. The results show very good performances in terms of state estimation, constraints fulfillment and computation load.

Dynamic Real-Time Optimization of Air-Conditioning Systems in Residential Houses with a Battery Energy Storage under Different Electricity Pricing Structures

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

Dynamic real-time optimization for nonlinear systems with Lyapunov stabilizing MPC

Distributed real-time dynamic cooperative optimization with unknown performance function form under resources constraints.

Problem formulations and algorithms are considered for optimization problems with differential-algebraic equation (DAE) models. In particular, we provide an overview of direct methods, based on nonlinear programming (NLP), and indirect, or variational, methods. We further classify each method and tailor it to the appropriate applications. For direct methods, we briefly describe current approaches including the sequential approach (or single shooting), multiple shooting method, and the simultaneous collocation (or direct transcription) approach. In parallel to these strategies we discuss NLP algorithms for these methods and discuss optimality conditions and convergence properties. In particular, we present the simultaneous collocation approach, where both the state and control variable profiles are discretized. This approach allows a transparent handling of inequality constraints and unstable systems. Here, large scale nonlinear programming strategies are essential and a novel barrier method, called IPOPT, is described. This NLP algorithm incorporates a number of features for handling large-scale systems and improving global convergence. The overall approach is Newton-based with analytic second derivatives and this leads to fast convergence rates. Moreover, it allows us to consider the extension of these optimization formulations to deal with nonlinear model predictive control and real-time optimization. To illustrate these topics we consider a case study of a low density polyethylene (LDPE) reactor. This large-scale optimization problem allows us to apply off-line parameter estimation and on-line strategies that include state estimation, nonlinear model predictive control and dynamic real-time optimization.

State and Parameter Estimation in Dynamic Real-Time Optimization with Embedded MPC

Unlike steady-state optimization and linear model predictive control, dynamic optimization and nonlinear model predictive control is underway to industry application. This paper discusses the implementation issues of two-level control strategy integrated dynamic real-time optimization and nonlinear model predictive control of a heat-integrated distillation column system. To realize this scheme, four nonlinear programming problems: economic operation optimization, data reconciliation and state estimation, parameter estimation and nonlinear model predictive control, are considered based on system rigorous dynamic model. The economic operation optimization is to minimize the cost of the plant. The unmeasured states are estimated by measurements of temperatures, pressures and flow rates. Time-variant parameters such as tray efficiencies and feed composition are updated on-line. The sequential quadratic programming and orthogonal collocation are used to solve the dynamic nonlinear optimization problems.

Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs

Application of Economics Optimizing Control to a Two-step Transesterification Reaction in a Pilot-Scale Reactive Distillation Column

In this paper, the set-membership state and parameter estimation problems are considered for linear time-varying systems. The noises in the system are unknown but bounded and the variation of parameters is considered as a bounded expansion. The entire estimator is an interactive estimation of parameters and states. In parameter estimation, the exact set of parameter estimates may be non-convex because of the uncertain states. A polytopic linear parameter varying (LPV) enclosure of the system regression expression is constructed as a convex relaxation. The result of parameter estimation is a set to include the true parameter of the model. Meanwhile, in the state estimation, a similar LPV enclosure of the state-space model is also used to bound the state matrix uncertainties. This LPV enclosure in the state estimation is narrowed by the parameter estimation, and then the set of state estimates is computed to include the true state. The constrained zonotope (CZ) is used to describe sets of interest and the necessary set operations are expanded to LPV mapping. Because of the highly adjustable accuracy of CZ, the proposed algorithm can make a better trade-off between computational accuracy and efficiency. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC