Dynamic data reconciliation for sequential modular simulators: application to a mixing process

This paper describes a method for dynamic data reconciliation of nonlinear systems that are simulated using the sequential modular approach, and where individual modules are represented by a class of differential algebraic equations. The estimation technique consists of a bank of extended Kalman filters that are integrated with the modules. The paper reports a study based on experimental data obtained from a pilot scale mixing process.

Optimal control of nonlinear systems represented by differential algebraic equations

An iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified simplified model based problem with parameter updating in such a manner that the correct solution of the original nonlinear problem is achieved.

Model predictive control using dynamic integrated system optimisation and parameter estimation (DISOPE)

DISOPE is a technique for solving optimal control problems where there are differences in structure and parameter values between reality and the model employed in the computations. The model reality differences can also allow for deliberate simplification of model characteristics and performance indices in order to facilitate the solution of the optimal control problem. The technique was developed originally in continuous time and later extended to discrete time. The main property of the procedure is that by iterating on appropriately modified model based problems the correct optimal solution is achieved in spite of the model-reality differences. Algorithms have been developed in both continuous and discrete time for a general nonlinear optimal control problem with terminal weighting, bounded controls and terminal constraints. The aim of this paper is to show how the DISOPE technique can aid receding horizon optimal control computation in nonlinear model predictive control.

Dynamic data reconciliation for sequential modular simulators

In this paper the implementation of dynamic data reconciliation techniques for sequential modular models is described. The paper is organised as follows. First, an introduction to dynamic data reconciliation is given. Then, the online use of rigorous process models is introduced. The sequential modular approach to dynamic simulation is briefly discussed followed by a short review of the extended Kalman filter. The second section describes how the modules are implemented. A simulation case study and its results are also presented.

New developments in batch process input profile optimisation

A novel algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses dynamic integrated system optimisation and parameter estimation (DISOPE) which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimisation procedure. A new method for approximating some Jacobian trajectories required by the algorithm is introduced. It is shown that the iterative procedure associated with the algorithm naturally suits applications to batch chemical processes.

A new approach for the solution of the optimal set-point tracking problem for nonlinear systems

In this paper, a discrete time dynamic integrated system optimisation and parameter estimation algorithm is applied to the solution of the nonlinear tracking optimal control problem. A version of the algorithm with a linear-quadratic model-based problem is developed and implemented in software. The algorithm implemented is tested with simulation examples.

Development and application of a novel algorithm for steady-state optimising control using dynamic information

A novel optimising controller is designed that leads a slow process from a sub-optimal operational condition to the steady-state optimum in a continuous way based on dynamic information. Using standard results from optimisation theory and discrete optimal control, the solution of a steady-state optimisation problem is achieved by solving a receding-horizon optimal control problem which uses derivative and state information from the plant via a shadow model and a state-space identifier. The paper analyzes the steady-state optimality of the procedure, develops algorithms with and without control rate constraints and applies the procedure to a high fidelity simulation study of a distillation column optimisation.

Hierarchical control and optimisation of the Tennessee Eastman process

Based on integrated system optimisation and parameter estimation a method is described for on-line steady state optimisation which compensates for model-plant mismatch and solves a non-linear optimisation problem by iterating on a linear - quadratic representation. The method requires real process derivatives which are estimated using a dynamic identification technique. The utility of the method is demonstrated using a simulation of the Tennessee Eastman benchmark chemical process.

Optimal control of nonlinear differential algebraic equation systems

A novel iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified linear quadratic model based problem with parameter updating in such a manner that the correct solution of the original non-linear problem is achieved. The resulting algorithm has a particular advantage in that the solution is achieved without the need to solve the differential algebraic equations . Convergence aspects are discussed and a simulation example is described which illustrates the performance of the technique. 1. Introduction When modelling industrial processes often the resulting equations consist of coupled differential and algebraic equations (DAEs). In many situations these equations are nonlinear and cannot readily be directly reduced to ordinary differential equations.

Analysis of aluminium content and iron homeostasis in nipple aspirate fluids from healthy women and breast cancer-affected patients

Aluminium is not a physiological component of the breast but has been measured recently in human breast tissues and breast cyst fluids at levels above those found in blood serum or milk. Since the presence of aluminium can lead to iron dyshomeostasis, levels of aluminium and iron-binding proteins (ferritin, transferrin) were measured in nipple aspirate fluid (NAF), a fluid present in the breast duct tree and mirroring the breast microenvironment. NAFs were collected noninvasively from healthy women (NoCancer; n = 16) and breast cancer-affected women (Cancer; n = 19), and compared with levels in serum (n = 15) and milk (n = 45) from healthy subjects. The mean level of aluminium, measured by ICP-mass spectrometry, was significantly higher in Cancer NAF (268.4 ± 28.1 μg l−1; n = 19) than in NoCancer NAF (131.3 ± 9.6 μg l−1; n = 16; P < 0.0001). The mean level of ferritin, measured through immunoassay, was also found to be higher in Cancer NAF (280.0 ± 32.3 μg l−1) than in NoCancer NAF (55.5 ± 7.2 μg l−1), and furthermore, a positive correlation was found between levels of aluminium and ferritin in the Cancer NAF (correlation coefficient R = 0.94, P < 0.001). These results may suggest a role for raised levels of aluminium and modulation of proteins that regulate iron homeostasis as biomarkers for identification of women at higher risk of developing breast cancer. The reasons for the high levels of aluminium in NAF remain unknown but possibilities include either exposure to aluminium-based antiperspirant salts in the adjacent underarm area and/or preferential accumulation of aluminium by breast tissues.