Vladimir Havlena

Combustion optimization with inferential sensing

The paper presents development and implementation of an advanced combustion controller (ACC) for a coal-fired boiler. The solution consists of combustion controller (multi-variable predictive controller) and combustion optimizer (cautious strategy stochastic optimizer). Optimization is based on a model of the CO and NO/sub x/ emissions. The model is used to calculate the setpoint of the optimal air/fuel ratio(s) maximizing the efficiency of the plant under constraints given by emission limits.

A DISTRIBUTED AUTOMATION FRAMEWORK FOR PLANT-WIDE CONTROL, OPTIMISATION, SCHEDULING AND PLANNING

Abstract The objective of the talk will be to identify current open problems and trends in plant wide control and demonstrate a solution based on distributed, solution component based architecture for integrated process management, embracing the layers of Advanced Process Control, Real Time Optimisation and Planning & Scheduling, in selected application areas. The problems and outlined solutions are intended to stimulate discussion as well as attract more research interest.

Structured Model Order Reduction of Parallel Models in Feedback

Parallel working units in closed-loop operation are frequently encountered in industrial applications of advanced process control (boilers, turbines, chemical reactors, etc.). Control strategies typically require different low-order models for each configuration of parallel units. These different models are usually obtained by heuristics applied to the parallel models. To replace these heuristics, this paper proposes a systematic solution based on structured model order reduction. Two methods are considered, the first has general applicability to stable closed-loop systems, but gives no a priori error bounds; the second linear matrix inequality (LMI)-based method comes with an explicit error bounds, but cannot be applied to general models. However, it is shown that for models composed of cascades of stable subsystems and negative feedbacks of strictly positive real subsystems, the LMIs are always feasible. Both methods are demonstrated on a practical example of a cogeneration power plant with multiple boilers. It is proved that the second LMI-based method can always be applied to general problems with structures similar to the boiler-header systems considered in this paper.

Application of MPC to Advanced Combustion Control

Abstract The objective of application of model-based predictive control technology for boiler control is to enable tight dynamical coordination of selected controlled variables, particularly the coordination of air and fuel during the transients. It is shown that this approach can be used in connection with excess air optimization to increase the efficiency by at least 1% while considerably reducing the production of NOx.

Optimality Tests and Adaptive Kalman Filter

Abstract Kalman filter tuning is based on the process and measurement noise covariances that are often obtained by ad hoc methods. After the filter is tuned, it is necessary to evaluate the quality of the state estimation. In this article, several methods are described for the quality evaluation of the Kalman filter performance. The article includes simulation results evaluating the reliability of the described optimality tests. The sequential test is then used for an adaptive algorithm for a Kalman filter. Further, properties of an autocorrelation function are discussed and several methods for its estimation are compared..

Plant Energy Management

In the IMC-AESOP project, a plant energy management use case was developed to highlight advantages of service orientation, event-driven processing and information models for increased performance, easier configuration, dynamic synchronisation and long-term maintenance of complicated multi-layer solutions, which are deployed nowadays in the continuous process plants. From the application perspective, three scenarios were implemented including advanced control and real-time optimisation of an industrial utility plant, enterprise energy management enabling interactions with the external electricity market, and advanced alarm management utilizing the Complex Event Processing technology.

Structured Model Order Reduction of Boiler-Header Models

Abstract This paper presents a model reduction of a boiler-header system. Since it is desirable that the reduced model retains the structure of the full model where the boilers are interconnected with the header, a structured model reduction technique is applied, which takes the entire system into account. This method requires the solution of two linear matrix inequalities to obtain the structured Gramians of the system, but in general it is not possible to guarantee feasibility of these linear matrix inequalities. However for stable systems that are connected in series with a negative feedback-loop with strictly positive real subsystems, we prove that solutions always exist. By showing that the boiler-header system belongs to this class of systems it follows that the structured model reduction method can be applied regardless of the system parameters.

Application of Distributed MPC to Barcelona Water Distribution Network

Abstract The paper presents application of Distributed Model Predictive Control (DMPC) schemes to complex system of Barcelona water distribution network. The dual decomposition of convex optimization problems is well known and has been already adopted to DMPC. However, the application of dual based DMPC to truly large scale systems requires efficient algorithms for consensus iterations. The paper treats DMPC with and without centralized coordinator. The non-centralized coordination is based on Nesterov accelerated gradient method and centralized coordination is based on limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method.

PLATFORM FOR ADVANCED CONTROL APPLICATIONS

Abstract Control applications have many requirements not provided by commercial operating systems. This paper describes the characteristics and usage of an environment for hosting process-control applications, which is implemented on a commercial operating system. It is called Unified Real Time (URT) platform, and is intended for applications that are large or complex and that may involve dynamic configuration, flexible scheduling, complex organization, etc. This paper also demonstrates the structure of a typical Advanced Control Application (ACA) designed under URT.

Grey-box model identification – control relevant approach

Abstract Grey-box modeling is an advantageous tool for system identification when obtained input/output experimental data are insufficiently excited. The lack of information in the data can be often replaced with some additional knowledge about the modeled system, which constricts the class of models under consideration. The real system is usually more complex and do not fit the model class, thus a bias error occurs. The main goal of this paper is to show an effective way how to identify grey-box models, which would be relevant in commissioning predictive control.