Enso Ikonen

Neuro-fuzzy modelling of power plant flue-gas emissions

This paper concerns process modelling using fuzzy neural networks. In distributed logic processors (DLP) the rule base is parameterised. The DLP derivatives required by gradient-based training methods are given, and the recursive prediction error method is used to adjust the model parameters. The power of the approach is illustrated with a modelling example where NOx-emission data from a full-scale fluidised-bed combustion district heating plant are used. The method presented in this paper is general, and can be applied to other complex processes as well.

Forecasting time series with a new architecture for polynomial artificial neural network

Polynomial artificial neural networks (PANN) have been shown to be powerful for forecasting nonlinear time series. The training time is small compared to the time used by other algorithms of artificial neural networks and the capacity to compute relations between the inputs and outputs represented by every term of the polynomial. In this paper a new structure of polynomial is presented that improves the performance of this type of network considering only non-integers exponents. The architecture adaptation uses genetic algorithm (GA) to find the optimal architecture for every example. Some examples of sunspots and chaotic time series are presented.

Oxidant control and air-oxy switching concepts for CFB furnace operation

Abstract Oxy combustion in circulating fluidized bed (CFB) boilers was investigated in this paper. Oxy combustion is a carbon capture and storage technology, which uses oxygen and recirculated flue gas (RFG) instead of air as an oxidant. Air and oxy combustion were compared through physical considerations and simulations, focusing on process dynamics, transients and control. The oxidant specific heat capacity and density are elevated in oxy combustion, which leads to slower temperature dynamics. Flue gas recirculation introduces internal feedback dynamics to the process. The possibility to adjust the RFG and oxygen flows separately gives an additional degree of freedom for control. In the simulations, “direct” and “sequenced” switches between air- and oxy-firing were compared. Fast “direct” switches with simultaneous ramping of all inputs should be preferred due to the resulting smooth temperature responses. If these process input changes are unfeasible, the fuel should be altered after the gaseous flows (“sequenced” method).

Dynamic model for a bubbling fluidized bed coal combustor

Abstract The bed fuel inventory is one of the key variables in the fluidized bed combustion process when describing the dynamic behaviour of combustors burning fuels like coal or graphite. Many of the essential features of fluidized bed combustor responses can be simulated with the dynamic model presented. The model consists of differential equations describing the dynamic behaviour of the furnace and steady-state calculations for the burning of char particles. The simulations show good agreement when compared to process measurements from a commercial-sized coal combustor.

Learning Control of Thermal Systems

Thermal processes are common in industry. The systems are often quite complex because of the phenomena involved and the sizes of the plants. This Chapter shows how learning techniques can be used for such complex systems.

Open-loop regulation and tracking control based on a genealogical decision tree

The goal of this paper is to design a new control algorithm for open-loop control of complex systems. This control approach is based on a genealogical decision tree for both regulation and tracking control problems. The idea behind this control strategy consists of associating Gaussian distributions to both the norms of the control actions and the tracking errors. This stochastic search model can be interpreted as a simple genetic particle evolution model with a natural birth and death interpretation. It converges on probability. A numerical example dealing with the control of a fluidized bed combustion power plant illustrates the feasibility and the performance of this control algorithm.

Adaptive selection of the optimal order of linear regression models using learning automata

This paper concerns the adaptive selection of the optimal order of linear regression models using a variable-structure stochastic learning automaton. The Alaike criterion is derived for stationary and non-stationary cases, and it is shown that the optimal order minimizes a loss function corresponding to the evaluation of this criterion. The order of the regression model belongs to a finite set. Each order value is associated with an action of the automaton. The Bush-Mosteller reinforcement scheme with normalized automaton input is used to adjust the probability distribution. Simulation results illustrate the feasibility and performance of this model order selection approach

Short term optimization of district heating network supply temperatures

The increasing challenges in district heating operational optimization are briefly discussed. The paper describes the first steps in a research project on minimization of short term operational costs in a full scale district heating system. Based on a test model describing a part of a real district heating network, and a chosen approximate dynamic programming technique, simulations are used to illustrate and validate the fundamentals of the modelling and optimization approaches. It is concluded that the considered methods provide an adequate set of tools for the design of optimal network loading. The project is currently continuing with building of a more realistic dynamic model of the full-scale energy network and its components.

Multiple Model-Based Control Using Finite Controlled Markov Chains

Cognition and control processes share many similar characteristics, and decisionmaking and learning under the paradigm of multiple models has increasingly gained attention in both fields. The controlled finite Markov chain (CFMC) approach enables to deal with a large variety of signals and systems with multivariable, nonlinear, and stochastic nature. In this article, adaptive control based on multiple models is considered. For a set of candidate plant models, CFMC models (and controllers) are constructed off-line. The outcomes of the CFMC models are compared with frequentist information obtained from on-line data. The best model (and controller) is chosen based on the Kullback–Leibler information. This approach to adaptive control emphasizes the use of physical models as the basis of reliable plant identification. Three series of simulations are conducted: to examine the performace of the developed Matlab-tools; to illustrate the approach in the control of a nonlinear nonminimum phase van der Vusse CSTR plant; and to examine the suggested model selection method for the adaptive control.

A Genealogical Decision Tree Solution to Optimal Control Problems

Abstract A new control algorithm for open-loop control of complex systems is suggested. The approach is based on a genealogical decision tree for tracking control problems. The idea behind this control strategy consists of associating Gaussian distributions to both the norms of the control actions and the tracking errors. This stochastic search model can be interpreted as a simple genetic particle evolution model with a natural birth and death interpretation. It converges in probability. A numerical example illustrates the feasibility and the performance of this control algorithm.