Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Improving the robustness of the iterative solver in state-space modelling of guitar distortion circuitry

Iterative solvers are required for the discrete-time simulation of nonlinear behaviour in analogue distortion circuits. Unfortunately,these methods are often computationally too expensive for realtime simulation. Two methods are presented which attempt to reduce the expense of iterative solvers. This is achieved by applying information that is derived from the specific form of the non linearity.The approach is first explained through the modelling of an asymmetrical diode clipper, and further exemplified by application to the Dallas Rangemaster Treble Booster guitar pedal, which provides an initial perspective of the performance on systems with multiple nonlinearities.

Dynamic Multivariate Statistical Process Control using Subspace Identification

This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Identification of dynamic systems under closed-loop control

This paper introduces a novel modelling framework for identifying dynamic models of systems that are under feedback control. These models are identified under closed-loop conditions and produce a joint representation that includes both the plant and controller models in state space form. The joint plant/controller model is identified using subspace model identification (SMI), which is followed by the separation of the plant model from the identified one. Compared to previous research, this work (i) proposes a new modelling framework for identifying closed-loop systems, (ii) introduces a generic structure to represent the controller and (iii) explains how that the new framework gives rise to a simplified determination of the plant models. In contrast, the use of the conventional modelling approach renders the separation of the plant model a difficult task. The benefits of using the new model method are demonstrated using a number of application studies.

Six ways to leave unemployment

This paper uses a unique Portuguese dataset to examine the effect of access to unemployment benefits (UBs) and their maximum potential duration on escape rates from unemployment. In examining the time profile of transitions out of unemployment, the principal contributions of the paper are twofold. First, it provides a detailed state space of potential outcomes: open-ended employment, fixed-term contracts, part-time work, government-provided jobs, self employment, and labour force withdrawal. Second, it is able to exploit major exogenous discontinuities in the maximum duration of unemployment benefits to identify disincentive effects. While confirming strong disincentive effects, it is shown that use of an aggregate hazard function regression model compounds very different and even contradictory effects of the determinants of unemployment.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

The Effects of Anti-Hypertensive Drugs Evaluated Using Markov Modelling for Northern Ireland Chronic Kidney Disease Patients

The aim of this paper is to use Markov modelling to
investigate survival for particular types of kidney patients
in relation to their exposure to anti-hypertensive treatment
drugs. In order to monitor kidney function an intuitive three
point assessment is proposed through the collection of blood
samples in relation to Chronic Kidney Disease for Northern
Ireland patients. A five state Markov Model was devised
using specific transition probabilities for males and
females over all age groups. These transition probabilities
were then adjusted appropriately using relative risk scores
for the event death for different subgroups of patients. The
model was built using TreeAge software package in order to
explore the effects of anti-hypertensive drugs on patients.

Exploiting spatio-temporal constraints for robust 2D pose tracking

We present a Spatio-temporal 2D Models Framework (STMF) for 2D-Pose tracking. Space and time are discretized and a mixture of probabilistic "local models" is learnt associating 2D Shapes and 2D Stick Figures. Those spatio-temporal models generalize well for a particular viewpoint and state of the tracked action but some spatio-temporal discontinuities can appear along a sequence, as a direct consequence of the discretization. To overcome the problem, we propose to apply a Rao-Blackwellized Particle Filter (RBPF) in the 2D-Pose eigenspace, thus interpolating unseen data between view-based clusters. The fitness to the images of the predicted 2D-Poses is evaluated combining our STMF with spatio-temporal constraints. A robust, fast and smooth human motion tracker is obtained by tracking only the few most important dimensions of the state space and by refining deterministically with our STMF.

Complex organic molecules in protoplanetary disks

Context. Protoplanetary disks are vital objects in star and planet formation, possessing all the material, gas and dust, which may form a planetary system orbiting the new star. Small, simple molecules have traditionally been detected in protoplanetary disks; however, in the ALMA era, we expect the molecular inventory of protoplanetary disks to significantly increase.

Aims. We investigate the synthesis of complex organic molecules (COMs) in protoplanetary disks to put constraints on the achievable chemical complexity and to predict species and transitions which may be observable with ALMA.

Methods. We have coupled a 2D steady-state physical model of a protoplanetary disk around a typical T Tauri star with a large gas-grain chemical network including COMs. We compare the resulting column densities with those derived from observations and perform ray-tracing calculations to predict line spectra. We compare the synthesised line intensities with current observations and determine those COMs which may be observable in nearby objects. We also compare the predicted grain-surface abundances with those derived from cometary comae observations.

Results. We find COMs are efficiently formed in the disk midplane via grain-surface chemical reactions, reaching peak grain-surface fractional abundances similar to 10(-6)-10(-4) that of the H nuclei number density. COMs formed on grain surfaces are returned to the gas phase via non-thermal desorption; however, gas-phase species reach lower fractional abundances than their grain-surface equivalents, similar to 10(-12)-10(-7). Including the irradiation of grain mantle material helps build further complexity in the ice through the replenishment of grain-surface radicals which take part in further grain-surface reactions. There is reasonable agreement with several line transitions of H2CO observed towards T Tauri star-disk systems. There is poor agreement with HC3(N) lines observed towards LkCa 15 and GO Tau and we discuss possible explanations for these discrepancies. The synthesised line intensities for CH3OH are consistent with upper limits determined towards all sources. Our models suggest CH3OH should be readily observable in nearby protoplanetary disks with ALMA; however, detection of more complex species may prove challenging, even with ALMA "Full Science" capabilities. Our grain-surface abundances are consistent with those derived from cometary comae observations providing additional evidence for the hypothesis that comets (and other planetesimals) formed via the coagulation of icy grains in the Sun's natal disk.

Modelling of Organic Rankine Cycle for Waste Heat Recovery Process in Supercritical Condition

Organic Rankine Cycle (ORC) is the most commonly used method for recovering energy from small sources of heat. The investigation of the ORC in supercritical condition is a new research area as it has a potential to generate high power and thermal efficiency in a waste heat recovery system. This paper presents a steady state ORC model in supercritical condition and its simulations with a real engine’s exhaust data. The key component of ORC, evaporator, is modelled using finite volume method, modelling of all other components of the waste heat recovery system such as pump, expander and condenser are also presented. The aim of this paper is to investigate the effects of mass flow rate and evaporator outlet temperature on the efficiency of the waste heat recovery process. Additionally, the necessity of maintaining an optimum evaporator outlet temperature is also investigated. Simulation results show that modification of mass flow rate is the key to changing the operating temperature at the evaporator outlet.

Space and time resolved rotational state populations and gas temperatures in an inductively coupled hydrogen RF discharge

Gas temperature is of major importance in plasma based surface treatment, since the surface processes are strongly temperature sensitive. The spatial distribution of reactive species responsible for surface modification is also influenced by the gas temperature. Industrial applications of RF plasma reactors require a high degree of homogeneity of the plasma in contact with the substrate. Reliable measurements of spatially resolved gas temperatures are, therefore, of great importance. The gas temperature can be obtained, e.g. by optical emission spectroscopy (OES). Common methods of OES to obtain gas temperatures from analysis of rotational distributions in excited states do not include the population dynamics influenced by cascading processes from higher electronic states. A model was developed to evaluate this effect on the apparent rotational temperature that is observed. Phase resolved OES confirmed the validity of this model. It was found that cascading leads to higher apparent temperatures, but the deviation (similar or equal to 25 K) is relatively small and can be ignored in most cases. This analysis is applied to investigate axially and radially resolved temperature profiles in an inductively coupled hydrogen RF discharge.

Kinetics of liquid phase semiconductor photoassisted reactions: Supporting observations for a pseudo-steady-state model

The kinetics of liquid phase semiconductor photocatalytic and photoassisted reactions are an area of some debate, reignited recently by an article by Ollis(1) in which he proposed a simple pseudo- steady- state model to interpret the Langmuir- Hinshelwood type kinetics, commonly observed in such systems. In the current article, support for this model, over other models, is provided by a reinterpretation of the results of a study, reported initially in 1999,2 of the photoassisted mineralization of 4- chlorophenol, 4-CP, by titania films and dispersions as a function of incident light intensity, I. On the basis of this model, these results indicate that 4- CP is adsorbed more strongly on P25 TiO2 when it is in a dispersed, rather than a film form, due to a higher rate constant for adsorption, k(1). In addition, the kinetics of 4- CP removal appear to depend on I-beta where, beta = 1 or 0.6 for when the TiO2 is in a film or a dispersed form, respectively. These findings are discussed both in terms of the pseudo- steady- state model and other popular kinetic models.