Matthew J. Lees

Modelling and PIP control design for open-top chambers

The paper first describes the identification of a control model for carbon dioxide concentration in an open-top chamber (OTC) used in plant physiology atmospheric change experiments. This model is then employed in the design of a gain-scheduled controller utilising the Proportional-Integral-Plus (PIP) control design methodology developed by Young et al. (1987). The system has been evaluated in a number of field trials, yielding good control, well within the required design specifications.

Advances in Transfer Function Based Flood Forecasting

Simple transfer function (TF) models were first used for operational real-time flood forecasting in the 1970s after a number of researchers had shown that they could efficiently characterise dominant rainfall-runoff dynamics. A major advantage of the approach at that time was the small requirement for computing power in comparison with more complex conceptual models. Although computing power is no longer a particularly advantageous feature, recent advances in transfer function modelling and forecasting techniques provide the ability to produce state-of-the-art flood forecasts whilst retaining the advantage of simplicity. This paper challenges some popularly held views on transfer function forecasting techniques, such as “a major disadvantage of TF modelling compared to conceptual models is that it is a black-box approach with no physical process explanation”, and presents a number of recent advances in non-linear transfer function identification, parameter estimation and real-time implementation, concluding with an illustrative example.

Dynamic Modelling of Spatially Variable Catchment Hydrochemistry for Critical Loads Assessment

Concern about acidification in upland areas has brought about the need to model the stream hydrochemical response to deposition and land-use changes and calculate critical loads. Application of dynamic models such as MAGIC are preferable to steady-state methods, since they are able to produce an estimate of the time scale required to meet some water chemistry target given a reduction in acid deposition. These models typically consider annual changes in stream chemistry at one point. However, in order to protect biota from ‘acid episodes’, quantification of temporal variability needs to encompass event responses; in addition spatial variability across the catchment also needs to be considered. In this paper, modelling of both spatial and temporal variability is combined in a new framework which enables quantification of catchment hydrochemical variability in time and space. Both low and high flow hydro-chemical variability are quantified in terms of statistical distributions of ANC (Acid Neutralisation Capacity). These are then input as stochastic variables to an EMMA (End-Member Mixing Analysis) model which accounts for temporal variability and ANC is hence predicted as a function of time and space across the whole catchment using Monte-Carlo simulation. The method is linked to MAGIC to predict future scenarios and may be used by iteration to calculate critical loads. The model is applied to the headwaters of the River Severn at Plynlimon, Wales, to demonstrate its capabilities.

Modelling and control design for open top chambers used in plant physiology climate change experiments

Abstract The paper describes the identification of a control model for carbon dioxide concentration in an Open-Top Chamber (OTC) used in plant physiology climate change experiments. This model is used to design an advanced, model-based, gain-scheduled controller using the Proportional-Integral-Plus (PIP) control design methodology developed by Young et al . (1987). Prior to implementation, the controller is evaluated by simulation, utilising realistic disturbance data

The Modelling and Multivariable Control of Glasshouse Systems

Abstract The paper presents a multivariable simulation model of a glasshouse microclimate. A linear reduced order model is obtained using identification techniques. This control model is used as the basis of a True Digital Control design for temperature in the glasshouse. Finally multivariable extensions to the glasshouse control design methodology are discussed.