Mathematical modelling and control of agitated extraction columns

Liquid-liquid extraction has long been known as a unit operation that plays an important role in industry. This process is well known for its complexity and sensitivity to operation conditions. This thesis presents an attempt to explore the dynamics and control of this process using a systematic approach and state of the art control system design techniques. The process was studied first experimentally under carefully selected. operation conditions, which resembles the ranges employed practically under stable and efficient conditions. Data were collected at steady state conditions using adequate sampling techniques for the dispersed and continuous phases as well as during the transients of the column with the aid of a computer-based online data logging system and online concentration analysis. A stagewise single stage backflow model was improved to mimic the dynamic operation of the column. The developed model accounts for the variation in hydrodynamics, mass transfer, and physical properties throughout the length of the column. End effects were treated by addition of stages at the column entrances. Two parameters were incorporated in the model namely; mass transfer weight factor to correct for the assumption of no mass transfer in the. settling zones at each stage and the backmixing coefficients to handle the axial dispersion phenomena encountered in the course of column operation. The parameters were estimated by minimizing the differences between the experimental and the model predicted concentration profiles at steady state conditions using non-linear optimisation technique. The estimated values were then correlated as functions of operating parameters and were incorporated in·the model equations. The model equations comprise a stiff differential~algebraic system. This system was solved using the GEAR ODE solver. The calculated concentration profiles were compared to those experimentally measured. A very good agreement of the two profiles was achieved within a percent relative error of ±2.S%. The developed rigorous dynamic model of the extraction column was used to derive linear time-invariant reduced-order models that relate the input variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output variables (raffinate concentration and extract concentration) using the asymptotic method of system identification. The reduced-order models were shown to be accurate in capturing the dynamic behaviour of the process with a maximum modelling prediction error of I %. The simplicity and accuracy of the derived reduced-order models allow for control system design and analysis of such complicated processes. The extraction column is a typical multivariable process with agitator speed and solvent feed flowrate considered as manipulative variables; raffinate concentration and extract concentration as controlled variables and the feeds concentration and feed flowrate as disturbance variables. The control system design of the extraction process was tackled as multi-loop decentralised SISO (Single Input Single Output) as well as centralised MIMO (Multi-Input Multi-Output) system using both conventional and model-based control techniques such as IMC (Internal Model Control) and MPC (Model Predictive Control). Control performance of each control scheme was. studied in terms of stability, speed of response, sensitivity to modelling errors (robustness), setpoint tracking capabilities and load rejection. For decentralised control, multiple loops were assigned to pair.each manipulated variable with each controlled variable according to the interaction analysis and other pairing criteria such as relative gain array (RGA), singular value analysis (SVD). Loops namely Rotor speed-Raffinate concentration and Solvent flowrate Extract concentration showed weak interaction. Multivariable MPC has shown more effective performance compared to other conventional techniques since it accounts for loops interaction, time delays, and input-output variables constraints.

Attentional changes in pre-stimulus oscillatory activity within early visual cortex are predictive of human visual performance

Physiological and neuroimaging studies provide evidence to suggest that attentional mechanisms operating within the fronto-parietal network may exert top–down control on early visual areas, priming them for forthcoming sensory events. The believed consequence of such priming is enhanced task performance. Using the technique of magnetoencephalography (MEG), we investigated this possibility by examining whether attention-driven changes in cortical activity are correlated with performance on a line-orientation judgment task. We observed that, approximately 200 ms after a covert attentional shift towards the impending visual stimulus, the level of phase-resetting (transient neural coherence) within the calcarine significantly increased for 2–10 Hz activity. This was followed by a suppression of alpha activity (near 10 Hz) which persisted until the onset of the stimulus. The levels of phase-resetting, alpha suppression and subsequent behavioral performance varied between subjects in a systematic fashion. The magnitudes of phase-resetting and alpha-band power were negatively correlated, with high levels of coherence associated with high levels of performance. We propose that top–down attentional control mechanisms exert their initial effects within the calcarine through a phase-resetting within the 2–10 Hz band, which in turn triggers a suppression of alpha activity, priming early visual areas for incoming information and enhancing behavioral performance.

Forecasting and risk analysis applied to management planning and control

The aim of this research was to improve the quantitative support to project planning and control principally through the use of more accurate forecasting for which new techniques were developed. This study arose from the observation that in most cases construction project forecasts were based on a methodology (c.1980) which relied on the DHSS cumulative cubic cost model and network based risk analysis (PERT). The former of these, in particular, imposes severe limitations which this study overcomes. Three areas of study were identified, namely growth curve forecasting, risk analysis and the interface of these quantitative techniques with project management. These fields have been used as a basis for the research programme. In order to give a sound basis for the research, industrial support was sought. This resulted in both the acquisition of cost profiles for a large number of projects and the opportunity to validate practical implementation. The outcome of this research project was deemed successful both in theory and practice. The new forecasting theory was shown to give major reductions in projection errors. The integration of the new predictive and risk analysis technologies with management principles, allowed the development of a viable software management aid which fills an acknowledged gap in current technology.

Cost and time control of construction project: inhibiting factors and mitigating measures in practice

Despite the availability of various control techniques and project control software many construction projects still do not achieve their cost and time objectives. Research in this area so far has mainly been devoted to identifying causes of cost and time overruns. There is limited research geared towards studying factors inhibiting the ability of practitioners to effectively control their projects. To fill this gap, a survey was conducted on 250 construction project organizations in the UK, which was followed by face-to-face interviews with experienced practitioners from 15 of these organizations. The common factors that inhibit both time and cost control during construction projects were first identified. Subsequently 90 mitigating measures have been developed for the top five leading inhibiting factors—design changes, risks/uncertainties, inaccurate evaluation of project time/duration, complexities and non-performance of subcontractors were recommended. These mitigating measures were classified as: preventive, predictive, corrective and organizational measures. They can be used as a checklist of good practice and help project managers to improve the effectiveness of control of their projects.

Anticipatory control of long-range phase synchronization

Everyday human behaviour relies on our ability to predict outcomes on the basis of moment by moment information. Long-range neural phase synchronization has been hypothesized as a mechanism by which ‘predictions’ can exert an effect on the processing of incoming sensory events. Using magnetoencephalography (MEG) we have studied the relationship between the modulation of phase synchronization in a cerebral network of areas involved in visual target processing and the predictability of target occurrence. Our results reveal a striking increase in the modulation of phase synchronization associated with an increased probability of target occurrence. These observations are consistent with the hypothesis that long-range phase synchronization plays a critical functional role in humans' ability to effectively employ predictive heuristics.