Role of modelling on state and parameter estimation

In process industry, plants are generally operated at conditions that differ from the designed ones mainly due to disturbances. Disturbances can enter the system in form of fluctuations in feed flow, temperature and composition, or fluctuation of the utilities quality. These events cause a deterioration of the plant performance that cannot be quantified and online compensated by means of controllers unless online measurements of the quality targets (e.g. concentration, conversion, etc) are available. However the problem of online monitoring cannot be always solved in practice by means of hardware analysers because of unreliable and delayed measurements. An alternative approach is based on estimators that infer the variables of interests by means of secondary measurements and a often nonlinear model of the process. This type of realization of observers can include the online estimation of model parameters for a more accurate alignment of the model with the process behaviour. This work addresses the role of the estimation model on estimation performance. Recent studies [1, 2] pointed out that for a defined set of plant measurements the choice of the estimation model and the innovated states play a key role on the performance of the estimator regardless the algorithm employed. Even if in the cited studies some features of the estimation model (such as level of detail, computational complexity) have been taken into account, the effect on the estimation performance of model manipulations such as variables and parameters scaling [3] and transformation have not been investigated yet. For this reason the role of different realizations of the same estimation model needs to be further investigated

On the model-based monitoring of industrial batch crystallizers

Crystallization is an important separation process to obtain high value-added chemicals in crystalline form from liquid solution in pharmaceutical, food and fine chemical industries. As most of the particulate processes, the quality of the solid product is determined by its particle size distribution (PSD). The achievement of the desired quality targets of the fine crystalline products relies on an efficient online process monitoring for separation supervision and control. However, hardware analyzers able to online measure the PSD and the solute concentration are rarely available, due to their costs \cite{Multi}. These unmeasured process variables can be estimated by state estimators that combine information from the process model and secondary measurements. The problem of designing state observers for online monitoring the PSD evolution has been mostly addressed under the assumption that some PSD measurements were available (see \cite{Mesb} and literature therein), which is not likely in practice. This work proposes a methodology to asses the feasibility of using common measurements (e.g. temperature and liquid fraction) for estimation purposes based on local observability \cite{Herm} and detectability \cite{AlFer} arguments. The results are supported using a data-derived technique, with data generated by a simulation model of the industrial crystallizer. Based on the results of the observability analysis, the structure of a state estimator is proposed

Role of modelling on state and parameter estimation

In process industry, plants are generally operated at conditions that differ from the designed ones mainly due to disturbances. Disturbances can enter the system in form of fluctuations in feed flow, temperature and composition, or fluctuation of the utilities quality. These events cause a deterioration of the plant performance that cannot be quantified and online compensated by means of controllers unless online measurements of the quality targets (e.g. concentration, conversion, etc) are available. However the problem of online monitoring cannot be always solved in practice by means of hardware analysers because of unreliable and delayed measurements. An alternative approach is based on estimators that infer the variables of interests by means of secondary measurements and a often nonlinear model of theprocess. This type of realization of observers can include the online estimation of model parameters for a more accurate alignment of the model with the process behaviour.This work addresses the role of the estimation model on estimation performance. Recent studies [1, 2] pointed out that for a defined set of plant measurements the choice of the estimation model and the innovated states play a key role on the performance of the estimator regardless the algorithm employed. Even if in the cited studies some features of the estimation model (such as level of detail, computational complexity) have been taken into account, the effect on the estimation performance of model manipulations such as variables and parameters scaling [3] and transformation have not been investigated yet. For this reason the role of different realizations of the same estimation model needs to be further investigated

Speakers Raise their Hands and Head during Self-Repairs in Dyadic Conversations

People often encounter difficulties in building shared understanding during everyday conversation. The most common symptom of these difficulties are self-repairs, when a speaker restarts, edits or amends their utterances mid-turn. Previous work has focused on the verbal signals of self-repair, i.e. speech disfluences (filled pauses, truncated words and phrases, word substitutions or reformulations), and computational tools now exist that can automatically detect these verbal phenomena. However, face-to-face conversation also exploits rich non-verbal resources and previous research suggests that self-repairs are associated with distinct hand movement patterns. This paper extends those results by exploring head and hand movements of both speakers and listeners using two motion parameters: height (vertical position) and 3D velocity. The results show that speech sequences containing self-repairs are distinguishable from fluent ones: speakers raise their hands and head more (and move more rapidly) during self-repairs. We obtain these results by analysing data from a corpus of 13 unscripted dialogues, and we discuss how these findings could support the creation of improved cognitive artificial systems for natural human-machine and human-robot interaction

Scenarios for the Development of Smart Grids in the UK: Literature Review

This Working Paper reviews the existing literature on the socio-technical aspects of smart grid development. This work was undertaken as part of the Scenarios for the Development of Smart Grids in the UK project

Change detection for objects on surfaces slanted in depth

Change detection for objects associated with a surface extended in depth might be more difficult than for a frontal surface if it is easier to shift attention within a frontal surface. On the other hand, previous research has shown that ground surfaces have a special role in organizing the 3-D layout of objects shown against scene backgrounds. In the current study, we examined whether a frontal background or a ground surface background would result in superior change detection performance using a change detection flicker paradigm. In the first experiment, we considered whether background slant affects change detection performance. In Experiment 2, we examined the effect of height in the image on change detection performance. In Experiment 3, we examined change detection performance on slanted ceiling surfaces. The results of these experiments indicate that change detection is more efficient on near-ground planes than on surfaces at intermediate slants or ceiling surfaces. This suggests that any superiority of frontal plane backgrounds in a change detection task may be equivalent to the superiority of a near-ground plane in organizing a scene, with the lowest level of performance occurring for surfaces that are not frontal but further from a ground surface orientation