Issues in on-line optimisation

In general, on-line optimisation can be defined as the on-line process of finding the optimum set-points of the system. Several areas might be concerned in this procedure. This thesis evaluates algorithms for on-line Optimisation. Techniques for steady-state detection, static data reconciliation, gross error detection and steady-state optimisation are presented and implemented separately and within an on-line optimisation methodology. It has been acknowledged for some time now that the estimation of derivative information is probably the major drawback of the steady-state optimisation technique considered here: the ISOPE algorithm. This thesis investigates the requirements of these derivatives, methods proposed to estimate them, and presents some attempts to overcome some related problems. Also a modified version of the dynamic model identification method that uses a nonlinear model representation is proposed, and compared under simulation with other available techniques. In the same context, an alternative method based on Artificial Neural Networks to estimate the derivatives is also implemented and tested. Often, rigorous steady-state detection is crucial for process performance assessment, simulation, optimisation and control. In general, at steady-state data is collected for safe, beneficial and rational management of processes. A method for automatic detection of steady-state in multivariable processes is implemented and tested. The technique is applied on a dynamic model of a chemical reactor. The presence of errors in process measurements can invalidate the potential gains obtained from advanced optimisation and control techniques. Data reconciliation and gross error detection methods are used to reduce the inaccuracies of these measurements. The implementation and application of static data reconciliation and gross error detection techniques in this thesis show a noticeable improvement in the operation of the system, and general control system performance. The various algorithms mentioned above are successfully implemented and tested under simulation. It is illustrated that in some cases, it is possible to use steady state detection in conjunction with data reconciliation, gross error detection, parameter estimation and optimisation, to form an on-line optimisation methodology. The methodology was tested on a dynamic model of a chemical reactor

Analysis of A Nonsmooth Optimization Approach to Robust Estimation

In this paper, we consider the problem of identifying a linear map from measurements which are subject to intermittent and arbitarily large errors. This is a fundamental problem in many estimation-related applications such as fault detection, state estimation in lossy networks, hybrid system identification, robust estimation, etc. The problem is hard because it exhibits some intrinsic combinatorial features. Therefore, obtaining an effective solution necessitates relaxations that are both solvable at a reasonable cost and effective in the sense that they can return the true parameter vector. The current paper discusses a nonsmooth convex optimization approach and provides a new analysis of its behavior. In particular, it is shown that under appropriate conditions on the data, an exact estimate can be recovered from data corrupted by a large (even infinite) number of gross errors.Comment: 17 pages, 9 figure

On a class of optimization-based robust estimators

We consider in this paper the problem of estimating a parameter matrix from observations which are affected by two types of noise components: (i) a sparse noise sequence which, whenever nonzero can have arbitrarily large amplitude (ii) and a dense and bounded noise sequence of "moderate" amount. This is termed a robust regression problem. To tackle it, a quite general optimization-based framework is proposed and analyzed. When only the sparse noise is present, a sufficient bound is derived on the number of nonzero elements in the sparse noise sequence that can be accommodated by the estimator while still returning the true parameter matrix. While almost all the restricted isometry-based bounds from the literature are not verifiable, our bound can be easily computed through solving a convex optimization problem. Moreover, empirical evidence tends to suggest that it is generally tight. If in addition to the sparse noise sequence, the training data are affected by a bounded dense noise, we derive an upper bound on the estimation error.Comment: To appear in IEEE Transactions on Automatic Contro

Calibration and validation of a combustion-cogeneration

This paper describes the calibration and validation of a combustion cogeneration model for whole-building simulation. As part of IEA Annex 42, we proposed a combustion cogeneration model for studying residentialscale cogeneration systems based on both Stirling and internal combustion engines. We implemented this model independently in the EnergyPlus, ESP-r and TRNSYS building simulation programs, and undertook a comprehensive effort to validate the model's predictions. Using established comparative testing and empirical validation principles, we vetted the model's theoretical basis and its software implementations. The results demonstrate acceptable-to-excellent agreement, and suggest the calibrated model can be used with confidence

Optimising the learning of gifted aboriginal students

[Abstract]: According to the United Nations Educational, Scientific and Cultural Organisation's (2000) 'Education for All' goals, all students are entitled to opportunities to fulfil their potential. This implies that appropriate programs need to be in place for all children, especially gifted Aboriginal students. Accordingly, this means that all educational institutions in Australia have an obligation to provide involvement and commitment opportunities for all gifted and talented Aboriginal students in meeting their basic learning needs. This goal is not being achieved within Australia. Gifted and talented Aboriginal students have been identified as the most educationally disadvantaged group in the Australian education system (Sydney Morning Herald, 2004). Education for Aboriginal learners varies throughout the states of Australia. While New South Wales has provided excellent modelling of accommodating for inclusion of gifted Aboriginal students, in Queensland the lower representation of Indigenous students in gifted programs suggests inappropriate facilitation. This discussion paper compares and contrasts New South Wales and Queensland gifted Indigenous educational policy, exploring the issues of appropriate identification and programs for gifted Aboriginal students, Aboriginal learning styles and the role of the classroom teacher in accommodating these students

Integrated Analysis of Pressure Transient Tests in the Gulf of Mexico

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Does Benefit Receipt Affect Future Income? An Econometric Explanation

This paper provides an econometric analysis of the effects of receiving welfare benefits on individuals’ future income, using longitudinal administrative data on individual incomes. After controlling for heterogeneous differences in individual incomes, spurious effects of contemporaneous benefit receipt and possible endogeneity with incomes, there is no systematic evidence of a positive or negative effect of benefit receipt on incomes. The results are generally imprecisely estimated and sensitive to the choice of specification. Also, a simple first-order specification with unobserved heterogeneity provides a reasonable characterisation of individual income dynamics, although formal statistical tests tend to reject this specification as being too parsimonious.