A hierarchical procedure for adjustment and material balancing of mineral processes data

Hodouin, D.; Everell, M.D.

doi:10.1016/0301-7516(80)90002-2

Cited by 92 publications

(27 citation statements)

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“…In an assessment of the functions used in the literature, it became clear that the robust functions employed belonged to the redescending M-estimators families which in turn are based on the maximum likelihood function (Hodouin & Everell, 1980 Table 1 were used for the study of cases of interest. They were retrieved from Ozyurt and Pike (2004), Jin et al (2012) and Alamgir etal.…”

Section: Methodology Formulation Of Data Reconciliation Problem With mentioning

confidence: 99%

<b>Evaluation of robust functions for data reconciliation in thermal systems

2016

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ABSTRACT. Process variables regularly control and evaluate industrial processes. Information with gross errors may in some cases not be attenuated by function reconciliation and change the calculation of process balance, leading optimization results towards non-feasible regions or to optimal sites. A promising alternative for reconciling functions is the use of robust functions. Current paper considers the above scenario and evaluates the fitness of some robust functions in solving in steady state chemical processes data reconciliation problems represented by linear and nonlinear systems in the presence of gross errors. Traditional Cauchy, Fair, Contaminated Normal and Logistic robust functions are used in the reconciliation problem where their estimates are compared to those obtained with the use of the latest features, such as New Target and Alarm. Rates for gross errors in tests were limited between 4 and 10σ of the measured current and elaborated a region of outliers. Results showed that New Target and Alarm functions are different from the others as the magnitude of the gross error increases, tending towards true rates specified by set point.Keywords: set measures, robustness, efficiency, nonlinear systems.Avaliação de funções robustas para reconciliação de dados em sistemas térmicos RESUMO. Variáveis de processo são regularmente utilizadas para controlar e avaliar os processos industriais. As informações contendo erros grosseiros podem, em alguns casos não ser atenuadas por funções reconciliadoras e, desta forma, alterar os cálculos de balanços de processo, conduzindo os resultados de otimização a regiões inviáveis ou a ótimos locais. Neste sentido, uma alternativa promissora para função reconciliadora é a utilização das funções robustas. O presente trabalho leva em consideração este cenário e avalia a aptidão de algumas funções robustas na resolução de problemas de reconciliação de dados em processos químicos em estado estacionário representados por sistemas lineares e não lineares na presença de erros grosseiros. As tradicionais funções robustas Cauchy, Fair, Normal Contaminada e Logística são utilizadas, onde, as suas estimativas são comparadas com as obtidas com o uso de funções mais recentes, como a New Target e Alarm. Nos testes realizados, os valores gerados para os erros grosseiros foram limitados entre 4 a 10σ da medida da corrente, configurando assim uma região de grandes desvios. Os resultados indicaram que as funções New Target e Alarm diferenciam-se das demais à medida que aumenta a magnitude do erro grosseiro indicando uma tendência ao valor verdadeiro especificado pelo setpoint.Palavras-chave: ajuste de medidas, robustez, eficiência, sistemas não lineares.

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Section: Methodology Formulation Of Data Reconciliation Problem With mentioning

confidence: 99%

<b>Evaluation of robust functions for data reconciliation in thermal systems

2016

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“…By establishing that the complexity of large dimension optimization problems can be reduced by performing a partition of the criterion to be optimized, Hodouin and Everell [1] developed a strategy in which the original problem is partitioned into subsystems that are not functions of the same variables. The decoupling is performed such that the subsystems are easy to solve.…”

Section: Solution Strategiesmentioning

confidence: 99%

“…Lee et al [81] proposed an on-line data reconciliation and optimization for an industrial utility plant based on a hierarchical decomposition approach, similar to the strategy applied by [1]. The strategy decomposes a large and complex system into several subsystems, where it is argued that handling small size subsystems instead of an original large one provides a reduction of computation time, a more robust solution, simplicity of problem formulation and easy maintenance.…”

Section: Solution Strategiesmentioning

confidence: 99%

“…Measurements of process variables, both on-and off-line, are subject to random errors (noise), such as natural disturbances, sampling errors, unreliable instrument readouts and laboratory analyses inaccuracies, and nonrandom errors, such as sensor bias or sensor failure (gross errors) [1]. Random noise may also be the outcome of other effects, such as high-frequency pick-up, low resolution, and errors in transmission and conversion (including A/D and D/A conversion) [2], usually attributed applications, where reconciled measurements are used in on-line applications such as performance monitoring, process control, and real-time optimization [55][56][57].…”

Section: Introductionmentioning

confidence: 99%

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Numerical Aspects of Data Reconciliation in Industrial Applications

et al. 2017

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Abstract:Data reconciliation is a model-based technique that reduces measurement errors by making use of redundancies in process data. It is largely applied in modern process industries, being commercially available in software tools. Based on industrial applications reported in the literature, we have identified and tested different configuration settings providing a numerical assessment on the performance of several important aspects involved in the solution of nonlinear steady-state data reconciliation that are generally overlooked. The discussed items are comprised of problem formulation, regarding the presence of estimated parameters in the objective function; solution approach when applying nonlinear programming solvers; methods for estimating objective function gradients; initial guess; and optimization algorithm. The study is based on simulations of a rigorous and validated model of a real offshore oil production system. The assessment includes evaluations of solution robustness, constraint violation at convergence, and computational cost. In addition, we propose the use of a global test to detect inconsistencies in the formulation and in the solution of the problem. Results show that different settings have a great impact on the performance of reconciliation procedures, often leading to local solutions. The question of how to satisfactorily solve the data reconciliation problem is discussed so as to obtain improved estimates.

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“…The values for the variables of Eq. (1) are usually obtained through sampling and data reconciliation (Hodouin and Everell, 1980). The plot of the partition factors as a function of the particle size is called the mineral partition or size recovery curve of the classifier.…”

Section: Spiral Operation and Partition Curves Of The Particlesmentioning

confidence: 99%