Monitoring of Sensors and Processes in a Galvanising Line

Sirou, F.; Eustache, V.; Bloch, Gérard; Cecchin, Thierry; Fatrez, P.

doi:10.1016/s1474-6670(17)43262-9

Cited by 1 publication

(3 citation statements)

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“…These three possibilities, standard, prefiltered, and robust Page-Hinkley tests are presented and tested in [13] and [14]. These monitoring tests are implemented on the plant supervision system.…”

Section: Robust Page-hinkley Testmentioning

confidence: 99%

“…in the gradient (12) and with its second derivative L ′ (ε(k)) = 1 (17) in the approximate Hessian (14). This algorithm will be called Levenberg-Marquard neural network (LMNN).…”

Section: Control Of the Induction Furnacementioning

confidence: 99%

“…in the approximate Hessian (14). The expression of the variance σ 2 n (k) of the kth prediction error, calculated at each iteration of algorithm (11), from all the n errors, is given in [21].…”

Section: Control Of the Induction Furnacementioning

confidence: 99%

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The improvement of the performances of a complex production process such as the Sollac hot dip galvanizing line of Florange (France) needs to integrate various approaches, including quality monitoring, diagnosis, control, optimization methods, etc. These techniques can be grouped under the term of intelligent control and aim to enhance the operating of the process as well as the quality of delivered products. The first section briefly describes the plant concerned and presents the objectives of the study. These objectives are mainly reached by incorporating the skill of the operators in neural models, at different levels of control. The low-level supervision of measurements and operating conditions are briefly presented. The control of the coating process, highly nonlinear, is divided in two parts. The optimal thermal cycle of alloying is determined using a radial basis function neural network, from a static database built up from recorded measurements. The learning of the weights is carried out from the results of a fuzzy C-means clustering algorithm. The control of the annealing furnace, the most important equipment, is achieved by mixing a static inverse model of the furnace based on a feedforward multilayer perceptron and a regulation loop. Robust learning criteria are used to tackle possible outliers in the database. The neural network is then pruned in order to enhance the generalization capabilities.

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Section: Robust Page-hinkley Testmentioning

confidence: 99%

“…in the gradient (12) and with its second derivative L ′ (ε(k)) = 1 (17) in the approximate Hessian (14). This algorithm will be called Levenberg-Marquard neural network (LMNN).…”

Section: Control Of the Induction Furnacementioning

confidence: 99%