1998
DOI: 10.1016/s0165-0114(96)00237-0
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Optimal control of the microfiltration of sugar product using a controller combining fuzzy and genetic approaches

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Cited by 17 publications
(9 citation statements)
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“…Furthermore, in this context, it is interesting to use fuzzy logic to control the crossflow microfiltration process in order to maintain the permeate flux at a given reference value (consequently so as to control membrane fouling) and to optimize this control, using the transmembrane pressure and the flow rate as two major variables considered for this process. This approach was already used by Perrot et al [22] to control an unsteady-state, non-linear and multivariable process, such as cross-flow microfiltration of a raw cane sugar solution, combining fuzzy logic, neural network and genetic algorithms.…”
Section: Research Articlementioning
confidence: 99%
“…Furthermore, in this context, it is interesting to use fuzzy logic to control the crossflow microfiltration process in order to maintain the permeate flux at a given reference value (consequently so as to control membrane fouling) and to optimize this control, using the transmembrane pressure and the flow rate as two major variables considered for this process. This approach was already used by Perrot et al [22] to control an unsteady-state, non-linear and multivariable process, such as cross-flow microfiltration of a raw cane sugar solution, combining fuzzy logic, neural network and genetic algorithms.…”
Section: Research Articlementioning
confidence: 99%
“…Moreover, in the first analysis, the experts who performed the exercise found the approach relevant for future application during dam diagnosis and analysis. Considering the whole set of possibility distributions, the maximal length used to define the support was 5 units (for instance F0 = [2,3,4,5,6]) while the maximal length to define the core was 2 units (for instance, F1 = [5,6]) on a scale from 0 to 10.…”
Section: Possibility Expression Of Imperfect Assessmentsmentioning
confidence: 99%
“…• the time interval for data processing: monitoring data are processed once a year and trends are analysed by considering a period of several years; (0,1,2), "medium deviation" (4,5,6), "high deviation" (7,8,9) and "non-conformity" (10). Table 2 describes the instrumental monitoring piezometry indicator.…”
mentioning
confidence: 99%
“…Adaptation of such models is generally achieved by hand although in some studies (seven papers out of 40), an initial structure of the fuzzy model is defined with the experts and an optimisation tool is used to optimise it or identify the fuzzy model's parameters as in Perrot et al [64] on a filtration process or Davidson et al [21] on a peanut roasting process. The difficulty in this case is to keep the fundamental knowledge brought by the experts.…”
Section: Diagnosis Supervision and Control Of Food Qualitymentioning
confidence: 99%