scholarly journals Supervision of an industrial plant subject to a maximal duration constraint

Author(s):  
Abdourrahmane M. Atto ◽  
Claude Martinez ◽  
Said Amari
Keyword(s):  
2013 ◽  
pp. 645-650
Author(s):  
Fabio R.M. Batista ◽  
Antonio J.A. Meirelles

Experimental validation of the process simulation a typical industrial bioethanol unit was conducted, comparing the obtained results with the information collected in an industrial plant. A standard solution containing water, ethanol and 17 congeners was chosen to represent the fermented must, whose composition was selected according to analyses of samples of industrial wines. A careful study of the vapour-liquid equilibrium of the wine components was performed. An attempt to optimise the industrial plant was conducted considering two optimising approaches: the central composite design (CCD) and the Sequential Quadratic Programming (SQP). The process was investigated in terms of bioethanol alcoholic graduation, ethanol recovery, energy consumption and ethanol loss. The results showed that the simulation approach was capable of correctly reproducing a real plant of bioethanol distillation and that the optimal conditions are slightly different from those used at the industrial plant investigated. Substantial fluctuations in wine composition were easily controlled for the two loop controls preventing an off-specification product. The optimised conditions indicate a distillation process able to produce bioethanol according to the legislation requirements and with appropriate steam consumption and loss of ethanol. However, for the production of alcohols with superior qualities, substantial changes in the production system may be required.


Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 114
Author(s):  
Tiziano Zarra ◽  
Mark Gino K. Galang ◽  
Florencio C. Ballesteros ◽  
Vincenzo Belgiorno ◽  
Vincenzo Naddeo

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.


Procedia CIRP ◽  
2020 ◽  
Vol 91 ◽  
pp. 15-20
Author(s):  
Steffen Kunnen ◽  
Dmytro Adamenko ◽  
Robin Pluhnau ◽  
André Loibl ◽  
Arun Nagarajah

1987 ◽  
Vol 109 (4) ◽  
pp. 335-342
Author(s):  
D. Miconi

The present paper is a report on the construction of nomograms to ascertain the domain of elastic-inertial-damping characteristics required in vibrating machine-foundation systems, in order to ensure that ergonomic and other technical constraints are complied with. Nomograms, which are the graphic representation of mathematical models in nondimensional form, prove to be an effective instrument for orientation in the design stage.


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