Real-Time Monitoring and Diagnosis of Environmental Protection Systems by Artificial Neural Networks Case study: Pharmaceutical Isolator

M. Amrani, D. Benazzouz

Abstract


Abstract: The main objective of this work is the study of risk analysis, in the field of pharmaceutical production. Some dangers can affect pharmaceutical companies’ personnel, as well as their internal and external environment, during the manufacturing process. Furthermore, the current regulations that governs this very sensitive field of manufacturing and the standards which are scrupulously very sharp. Also see the technical complexity of the industrial systems implemented. These three parameters constitute a real problem to be solved. To do this, we have developed an intelligent technique for monitoring these protection systems, in real time, in order to protect the personnel and the environment. This technique is mainly based on the use of an artificial neural network (ANN) which detects and localizes any anomalies that may occur at any time in the protection system. The experiment was carried out on an isolator belonging to BEKER Laboratories (a medecine manufacturing and development company in Dar El Beida-Algers). The test results allowed us to define the good and bad areas of the isolator operation. We concluded that, it is possible to define defaults in real time, using our new technique.

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References


Documentation technique de l’Isolateur ESCO, Laboratoires BEKER, Dar EL Beida, Alger, Algérie, (2021).

Afia, A.; Rahmoune, C.; Benazzouz, D.; Merainani, B.; Fedala, S. New Gear Fault Diagnosis Method Based on MODWPT and Neural Network for Feature Extraction and Classification. Journal of Testing and Evaluation 49 (2), (2021), 1064-1085, https://doi.org/10.1520/JTE20190107.

Gougam, F.; Rahmoune, C.; Benazzouz, D.; Afia, A.; Zair, M. Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system. Advances in Mechanical Engineering 12 (10), (2020), https://doi.org/10.1177/1687814020967874.

Gougam, F; Rahmoune, C.; Benazzouz, D.; Varnier, C.; Nicod, J-M. Health monitoring approach of bearing: application of adaptive neuro fuzzy inference system (ANFIS) for RUL-estimation and Autogram analysis for fault-localization. Computer Science 2020 Prognostics and Health Management Conference (PHM-Besançon), 200-206, (2020), DOI:10.1109/PHM-Besancon49106.2020.00040.

Benatiallah, D.; Benatiallah, D.; Bouchouicha, K.; Nasri, B. Prediction du rayonnement solaire horaire En utilisant les reseaux de neurone artificiel, Algerian Journal of Environmental Science and Technology 6 (1), (2020), 2437-1114.

Maouz, H.; Khaouane, L.; Hanini, S.; Ammi, Y.; Laidi, M.; Benimam, H. The prediction of carbonyl groups during photo-thermal and thermal aging of polymers using artificial neural networks, Algerian Journal of Environmental Science and Technology 6 (3), (2020), 2437-1114.

Nemili, Z.; Kalla, M. Modeling of Bridge Scour by Artificial Neural Networks based on PCR, Algerian J. Env. Sc. Technology, 5:3 (2019) 1036 -1040.

Tikhamarine, Y.; Souag-Gamane, D.; Kisi, O. A new intelligent method for monthly streamflow prediction: hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO). Arab J Geosci. 12, 540 (2019). https://doi.org/10.1007/s12517-019-4697-1.

Zair, M.; Rahmoune, C.; Benazzouz, D. Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network. Proceedings of the Institution of Mechanical Engineers, (2018), https://doi.org/10.1177/0954406218805510.

Miloudi, L.; Acheli, D.; Kesraoui, M.; Application of Artificial Neural Networks for Forecasting Photovoltaic System Parameters. Applied Solar Energy 53 (2017) 85–91.

Danandeh Mehr, A.; Kahya, E.; Şahin, A.; Nazemosadat, M.J. Successive-station monthly streamflow prediction using different artificial neural network algorithms. International Journal of Environmental Science and Technology. 12, 2191–2200 (2015). https://doi.org/10.1007/s13762-014-0613-0.

El Badaoui, H.; Abdallaoui, A.; Chabaa, S. Perceptron Multilayers and radial basis function grating for moisture prediction, International Journal of Innovation and Scientific Research, 5:1 (2014) 55-67.

Benazzouz, D.; Amrani, M.; Adjerid, S. Back-Propagation Algorithm Used for Tuning Parameters of ANN to Supervise a Compressor in a Pharmachemical Industry. American Journal of Intelligent Systems 2012 (2(4)), 60-65, (2012).

Hossain, A.; Nasser, M. Comparison of the finite mixture of ARMA-GARCH, back propagation neural networks and support-vector machines in forecasting financial returns. Journal of Applied Statistics. 38, 533–551 (2011). https://doi.org/10.1080/02664760903521435.

Souahi, F. ; A. Hachmaoui, A. ; Chitour, C-E. Caractérisation des mélanges complexes par une méthode utilisant les réseaux de neurones artificiels. Journal de la société Algérienne de chimie, (2007).

Denis Rémi Gilleron, F., PIERRE-JUSTIN, B. Définition et expressivité des réseaux multicouches l’algorithme de rétro-propagation du gradient, (2007).

Palluat, N. ; Racoceanu, D. ; Zerhouni, N. Utilisation des réseaux de neurones pour le pronostic et la surveillance dynamique, RSTI-RIA. Volume 19-n°6, Rue Alain Savary, F-25000 Besançon, pp 911 à 948, (2005).

Khodja, D-E. Diagnostic automatique des défaillances d’un système électromécanique par application des réseaux de neurones artificiels. Mémoire de magister, université de Boumerdes, (2001).


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