PHYSICOCHEMICAL CHARACTERIZATION OF JAMS, ARTINASAL, AND INDUSTRIAL PRODUCED FROM THE PRICKLY PEAR FRUIT OF OPUNTIA FICUS-INDICA L.

Objective: This research work is aimed at production and evaluation of the physical-chemical, and sensory qualities of jam produced from the prickly pear fruit from two varieties: Timgad region (Batna, semi-dry area), Elkseur (Bejaia, temperate zone), and an industrial: Roumais jam (Elkseur). Methods: The soluble solids content is determined by measuring the Brix at 20°C using a digital refractometer. Ash was determined by combustion of the sample in a muffle furnace at 550°C for 5 h. The total soluble sugar content was examined using phenol-sulfuric acid colorimetric method using a spectrophotometer (UV–VIS, Shimadzu). The total nitrogen content was determined by the micro-Kjeldahl method and total protein content was calculated using a 6.25 factor. Pectin content was determined by method of Golou and Bev. Reducing sugars were determined by the Fehling’s test. The crude fiber content was determined using the traditional Van Soest method. Lipid content was determined using a Soxhlet apparatus HT 1034 according to the procedure described by Huang. Sensory evaluation was carried out by 10 panelists using a 9-point Hedonic scale. Results: The physical and chemical analysis results give a very moderate total sugars (53.7; 46.4; and 23.2%), pectin (17.1; 16.0; and 12.0%), acidity (1.84; 1.45; and 2.9 g/100 g), °Brix (60; 62.3; and 27%), humidity (30.4; 32.4; and 71.8%), and fiber (13.3; 22.6; and 22.9%). The sensory results give for color (8.10; 6.62; and 7.22), for taste (7.89; 5.81; and 7.44), for odor (8.20; 6.64; and 7.98), and for texture (7.50; 8.30; and 5.23). Conclusion: It would be good to extend the field of study to a mixture of prickly pear to others fruit to develop other quality products (jam, jelly, syrup, and candy) in the innovation framework

Use of two drag coefficient concept for the investigation of flapping wings dynamics

Micro aerial vehicles design represents a challenge that lasted for years and the fact that they operate in a low Reynolds range, which makes the unsteady aerodynamic effect more influential, made the direct computational fluid dynamics simulation expensive in time and money, and an alternative method especially in the early phase of the design would be very beneficial and rentable. In this work the flight a flapping wings operated micro aerial vehicle was investigated by the simulation of the mechanical equations of motion in order to have an approximation of the true motion behavior and the flying condition of the vehicle, in the same time this approach present a model that can be electronically implemented to make the MAV auto-controlled by imposing some criteria. The equations were developed in spherical coordinates system, and simulated using the software Mathcad, and some of the constant related to the size of the vehicle are variated to match different range of existing flying animals from insects to birds, a concept of two different drag coefficient for the upstroke and down stroke was used successfully to model the flapping wing. And giving the fact that lots of parameter were simplified or neglected which lessen the accuracy, it gave good approximation, and the model can be used for auto-control by predefined flying path. Beside the main simulation work a small experimentation on a model wing covered in feather was conducted in a subsonic wind tunnel in order to present a practical alternative that economize energy by reducing the drag in upstroke, and it was found that the direction of the feather plays a significant role in the drag reduction and we concluded that the use of such materials can greatly improve the performances, and economize the energy used to operate such vehicles

Improved field oriented control for stand alone dual star induction generator used in wind energy conversion

This paper presents a novel direct rotor flux oriented control with online estimation of magnetizing current and magnetizing inductance applied to self-excited dual star induction generator equipping a wind turbine in remote sites. The induction generator is connected to nonlinear load through two PWM rectifiers. The fuzzy logic controller is used to ensure the DC bus voltage a constant value when changes in speed and load conditions. In this study, a performance comparison between the conventional approach and the novel approach is made. The proposed control strategy is validated by simulation in Matlab/Simulink

State of the reverse osmosis membrane of sea water corso plant desalination (Algiers)

AbstractSeawater reverse osmosis (SWRO) desalination is being increasingly emphasized as a strategy for conservation of limited resources of freshwater. Although desalination has been developed for the last few decades, the SWRO operation is still affected by membrane fouling. The membrane fouling of SWRO has a significant impact on operation of desalination plants. We follow the evolution of the permeate conductivity during three months of the sea water Corso (Algiers) plant desalination. The purpose of this work is to conduct an autopsy of fouled membranes in seawater using the scanning electron microscopy (SEM) coupled by an analysis EDX. This membrane shows a change of the surface morphology, which justifies the abrupt increase in the conductivity of the permeate in May 2006. In order to identify the nature of the fouling deposit, we analysed this deposit by Xrays diffraction (XRD)

BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs

[EN] In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive performance compared with some algorithms extracted from the literature.Abdessemed, AA.; Mouss, LH.; Benaggoune, K. (2023). BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs. International Journal of Production Management and Engineering. 11(2):167-177. https://doi.org/10.4995/ijpme.2023.18077167177112Abdul-Razaq, T. S., & Potts, C. N. (1988). Dynamic programming state-space relaxation for single-machine scheduling. Journal of the Operational Research Society, 39(2), 141-152. https://doi.org/10.1057/jors.1988.26Abdullah, S., & Alzaqebah, M. (2013). A hybrid self-adaptive bees algorithm for examination timetabling problems. Applied Soft Computing, 13(8), 3608-3620. https://doi.org/10.1016/j.asoc.2013.04.010Baker, K. R., & Scudder, G. D. (1990). Sequencing with earliness and tardiness penalties: a review. Operations research, 38(1), 22-36. https://doi.org/10.1287/opre.38.1.22Castellani, M., Pham, Q. T., & Pham, D. T. (2012). Dynamic optimisation by a modified bees algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 226(7), 956-971. https://doi.org/10.1177/0959651812443462Dereli, T., & Das, G. S. (2011). A hybrid 'bee (s) algorithm'for solving container loading problems. Applied Soft Computing, 11(2), 2854-2862. https://doi.org/10.1016/j.asoc.2010.11.017Dowsland, K. A., & Thompson, J. (2012). Simulated annealing. Handbook of natural computing, 1623-1655. https://doi.org/10.1007/978-3-540-92910-9_49Hansen, P., Mladenović, N., & Moreno Pérez, J. A. (2010). Variable neighbourhood search: methods and applications. Annals of Operations Research, 175(1), 367-407. https://doi.org/10.1007/s10479-009-0657-6Ho, Y. C., & Pepyne, D. L. (2001). Simple explanation of the no free lunch theorem of optimization. In Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No. 01CH37228) (Vol. 5, pp. 4409-4414). IEEE.Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598), 671-680. https://doi.org/10.1126/science.220.4598.671Lara, C., Flores, J. J., & Calderón, F. (2008). Solving a school timetabling problem using a bee algorithm. In Mexican International Conference on Artificial Intelligence (pp. 664-674). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_63Lim, C. H., Lim, S., How, B. S., Ng, W. P. Q., Ngan, S. L., Leong, W. D., & Lam, H. L. (2021). A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach. Renewable and Sustainable Energy Reviews, 135, 110223. https://doi.org/10.33932/rir.44.3.2Mei, C. A., Pham, D. T., Anthony, J. S., & Kok, W. N. (2010, November). PCB assembly optimisation using the Bees Algorithm enhanced with TRIZ operators. In IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society (pp. 2708-2713). IEEE. https://doi.org/10.1109/IECON.2010.5675114M'Hallah, R., & Alhajraf, A. (2016). Ant colony systems for the single-machine total weighted earliness tardiness scheduling problem. Journal of Scheduling, 19(2), 191-205. https://doi.org/10.1007/s10951-015-0429-xNguyen, K., Nguyen, P., & Tran, N. (2012). A hybrid algorithm of harmony search and bees algorithm for a university course timetabling problem. International Journal of Computer Science Issues (IJCSI), 9(1), 12.Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm-a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd. https://doi.org/10.1177/0959651811422759Pham, D. T., Koc, E., Lee, J. Y., & Phrueksanant, J. (2007a). Using the bees algorithm to schedule jobs for a machine. In Proceedings Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, LAMDAMAP, Euspen, UK, Cardiff (pp. 430-439).Pham, D. T., Otri, S., & Darwish, A. H. (2007b). Application of the Bees Algorithm to PCB assembly optimisation. In Proceedings of the 3rd virtual international conference on intelligent production machines and systems (IPROMS 2007) (pp. 511-516).Pham, D. T., Castellani, M., & Fahmy, A. A. (2008). Learning the inverse kinematics of a robot manipulator using the bees algorithm. In 2008 6th IEEE International Conference on Industrial Informatics (pp. 493-498). IEEE. https://doi.org/10.1109/INDIN.2008.4618151Pham, Q. T., Pham, D. T., & Castellani, M. (2012). A modified bees algorithm and a statistics-based method for tuning its parameters. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 226(3), 287-301. https://doi.org/10.1177/0959651811422759Seeley, T. D. (2009). The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press. https://doi.org/10.2307/j.ctv1kz4h15Sourd, F. (2009). New exact algorithms for one-machine earliness-tardiness scheduling. INFORMS Journal on Computing, 21(1), 167-175. https://doi.org/10.1287/ijoc.1080.0287Sourd, F., & Kedad-Sidhoum, S. (2008). A faster branch-and-bound algorithm for the earliness-tardiness scheduling problem. Journal of Scheduling, 11(1), 49-58. https://doi.org/10.1007/s10951-007-0048-2Tanaka, S., Fujikuma, S., & Araki, M. (2009). An exact algorithm for single-machine scheduling without machine idle time. Journal of Scheduling, 12(6), 575-593. https://doi.org/10.1007/s10951-008-0093-5Von Frisch, K. (2014). Bees: their vision, chemical senses, and language. Cornell University Press.Wan, L., & Yuan, J. (2013). Single-machine scheduling to minimize the total earliness and tardiness is strongly NP-hard. Operations Research Letters, 41(4), 363-365. https://doi.org/10.1016/j.orl.2013.04.007Yau, H., Pan, Y., & Shi, L. (2008). New solution approaches to the general single-machine earliness-tardiness problem. IEEE Transactions on Automation Science and Engineering, 5(2), 349-360. https://doi.org/10.1109/TASE.2007.895219Yuce, B., Packianather, M. S., Mastrocinque, E., Pham, D. T., & Lambiase, A. (2013). Honey bees inspired optimization method: the bees algorithm. Insects, 4(4), 646-662. https://doi.org/10.3390/insects4040646Yuce, B., Fruggiero, F., Packianather, M. S., Pham, D. T., Mastrocinque, E., Lambiase, A., & Fera, M. (2017). Hybrid Genetic Bees Algorithm applied to single machine scheduling with earliness and tardiness penalties. Computers & Industrial Engineering, 113, 842-858. https://doi.org/10.1016/j.cie.2017.07.018Yurtkuran, A., & Emel, E. (2016). A discrete artificial bee colony algorithm for single machine scheduling problems. International Journal of Production Research, 54(22), 6860-6878. https://doi.org/10.1080/00207543.2016.118555

Optimal design of a DC MHD pump by simulated annealing method

In this paper a design methodology of a magnetohydrodynamic pump is proposed. The methodology is based on direct interpretation of the design problem as an optimization problem. The simulated annealing method is used for an optimal design of a DC MHD pump. The optimization procedure uses an objective function which can be the minimum of the mass. The constraints are both of geometrics and electromagnetic in type. The obtained results are reported