Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Mellal, Mohamed ArezkiHas files: No

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    Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm
    (Springer Science and Business Media, 2025) Mellal, Mohamed Arezki; Tamazirt, Imene; Tiar, Maissa; Williams, Edward J.
    Manufacturing requires various machining processes. Nowadays, machining implies advanced technologies in order to meet more exacting process performance criteria. This paper addresses the optimization of four conventional and nonconventional machining processes: drilling, grinding, water jet machining (WJM), and wire electrical discharge machining (EDM). The input process parameters are: cutting speed, feed rate, cutting environment, depth of cut, grit size, water jet pressure, diameter of water jet nozzle, traverse rate of the nozzle, stand-off-distance, ignition pulse current, pulse-off time, pulse duration, servo reference mean voltage, servo speed variation, wire speed, wire tension, and injection pressure. The multi-objective EDM optimization problem is converted to a single-objective problem using the weighted-sum method. Two nature-inspired algorithms of artificial intelligence (AI) are implemented for solving these problems, namely the particle swarm optimization (PSO) and the flower pollination algorithm (FPA). Penalty functions are introduced to handle the constraints and to enhance the algorithms for better results. The machining outputs, required number of function evaluations, CPU time, and standard deviations are the performance metrics. The results obtained are compared and show better performance than that already documented in the literature.
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    Design of a cost and reliability optimized framework for the techno-economic analysis of a stand-alone PV/FC Li-ion battery system
    (Springer Science and Business Media Deutschland GmbH, 2025) Berrazouane, Sofiane; Alem, Said; Mellal, Mohamed Arezki
    The multiple advantages of the Li-ion batteries and the long-term storage capacity of hydrogen are exploited in this work to obtain an optimal system configuration and achieve energy self-sufficiency. The degradation cost of the battery, based on both calendar and cyclic aging, is included to provide a more detailed and accurate techno-economic balance and estimation. Moreover, the operating cost of the battery and hydrogen system is considered in the energy management strategy (EMS) to determine the prioritized storage system to be used. Therefore, to economize the costs and increase the reliability of the standalone photovoltaic/fuel cell (PV/FC) Li-ion battery system, an EMS is developed by customizing and adapting the Improved Grey Wolf Optimizer (IGWO), referred to as the Modified Improved Grey Wolf Optimizer (M-IGWO). The proposed system achieves a Levelized Cost of Energy (LCOE) of 0.3257 $/kWh when Loss of Power Supply Probability (LPSP) is less than 1%, with 68.18% use of hydrogen system over the time and 26.11% of the battery, demonstrating high reliability. Depending on the operating mode, the battery utilization to support H2_system is decreasing from 10.71% to 1.18%, while the hydrogen system utilization to support battery is decreasing from 29.64% to 9.16%. These findings highlight the effectiveness of the M-IGWO algorithm and EMS in optimizing PV/FC Li-ion battery system for various scenarios
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    A novel approach for remaining useful life prediction of high-reliability equipment based on long short-term memory and multi-head self-attention mechanism
    (Wiley-Blackwell, 2024) Al-Dahidi, Sameer; Rashed, Mohammad; Abu-Shams, Mohammad; Mellal, Mohamed Arezki; Alrbai, Mohammad; Ramadan, Saleem; Zio, Enrico
    Accurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high-reliability equipment, few complete-run-to-failure trajectories may be available in practice. This constitutes a technical challenge for data-driven techniques for estimating the RUL. This paper proposes a novel data-driven approach for fault prognostics using the Long-Short Term Memory (LSTM) model combined with the Multi-Head Self-Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long-term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high-reliability equipment compared to four benchmark models from the literature.
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    Combined heat and power economic dispatch problem with binary method using flower pollination algorithm and differential evolution
    (Springer, 2023) Mellal, Mohamed Arezki; Khitous, Marwa; Zemmouri, Meriem
    Nowadays, the need for electrical energy became crucial in the world. The co-generation plants, which simultaneously produce electrical and heat energies, are one of the alternative solutions to supply people and industry with both energies. The present work addresses the cost minimization of the nonconvex combined heat and power dispatch problem (CHPED). The nonconvex operating region is handled using the binary method, and the optimization problem is solved using two nature-inspired algorithms, namely the flower pollination algorithm (FPA) and the differential evolution (DE). Penalty functions are adopted to handle all the operating constraints, units’ limits, and demands. The results obtained compare the algorithms and those of the literature. It is observed that the fuel cost obtained by the flower pollination algorithm (FPA) is less than the one obtained by the differential evolution (DE) and the particle swarm optimization (PSO)
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    System design optimization with mixed subsystems failure dependencies
    (Elsevier, 2023) Mellal, Mohamed Arezki; Zio, Enrico; Al-Dahidi, Sameer; Masuyama, Naoki; Nojima, Yusuke
    Systems present dependencies among their components failure behavior, which impact their ultimate availability. Previous works addressed the optimal design of systems in relation to its cost and under given availability constraint, considering identical subsystems failure dependencies. The present paper addresses this problem in a realistic scenario by taking into consideration mixed subsystems failure dependencies. The problem is formulated with reference to a complex bridge network system and a series-parallel system. Three nature-inspired optimization techniques are implemented to solve the problem, namely differential evolution (DE), manta ray foraging optimization (MRFO), and shuffled frog leaping algorithm (SFLA) with constraint handling. A numerical evaluation is performed; the results show that DE outperforms MRFO and SFLA
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    An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies
    (SAGE Publications, 2019) Mellal, Mohamed Arezki; Zio, Enrico
    This article presents an algorithm for optimal redundancy and repair team allocation with respect to minimum system cost and a system availability constraint. Four scenarios are considered for the failures occurring in the subsystems of the system: independence, linear dependence, weak dependence, and strong dependence. An adaptive cuckoo optimization algorithm is developed for solving the nonlinear integer optimization problem of allocation. A series–parallel system with six subsystems is considered as a case study for demonstration purposes. The results obtained highlight the good performance of the developed algorithm
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    Fuzzy multiobjective system reliability optimization by genetic algorithms and clustering analysis
    (WILEY ONLINE Library, 2020) Chebouba, Billal Nazim; Mellal, Mohamed Arezki; Smail Adjerid
    System reliability optimization is a key element for a competitive and safe industrial plant. This paper addresses the multiobjective system reliability optimization in the presence of fuzzy data. A framework solution approach is proposed and based on four steps: defuzzify the data into crisp values by the ranking function procedure, the defuzzified problems are solved by the non‐sorting genetic algorithms II and III (NSGA‐II and NSGA‐III), the Pareto fronts are compared by the spacing method for selecting the best one, and then the best Pareto front is reduced by the clustering analysis for helping the decision maker. A case study presented in the literature as a mono‐objective redundancy allocation problem with fuzzy data is investigated in the present paper as multiobjective redundancy allocation and reliability‐redundancy allocation problems show the applicability of the approach.