Mohammed El Ganaoui

Two-stage anaerobic digestion (TSAD) enables sequential H₂ and CH₄ production from organic substrates, offering higher energy recovery than single-stage systems. This study couples the Anaerobic Digestion Model No. 1 (ADM1) with a dark fermentation model (Reactor 1, internal-circulation gas-lift) and a methanogenesis model (Reactor 2, a hybrid upflow anaerobic sludge blanket and continuous stirred-tank reactor), implemented in MATLAB with a stiff ordinary differential equation (ODE) solver. The reactors are coupled dynamically through piecewise cubic Hermite interpolants built on Reactor 1 effluent trajectories. To reduce computational cost (~60 s per ODE run), a physics-informed recurrent neural network (PI-RNN) surrogate is developed, using ODE residuals as physics-based loss terms. Five architectures are compared: a bidirectional long short-term memory network (BiLSTM), a gated recurrent unit (GRU), the PI-RNN (physics loss weight λ = 0.10), and two baselines (multilayer perceptron, linear regression), trained on N = 300 Latin-hypercube-sampled trajectories. A Morris sensitivity analysis identifies kLa_hyd, μ_max^DF and K_I,hyd as the dominant kinetic parameters governing H₂ production. The validated model predicts a steady-state dissolved H₂ concentration below the Thauer threshold (0.3 mol/m³), benchmark-consistent acetate transfer to Reactor 2, and an energy output of 0.73 kWh. The PI-RNN surrogate is the most accurate for all target variables (R² = 0.9885, mean absolute percentage error of 2.15%), eliminates unphysical state predictions, and reduces inference time by over four orders of magnitude (Sf = 29268), enabling real-time nonlinear model predictive control.

The influence of Cu content on the thermal conductivity and mechanical properties of Al-9Si-0.7Fe casting alloy were investigated in this paper. The results show that as the Cu content increases from 0.1 wt.% to 2.0 wt.%, the thermal conductivity of the alloy decreases from 173.6 W/(m·K) to 154.8 W/(m·K), while the yield strength increases from 72.2 MPa to 90.9 MPa. Metallographic, XRD, and EPMA analyses revealed that Cu has a relatively small impact on the secondary dendrite arm spacing of α-Al and the morphology of eutectic silicon. Its influence on the thermal conductivity and mechanical properties primarily stems from Cu atoms dissolving in the α-Al matrix, leading to a decreased lattice constant, increased lattice distortion, enhanced electron scattering, and improved solid solution strengthening effect. Based on the measured solubility of Cu, the Maxwell and Hashin–Shtrikman thermal conductivity models were modified. The correlation coefficients between the predicted values of the modified models and the experimental data were 92.77% and 93.11%, respectively, indicating a significant improvement in prediction accuracy.

Adsorption refrigeration machines (ARMs) offer a sustainable cooling solution by oper-ating with low electrical input and utilizing environmentally friendly refrigerants. Their compatibility with renewable energy sources makes them particularly suitable for inte-gration into clean energy systems, including hydrogen production processes. This study investigates the thermodynamic performance of an ARM through a parametric analysis focused on the influence of adsorption and desorption temperatures on system efficiency. The analysis is based on a dynamic thermodynamic model simulating the adsorption cycle under various temperature scenarios. Key performance indicators, such as the coef-ficient of performance (COP), cooling capacity, and thermal efficiency, are evaluated across a range of operating temperatures. Particular attention is given to the optimization of the desorption temperature, which critically affects energy consumption and the cooling demand required for hydrogen production via electrolysis or thermochemical processes. Results show that optimizing temperature parameters can significantly improve system performance. For instance, increasing the desorption temperature within an optimal range enhances the regeneration phase and maximizes the COP. The study identifies operating conditions that promote high thermal efficiency and effective coupling with hydrogen production cycles. These findings demonstrate the technical feasibility and energetic advantages of integrating adsorption refrigeration into hydrogen production systems. Such coupling not only reduces the total energy input but also supports the development of low-carbon, energy-efficient pathways for green hydrogen generation.

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions.

An experimental setup was developed, incorporating a monitored DualSun^® photovoltaic–thermal (PV/T) panel and a weather station to continuously record real-time climatic conditions. This setup enables an hour-by-hour comparison between the actual performance observed under real-world conditions and the predictions generated by the thermal model. The generated dataset was used to evaluate a thermal model derived from the literature, comparing its predictions with measured data. The model adopts a quasi-steady-state, one-dimensional approach based on heat balance equations applied to both the photovoltaic cells and the heat transfer fluid. Conducted during the summer of 2022, the experiment provides valuable insights into the accuracy of the literature-based thermal model under summer meteorological conditions. The results show a good correlation between the experimental data and the model’s predictions. The average deviation observed for the outlet fluid temperature is 0.1 °C during the day and 1.3 °C at night. Consequently, the findings underscore the model’s effectiveness for evaluating daytime performance, while also pointing out its limitations for nighttime predictions, especially when hybrid PV/T collectors are used for applications such as nighttime free cooling.

In recent decades, the degradation of air quality has become a growing global concern, driven by rapid industrialization, increased urbanization, and the emergence of new health threats such as the COVID-19 pandemic. This chapter explores the evolution of indoor air quality (IAQ) as a critical issue, with a focus on the development and implementation of international guidelines and standards that regulate key indoor pollutants, including carbon dioxide (CO₂), carbon monoxide (CO), and particulate matter (PM10, PM2.5). These standards apply to diverse environments such as hospitals, office buildings, and enclosed transport cabins, where maintaining healthy air quality is vital for human well-being. A central theme of the chapter is the integration of the Internet of Things (IoT) into IAQ monitoring and management systems. Specifically, the study highlights how Low-Cost Sensors (LCSs) are transforming traditional air quality monitoring by enabling real-time, continuous tracking of indoor pollutants. The chapter examines the role of IoT-based technologies in enhancing regulatory compliance, improving IAQ management practices, and providing actionable data for mitigating pollutant exposure. Furthermore, it assesses the challenges and opportunities presented by IoT adoption in IAQ management, including issues related to sensor accuracy, data privacy, and long-term sustainability. By analyzing case studies and real-world applications, this chapter offers key insights into how technological advancements are shaping the future of IAQ management. The findings underscore the importance of aligning technological innovation with evolving air quality standards to ensure safer and healthier indoor environments. Ultimately, this work contributes to the broader discourse on how IoT and smart sensor networks can support global efforts to enhance air quality in both public and private indoor spaces.