Fouad Belhora

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 construction sector requires environmentally sustainable materials that reduce embodied carbon while maintaining adequate mechanical performance for practical applications. This study aimed to investigate the influence of crushed Moroccan hemp shives on the physicochemical and mechanical...

Photovoltaic (PV) technology plays a pivotal role in the global transition toward sustainable energy. With the increasing deployment of PV systems, polymeric materials used in PV modules can be exposed to various environmental stresses during operation, which may induce microstructural defects and interfacial degradation. Such degradation processes can result in local electric field distortion within polymeric materials, promoting charge accumulation and accelerating electrical aging, which may ultimately affect the reliability of PV modules. Enhancing the charge dissipation capability and breakdown strength of polymers is critical for improving the electrical reliability of PV systems. Here, polyethylene terephthalate (PET) films are modified with TiO ₂ nanoparticles to regulate their charge trapping and transport characteristics. The incorporation of TiO ₂ nanoparticles effectively reduces the initial surface potential and accelerates charge decay. Moreover, the PET/TiO ₂ nanocomposites are laminated with a polyolefin elastomer (POE) encapsulant to construct a multilayer dielectric structure. The results reveal that the backsheet‐encapsulant interface modifies charge redistribution and relaxation processes, while TiO ₂ ‐induced trap regulation further improves interfacial charge dissipation. This study provides insights into the design of polymeric materials and the optimization of interfacial performance in PV systems.

A significant amount of mechanical and thermal energy is lost when typing on a laptop keyboard. To address this, hybrid energy harvesters must increase the generated power density and mitigate energy fluctuation issues. This paper explores the potential enhancement of energy harvesting by combining thermoelectric and piezoelectric effects within a multilayered structure integrated into a laptop keyboard button. Through numerical simulation, the study assesses how these two behaviors can synergistically increase the power density generated by the hybrid device. The focus is on optimizing energy efficiency by harnessing the heat losses from integrated circuits and the mechanical stresses due to the act of typing. The point is to refine the design of such a system to maximize the conversion of ambient energy into electricity. The findings indicate that the hybrid structure combining both piezoelectric and thermoelectric effects, effectively captures energy from a laptop keyboard, producing a substantial amount of electricity. This investigation shows that the generator can produce up to 2.07 mW of power using PU-40%PZT as piezoelectric material and an additional 71.93 μW through the PEDOT: PSS as thermoelectric material from a single keystroke when pressed and heated. This study underscores the potential for improving energy-harvesting efficiency in laptop keyboards, contributing to more sustainable and energy-efficient electronic devices.

In pico-hydropower, the use of pumps as turbines is a cost-effective solution, especially for remote areas. The abundant seawater makes it a good fluid for pumped storage. The operation of centrifugal pumps in normal and reverse modes involves thickness loss because of solid particle concentration and vapor cavitation. Some research has been performed to predict cavitation in centrifugal pumps, but this issue still exists in several pico-hydropower plants. Therefore, to analyse the synergy between erosion and cavitation in a seawater centrifugal pump, we performed a CFD analysis to compute the effect of material mass loss due to erosion on cavitation risk. The Euler–Lagrangian method was used to track the released particles combined with the RNG k-ε turbulence model. The first part studied the effect of the surface mean roughness height (R_a) on the performance of the centrifugal pump. Increasing R_a from 0 to 15 μm decreases the pump hydraulic efficiency from 93% to 91%, respectively. The second analysis focused on the distribution of erosion thickness and its temporal evolution for 40 μm and 50 μm particles. For both the pump mode and the turbine mode, the erosion thickness is a polynomial function of power 2 with time. The most eroded regions are the blade leading edge (LE) and the blade trailing edge in pump and turbine mode, respectively. The last section focuses on analysing the effect of erosion thickness on cavitation damage. As the surface roughness increases, the cavitation damage power increases. The cavitation power risk increases from 111 kW to 156 kW in pump mode. In turbine mode, when the erosion thickness is between 0.0011 μm and 0.0022 μm, the cavitation damage is the same, approximately 170 kW, whereas the total gas distribution is uniformly distributed in the blade channel. With respect to seawater, the NPSHr increased compared with that with freshwater, from 3.35 m to 3.67 m.

Impeller blades are one of the main parts of a centrifugal pump that affect the performance of the pump. The presence of solid particles in seawater, transported through a centrifugal pump, causes wear in the blade surface that reduces blade lifetime. In the orthogonal direction, this wear is an erosion thickness of the blade. Assuming that these particles have a spherical shape, the erosion rate depends on their velocity, size, impingement angle, and material hardness index. In this work, we investigate the erosion thickness of a low-head centrifugal pump operating in pump and turbine modes, with a particle radius ranging from 4 μm to 50 μm. The numerical simulation used an RNG k–ε turbulence model, assuming a perfect bounce collision between the particle and the rotating solid wall. The study shows that the blade pressure side is impacted by a solid particle concentration higher than the suction side. In pump mode, the erosion thickness on the blade sides increases if the particle radius is above 4 μm and reaches a maximum at 40 μm. In turbine mode, the erosion thickness decreases when the particle radius is greater than 5 μm. The thickness loss is greater in turbine mode than in pump mode. The influence of particle flow rate was investigated. Below a particle radius of 10 μm, particles follow the flow directions and reside for a longer time in the blade channel. Passing from a particle radius of 50 μm to 100 μm, the blade lifetime was decreased by a factor of 11.

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems.