Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power.     

Experimental and numerical study on tube-side flow and heat transfer characteristics of superheated vapor flow in catalyst-filled LH2 spiral wound heat exchanger

High energy consumption is considered to be one of the most persistent problems in liquid hydrogen (LH₂) plants. The combination of heat exchanger and ortho-para (O–P) hydrogen conversion has attracted considerable attention as a cutting-edge technology to reduce energy consumption. The flow and heat transfer characteristics of O–P hydrogen conversion catalyst-filled spiral wound heat exchanger (SWHE) were investigated in this study in two steps. In the first step, pressure-drop experiments were performed in a tube filled with porous media. The results indicated that the pressure drop was overestimated when using Ergun's equation. Therefore, a new empirical pressure-drop correlation for a channel filled with O–P catalyst was formulated. Subsequently, a novel heat transfer model was established based on this correlation for further numerical simulations. The distributions of the temperature, pressure, and para hydrogen content in a catalyst-filled tube were determined. In addition, the influence of the flow rate on the heat exchange coefficient and outlet para hydrogen was clarified; it was found that, with an increase in the flow rate, the heat exchange coefficient increased, whereas the outlet para hydrogen content decreased. At a flow rate of 0.5 m³/h, the para hydrogen content increased by 44% after hydrogen flowed through the channel filled with the O–P catalyst. Furthermore, a prediction model for the para hydrogen content with a flow rate range of 0–1.5 m³/h was derived. This study provides promising theoretical evidence for the engineering application of SWHEs filled with O–P catalysts in large-scale hydrogen liquefaction units.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Mechanism in chlorine-enhanced Pd catalyst for H2O2 in situ synthesis in electro-Fenton system

Palladium-based catalysts have been widely employed in the electro-Fenton process for in situ generation of H₂O₂. However, the process is still far from being practical on a large scale. In this work, a series of Cl_xFePd/γ-Al₂O₃/Al catalysts were prepared by a three-step-impregnation method. They exhibited excellent activity in H₂O₂ in situ synthesis and high efficiency in phenol degradation. The characterization results showed that Cl could assist in increasing the content of Pd⁰ and reducing the isoelectric point of catalysts, which led to the drastic promotion in the synthesis of H₂O₂. Theoretical calculations further demonstrated that Cl doping could facilitate the main reaction in H₂O₂ synthesis, as well as inhibit side reactions such as dissociation of the O O bond. Furthermore, kinetic models were proposed and fitted. A plausible reaction mechanism as well as degradation pathways were elaborated based on electron spin resonance and gas chromatography–mass spectrometry results. These findings illustrate the value of palladium-based Cl_xFePd/γ-Al₂O₃/Al catalysts for their application in the electro-Fenton process.     

Experimental evaluation of the luminescence performance of fired clay brick coated with SrAl2O4:Eu/Dy phosphor

This study evaluates the luminescence performance of fired clay bricks coated with SrAl₂O₄:Eu/Dy phosphor. To do so, SrAl₂O₄:Eu/Dy phosphor was first produced using the traditional solid-state reaction synthesis technique. The prepared phosphor was then used for coating fired clay bricks to analyze the luminescence performance via spectral analysis, decay characteristics, and microstructure of the bricks. The results reveal that excitation and emission spectra of the phosphor coated bricks range from 200 to 480 nm and 455 to 650 nm, respectively, suggesting that the phosphor coated bricks have the capacity of absorbing light with a wide range of wavelengths. The peak wavelength projected at 511 nm in the emission spectrum is achieved, which indicates 4f⁶5 d¹-4f⁷ transition of Europium (Eu²⁺). The repeated excitation and deexcitation of Eu²⁺ by using hole traps and trap levels offered by Dysprosium (Dy³⁺), exist between the ground and the excited state of Eu²⁺ leads to luminescent phenomenon. Moreover, the decay characteristics has revealed that phosphor coated bricks can emit light for a considerable amount of time (>8.5 min) upon the removal of the excitation source. The results reveal that phosphor coated bricks has the potential of increasing energy efficiency of residential and commercial buildings.     

Total Site Hydrogen Integration with fresh hydrogen of multiple quality and waste hydrogen recovery in refineries

This paper proposes a novel method combining Pinch Methodology and waste hydrogen recovery, aiming to minimise fresh hydrogen consumption and waste hydrogen discharge. The method of multiple-level resource Pinch Analysis is extended to the level of Total Site Hydrogen Integration by considering fresh hydrogen sources with various quality. Waste hydrogen after Total Site Integration is further regenerated. The technical feasibility and economy of the various purification approaches are considered, demonstrated with a case study of a refinery hydrogen network in a petrochemical industrial park. The results showed that fresh hydrogen usage and waste hydrogen discharge could be reduced by 21.3% and 67.6%. The hydrogen recovery ratio is 95.2%. It has significant economic benefits and a short payback period for Total Site Hydrogen Integration with waste hydrogen purification. The proposed method facilitates the reuse of waste hydrogen before the purification process that incurs an additional environmental footprint. In line with the Circular Economy principles, hydrogen resource is retained in the system as long as possible before discharge.     

Degradation analysis of the core components of metal plate proton exchange membrane fuel cell stack under dynamic load cycles

The durability of metal plate proton exchange membrane fuel cell (PEMFC) stack is still an important factor that hinders its large-scale commercial application. In this paper, we have conducted a 1000 h durability test on a 1 kW metal plate PEMFC stack, and explored the degradation of the core components. After 1000 h of dynamic load cycles, the voltage decay percentage of the stack under the current densities of 1000 mA cm^?2 is 5.67%. By analyzing the scanning electron microscopy (SEM) images, the surfaces of the metal plates are contaminated locally by organic matter precipitated from the membrane electrode assembly (MEA). The SEM images of the catalyst coated membrane (CCM) cross section indicate that the MEA has undergone severe degradation, including the agglomeration of the catalyst layer, and the thinning and perforation of the PEM. These are the main factors that cause the rapid increase in hydrogen crossover flow rate and performance decay of the PEMFC stack.     

Nanoarchitectonics of CdS/ZnSnO3 heterostructures for Z-Scheme mediated directional transfer of photo-generated charges with enhanced photocatalytic performance

The construction of heterostructure is an effective strategy to synergetically couple wide-band-gap with the narrow-band-gap semiconductor with a mediate optical property and charge transfer capability. Herein, the Z-Scheme CdS/ZnSnO₃ (CdS/ZSO) heterostructures were constructed by anchoring CdS nanoparticles on the surface of double-shell hollow cubic ZnSnO₃ via the hydrothermal method. The direct recombination of excited electrons in the conduction band (CB) of ZSO and holes in the valence band (VB) of CdS via d-p conjugation at the interface greatly accelerated the internal electric field (IEF). The transfer mode follows the Z-Scheme mechanism, where CdS/ZSO synergistically facilitates the efficient charges transfer from CdS to ZnSnO₃ through the intimate interface. Here, ZnSnO₃ and CdS serve as an oxidation photocatalyst (OP) and reduction photocatalyst (RP), respectively. Thus, it can promote synergistically the oxidation half-reaction and reduction half-reaction of H₂ evolution. The density-functional theory (DFT) calculation further confirms the charges transfer from CdS to ZnSnO₃. The hydrogen evolution of 5% CdS/ZSO heterostructure reached 1167.3 μmol g^?1, which was about 8 and 3 folds high compared to pristine ZSO (141.9 μmol g^?1) and CdS (315.5 μmol g^?1), during 3 h of reaction respectively. Furthermore, the CdS/ZSO heterostructures could suppress the photo corrosion of CdS, resulting in its high stability. This work is expected to enlighten the rational design of heterostructure for OP and RP to promote the hybrid heterostructures photocatalytic H₂ evolution.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.