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![Proton exchange fuel cell diagram [7]. Reproduced with permission from Daud, W.R.W., Renew. Energy; published by Elsevier, 2007.](publication/334377662/figure/fig1/AS:779312156405761@1562813905984/Proton-exchange-fuel-cell-diagram-7-Reproduced-with-permission-from-Daud-WRW.png)
Proton exchange fuel cell diagram [7]. Reproduced with permission from Daud, W.R.W., Renew. Energy; published by Elsevier, 2007.
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![Figure 1. Proton exchange fuel cell diagram [7]. Reproduced with...](publication/334377662/figure/fig1/AS:779312156405761@1562813905984/Proton-exchange-fuel-cell-diagram-7-Reproduced-with-permission-from-Daud-WRW_Q320.jpg)
Fuel cells are promising devices to transform chemical energy into electricity; their behavior is described by principles of electrochemistry and thermodynamics, which are often difficult to model mathematically. One alternative to overcome this issue is the use of modeling methods based on artificial intelligence techniques. In this paper is propo...
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... fuel stack is made up of a group of single fuel cells placed in series. Each cell is formed by a proton exchange membrane (PEM) (PEM) placed between two electrodes (anode and cathode) which are coated with a catalyst layer, usually platinum (see Figure 1). Figure 1. ...
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... cell is formed by a proton exchange membrane (PEM) (PEM) placed between two electrodes (anode and cathode) which are coated with a catalyst layer, usually platinum (see Figure 1). Figure 1. Proton exchange fuel cell diagram [7]. ...
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... Table 4 are presented the scores of each fold. In Figure 10, a comparison of the actual values against the predicted values is presented. The high regression accuracy (R 2 = 0.96) and the fast convergence are mainly due to the fact that in the PCA analysis, the irrelevant and redundant variables which have no impact on the output voltage were eliminated. ...
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... Table 4 are presented the scores of each fold. In Figure 10, a comparison of the actual values against the predicted values is presented. ...
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... neuro-PID controller is an already proven control approach in cases of system fault recovery, such as flooding, drying out, and auxiliary failures, such as of a compressor [20]. A PID-series neuro control scheme (with an inverse model of the fuel cell) was proposed to supply the optimal hydrogen pressure by taking into account the values of the main variables under transient conditions (see Figure 11). The self-autotuning of the PID control was done according to the method proposed by Omatu et al. [30]. ...
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... Figure 12 are compared the voltage, current, and hydrogen pressure for both controllers, the conventional and neuro PID-series. Both controllers achieved similar performance in steady-state and transient conditions. ...
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Citations
... On the other hand, publication [28] describes a hybrid modeling and control scheme for fuel cell systems using neural networks. Several feature selection algorithms were tested to reduce dimensionality, with the aim of eliminating irrelevant variables concerning the control objective. ...
The paper involved conducting preliminary research to explore the identification and control of a multi-dimensional, non-linear, and non-stationary cement grinding process using artificial neural networks and various other non-linear models. The primary objective was to establish a precise model that accurately characterizes the functioning of the grinding system. Several model structures were employed, including NARX models based on feed-forward network, Elman, Jordan, and Layer-Recurrent Network (LRN) recurrent networks, as well as MTL (Multi-Task Learning) and traditional NARX non-linear models. It was observed that, in contrast to the linear models, the non-linear models exhibited significantly superior performance in the modeling of the system. Another notable outcome of this research is the proposal of a neurocontroller, functioning as an expert system, which can provide control signals to operators. The development and implementation of such a neurocontroller have the potential to enhance the quality, simplicity, and efficiency of cement grinding process control.
... Fuel cells, such as proton exchange membranes, are a good source of energy. However, they cannot optimally respond to charge changes, which is mainly due to their slow thermodynamic and electrochemical response [11,12]. In this regard, a cell model is needed to evaluate their behavior. ...
... The work proposed by [10] uses the fuzzy control technique to keep the fuel cell output voltage constant by controlling the gas pressure. In Ref. [11], to control the output voltage of the fuel cell, a feedback controller based on a quadratic linear regulator is proposed. Studies [12][13][14] investigate the problems regarding proton exchange membrane (PEM) fuel cell control, using a PID controller for applications in complex and nonlinear control problems. ...
Humans have always been searching for new and efficient ways to convert fuels into usable energy. Solid oxide fuel cells, energy conversion devices capable of generating electrical energy, are widely used due to their high thermal energy production. In this research, fuzzy control was used to manage the voltage and current of solid oxide fuel cells. Simulations were conducted in two evaluation modes: checking the voltage, current, and power of the fuel cell, with and without the use of fuzzy control, and analyzing maximum power point tracking (MPPT) using fuzzy control. In the second mode, when connected to the load, the performance of the fuel cell was evaluated in the transient state, and the role of the controller was clearly visible According to the current–power characteristic of the fuel cell, which is a nonlinear curve and has a maximum point, and by using the fuzzy controller and the appropriate selection of input and output functions, this research aimed to make the system work at the maximum power point at all times. To this effect, a printer was used between the fuel cell and the load, and a fuzzy controller was used to set the cycle of activities, whose input was the slope of the current–power curve. The results show that this controller performs well and is faster when compared to the P&O control method. MATLAB software was used to design and analyze the system and, in order to validate the model, the transient behavior of the fuel cell was studied. The results were compared, and an acceptable match was observed.
... With this problem, the task of optical defect detection lends itself to the possibility of applying more advanced deep learning methods. Machine learning already has many applications in PEM fuel cells [39] ranging from on-line fault diagnosis [40] [45], aiding in fuel cell design [46][47] [48], and predicting optimal operating conditions [49][50] [51]. However, to the authors' knowledge, there have been no studies using machine learning, besides that by Johnson, centered on detecting defects from membrane images during the roll-to-roll manufacturing process. ...
... Indeed, the precise closed-loop control plant can be obtained without any knowledge related to the dynamic properties of the analyzed system. Consequently, the discussed solutions are applied in a number of engineering applications, where there is no possibility to receive an analytical model of the entire investigated process [16][17][18]. In the paper, the original approaches derived from the artificial intelligence phenomenon are proposed and compared with respect to classical performance indices. ...
In the paper, a comparative case study covering different control strategies of unstable and nonlinear magnetic levitation process is investigated. Three control procedures are examined in order to fulfill the specified performance indices. Thus, a dedicated PD regulator along with the hybrid fuzzy logic PID one as well as feed-forward neural network regulator are respected and summarized according to generally understood tuning techniques. It should be emphasized that the second PID controller is strictly derived from both arbitrary chosen membership functions and those ones selected through the genetic algorithm mechanism. Simulation examples have successfully confirmed the correctness of obtained results, especially in terms of entire control process quality of the magnetic levitation system. It has been observed that the artificial-intelligence-originated approaches have outperformed the classical one in the context of control accuracy and control speed properties in contrary to the energy-saving behavior whereby the conventional method has become a leader. The feature-related compromise, which has never been seen before, along with other crucial peculiarities, is effectively discussed within this paper.
... The application of backstepping control of a high order PEMFC is investigated in [16]. The approaches of [17] and [18] take advantage of neural networks and fuzzy type-2 methods to control PEMFCs, respectively. Furthermore, non-linear observer-based control approaches are reviewed in [19]. ...
Here, an optimal linear parameter varying (LPV) controller is developed for non-linear proton exchange membrane fuel cell (PEMFC) systems. Two performance criteria are involved in the optimal controller, including the membrane pressure and the power of the PEMFC. The former should be minimized; and, the latter should be maximized. Since the goal is to derive sufficient conditions in terms of linear matrix inequality (LMI) constraints, both performances are re-formulated and augmented in a unique convex optimization problem. Further, the non-linear PEMFC with the equilibrium profile dependent on time-varying parameters, that is, current and temperature, is represented by an LPV model. Using the LMI constraints and the obtained LPV model, the gains of the controller are derived. Comparing with the state-of-the-art methods, the proposed approach offers an optimal control approach with a systematic design method, in which both practical issues are treated, simultaneously. To show the advantages of the developed approach, six typical controllers, including the proposed and existing approaches, are considered and several numerical simulation results are conducted. © 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
... Hydrogen gas is supplied to the anode side and oxygen from the air is supplied to the cathode side. At the cathode, electrons coming from the anode and protons that have crossed the membrane combine with oxygen from the air to form water that flows out of the fuel cell [27]. ...
... The hydrogen fuel saving strategy can be carried out based on the load demand [7]. Fuel cell performance is influenced by several variables including reactant supply and load in transient conditions which can also affect fuel cell voltage levels [8]. Fluctuating loads are associated to important changes on the hydrogen requirement [9].Energy management in fuel cells is very important to increase fuel cell lifetime and also to minimize costs and minimum hydrogen consumption [10]. ...
This paper presents a control system to regulate the flow rate of hydrogen and oxygen gas for 500 watt PEMFC. Flow rates into the fuel cell are regulated according to load
demand for fuel efficiency. Increase and decrease the rate of hydrogen flow according to the load demand, so this research proposes a feedback control system to supply load
demand by regulating the fuel flow rate and fuel cell mathematical model.The proposed control algorithm for regulating the flow of hydrogen using the Mamdani Fuzzy method. The performance of the control system and the fuel cell model were tested with various loads. The simulation results show the fuzzy method has been able to regulate fuel cell output more effectively.
... Model-based techniques require a deep knowledge of the system and the inter-relations between the corresponding physical phenomena [23]. Data-driven (non-model-based) methods, are instead based on the data collected in the context of an experimental campaign, which is subsequently analyzed and is then employed in the training procedure [24][25][26][27]. Some recent studies have been dedicated to diagnosing water management faults through EIS. ...
The present paper is focused on proposing and implementing a methodology for robust and rapid diagnosis of PEM fuel cells’ faults using Electrochemical Impedance Spectroscopy (EIS). Accordingly, EIS tests have been first conducted on four identical fresh PEM fuel cells along with an aged PEMFC at different current density levels and operating conditions. A label, which represents the presence of a type of fault (flooding or dehydration) or the regular operation, is then assigned to each test based on the expert knowledge employing the cell’s spectrum on the Nyquist plot. Since the time required to generate the spectrum should be minimized and considering the notable difference in the time needed for carrying out EIS tests at different frequency ranges, the frequencies have been categorized into four clusters (based on the corresponding order of magnitude: >1 kHz, >100 Hz, >10 Hz, >1 Hz). Next, for each frequency cluster and each specific current density, while utilizing a classification algorithm, a feature selection procedure is implemented in order to find the combination of EIS frequencies utilizing which results in the highest fault diagnosis accuracy and requires the lowest EIS testing time. For the case of fresh cells, employing the cluster of frequencies with f > 10 Hz, an accuracy of 98.5 % is obtained, whereas once the EIS tests from degraded cells are added to the dataset, the achieved accuracy is reduced to 89.2 % . It is also demonstrated that, while utilizing the selected pipelines, the required time for conducting the EIS test is less than one second, an advantage that facilitates real-time in-operando diagnosis of water management issues.
... Likewise, the activation of the electrode and the ohmic losses, the leakage current and the inevitable heat losses increase the electrolysis voltage beyond the thermo-neutral voltage. Consequently, removing heat from the cell becomes essential; thus, the expected gas loss at several operating pressures is incorporated to reveal the energy consumption that may occur in practice [20][21][22][23]. ...
Although hydrogen is the most abundant element in the universe, it is not possible to find it in its purest state in nature. In this study, two-stage experimentation was carried out. The first stage was hydrogen production. The second stage was an electrochemical process to hydrogenate soybean oil in a PEM fuel cell. In the fist stage a Zirfon Perl UTP 500 membrane was used in an alkaline hydrolizer of separated gas to produce hydrogen, achieving 9.6 L/min compared with 5.1 L/min, the maximum obtained using a conventional membrane. The hydrogen obtained was used in the second stage to feed the fuel cell hydrogenating the soybean oil. Hydrogenated soybean oil showed a substantial diminished iodine index from 131 to 54.85, which represents a percentage of 58.13. This happens when applying a voltage of 90 mV for 240 min, constant temperature of 50 • C and one atm. This result was obtained by depositing 1 mg of Pt/cm 2 in the cathode of the fuel cell. This system represents a viable alternative for the use of hydrogen in energy generation.
... Morán-Durán et al. [11] propose the use of a trained neural network to predict and control the voltage of a proton-exchange membrane (PEM) fuel cell. The approach uses principal component analysis (PCA) to reduce the dimensionality, aiming to eliminate non-significant variables with respect to the control objective. ...
Mathematical optimization is the selection of the best element in a set with respect to a given criterion.[...]
