Changes In Operating Conditions Research Articles

Scenarios for Hydrogen Production from a Full-Scale Reversible Solid Oxide System with Electrolyte-Supported Stacks

Reversible solid oxide cell technology is of increasing interest and is emerging as a promising grid-flexible support system [1] with storage [2]. Among the types of cell architecture available, electrolyte-supported cells have proven to be better in terms of long-term stability and degradation of the cell, albeit sacrificing on performance as compared to anode-supported cells [3]. This makes electrolyte-supported cells better suited architecture to be used in reversible systems where higher stability is required due to the changing operating conditions in each mode. In the present work, validated cell and stack models of Nexceris electrolyte-supported cells are used to model a full-scale reversible solid oxide system designed for hydrogen production.EIS data from symmetric button cells and polarization curves from 5cm×5cm full cells are used to validate the stack model. A net 100 kWe system in fuel cell mode is modelled with electrolyte-supported stacks from Nexceris operating at a nominal temperature of 800 oC. The system is run at thermoneutral conditions in electrolysis mode and at 0.5 A/cm2 in fuel cell mode to balance between efficiency and hydrogen production. The levelized cost of hydrogen production is calculated using a price-taker model with the system pumping the produced hydrogen into a natural gas/hydrogen pipeline. The effect of various parameters, such as the capacity factor of the system, the operating pressure, grid-electricity pricing, etc. - are investigated on the levelized cost of hydrogen.[1] Guenther Glenk and Stefan Reichelstein, “Reversible Power-to-Gas systems for energy conversion and storage”, Nature Communications 13, 2010 (2022).[2] Evan Reznicek and Robert J. Braun, “Techno-economic and off-design analysis of stand-alone, distributed-scale reversible solid oxide cell energy storage systems”, Energy Conversion and Management 175, 2018.[3] Marco A. Buccheri, Anand Singh, and Josephine M. Hill, “Anode- versus electrolyte-supported Ni-YSZ/YSZ/Pt SOFCs: Effect of cell design on OCV, performance and carbon formation for the direct utilization of dry methane”, Journal of Power Sources 196, 2011. Figure 1

Experimental study of the characteristics of an active heat reclaim unit under changing operating conditions

Experimental study of the characteristics of an active heat reclaim unit under changing operating conditions

Common misinterpretations of thermal signatures on polycrystalline PV modules under different operational conditions

Thermal Infrared (TIR) images may be misinterpreted due to the dynamic nature of the operational current–voltage (I-V) and thermal characteristics of photovoltaic (PV) cells and modules. This study investigates and analyses the I-V and thermal characteristics of polycrystalline PV modules to understand the behaviour of abnormal thermal signatures (hot cells) under varying operational points on a module’s I-V curve. A change in operational conditions influences the operational voltage points such that the mismatched cells behave differently under changing load conditions, varying irradiance and partial shading. Mismatched cells can operate in reverse bias and cause abnormal thermal signatures when the module’s operational voltage is less than the maximum power voltage(VMP), 24 V. The mismatch becomes more significant when the operational voltage is 0.2 V, close to short circuit conditions. Cell partial shading of < 10% results in dynamics of the abnormal thermal signatures and can mislead decisions during TIR imaging inspections when bad cells do not show abnormal thermal signatures on TIR images. Carrying out TIR imaging on clean PV modules operating at < VMP will enhance mismatch and the bad cells can show as hot cells, unlike when operating at > VMP. Under low irradiance of < 600Wm−2, mismatched cells can easily emerge on TIR images, however, at irradiance levels > 600Wm−2, only cells having large isolated parts will cause significant mismatch and appear hot on TIR images. This study advances the understanding of the dynamics of operational I-V points and thermal signatures and can improve the operation and TIR imaging inspections of PV modules.

Real-time power optimization based on PSO feedforward and perturbation & observation of fuel cell system for high altitude

The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method is designed to adjust the air compressor voltage. The results show that the proposed strategy has real-time power optimization capability and can adapt to altitude change. The average setting time is 1.8 s when the operating condition changes and the power decreases to 9.1% at 3000 m altitude under a higher load (300 A).

Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions

Detecting faults in high-speed and high-power diesel engines under complex variable operating conditions is highly challenging. Online vibration monitoring systems have been used in such diesel engines in key fields, in which vibration sensors are installed on each cylinder to enable comprehensive monitoring. In this paper, a fault detection method for diesel engines under variable operating conditions is proposed based on multi-sensor signal multi-scale fusion. Firstly, a preprocessing framework is established for the raw vibration signals collected from each cylinder to eliminate random interference and system noise. Then, the resulting signals are phase-aligned based on the engine firing sequence and analyzed using a signal correlation algorithm to produce a multi-sensor multi-scale similarity matrix (MSMSSM). Finally, a multi-branch residual convolutional neural network (MBRCNN) model is constructed with the MSMSSM as the input to detect abnormal health states of the diesel engine. Fault simulation experiments are conducted on a 12-cylinder V-type high-speed and high-power diesel engine test rig. The comparative test results indicate that the proposed MSMSSM-MBRCNN method shows both the highest accuracy of 95.28% and the lowest standard deviation of 3.57% compared to other typical methods. The multi-sensor signals multi-scale fusion method proposed in this paper fully utilizes the key information that remains basically consistent in the synchronous acquisition signals of multiple sensors under different operating conditions. This can effectively reduce the interference of operating condition changes and improve the accuracy and robustness of fault detection.

Statistical process control versus deep learning for power plant condition monitoring

This study compares four models for industrial condition monitoring including a principal components analysis (PCA) approach and three deep learning models, one of which is a new, lightweight version of another. We also propose a simple attention mechanism for enchancing deep learning models with better predictions and feature importance. Two datasets are used, one simulated from the Tennessee Eastman Process, the other from two feedwater pumps at a Danish combined heat and power plant. Our final results show evidence in favour of the PCA-based approach as it has detection ability comparable to the deep learning approaches as well as faster training time, fewer hyperparameters, as well as robustness to changing operating conditions. We conclude the paper by putting into perspective the importance of building up complexity incrementally with a recommendation to start modelling with simpler and well-tested models before the adoption of more advanced, less transparent models.

Nonlinear coordination strategy between renewable energy sources and fuel cells for frequency regulation of hybrid power systems

This study proposes an advanced control strategy for the coordination of an energy storage system (ESS) based on fuel cells (FCs) and renewable energy sources (RESs) to enhance frequency dynamic performance in hybrid power systems (HPSs). The proposed coordination control strategy is based on the nonlinear proportional-integral (NPI) controller, which increases the system's flexibility in dealing with disturbances and changing operating conditions. In addition, it improves the system's dynamic response and attempts to address system weakness caused by highly penetrating RESs. The proposed NPI controller is optimally designed using a new optimization algorithm, called dandelion optimizer (DO), whose proficiency and effectiveness are verified by comparing its performance with other well-known optimization algorithms used in the literature; particle swarm optimization (PSO), grey wolf optimization (GWO), and ant lion optimization (ALO) algorithms considering various standard objective functions. Furthermore, the proposed NPI controller performs better than other control strategies used in the literature under load/RESs fluctuations. The effectiveness of the proposed nonlinear coordination control strategy is examined and investigated through a self-contained HPS that includes a diesel generator, RESs (i.e., photovoltaic and wind power plants), battery ESS, flywheel ESS, aqua electrolyzer for hydrogen production, FCs, electric vehicles, and customer loads. The simulation results carried out by the MATLAB software demonstrate the superior performance of the proposed DO-optimized NPI controller for HPS frequency regulation, even when the power system's parameters have substantial variations. Moreover, the results revealed that the proposed strategy significantly reduces the frequency deviation by approximately 95% compared to the conventional coordination strategy based on the fixed contribution of RESs and by 90% compared to the adaptive coordination control based on the PI controller.

THE EFFECT OF ORGANIZATIONAL RESOURCES, WORK INVOLVEMENT ON EMPLOYEE PERFORMANCE AND CUSTOMER LOYALTY WITH THE ROLE OF SERVICE CLIMATE

Quality human resources will basically be able to help the organization achieve its goals. Employees create a key element for organizations to achieve sustainable competitive advantage in today's dynamic and changing operating conditions. Every company wants its employees to have high loyalty and dedication to performance and the company, high loyalty can determine the company's progress and decline in the future. In addition to the need to increase employee loyalty, companies also need to create a conducive climate in the management of an organization or company. This study took a sample of 100 samples and used the Structural Equation Modeling (PLS –SEM) Data Analysis Method. SEM is one of the areas of statistical study that can be used to address research problems, where independent variables and response variables are immeasurable variables. he result of the PLS is that only the P Value is below 0.200, meaning that all variables influence each other strongly.

A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity

A parameter sensitivity analysis is an important part of the stability study of hydro turbine regulation systems, which helps operators to deepen their understanding of the characteristics and connections among the various parts of these systems. Considering that large hydropower stations undertake an essential regulation task in the power grid, the safety and stability of their operation cannot be ignored. To this end, taking a unit in a giant hydropower station in China as an example, a hydraulic–mechanical–electrical coupling model of the hydraulic turbine regulation system is established. A comprehensive parameter sensitivity indicator and parameter sensitivity analysis framework are proposed. On this basis, the sensitivity of the main system variables to parameter changes under full operating conditions is investigated by considering two different control modes of the unit (i.e., corresponding to different grid types). The results show that the sensitivity of the system state to the mechanical parameters of the generator is the highest in the power control mode, while the sensitivity to the electrical parameters of the generator and excitation system is higher in the frequency control mode. The sensitivity of the system with these key parameters also shows different patterns of change with a change in the unit operating conditions. The relevant findings can provide some theoretical guidance for the operation of hydropower stations and help to reduce the risk of system instability.

An explainable unsupervised learning framework for scalable machine fault detection in Industry 4.0

Despite the diverse number of machine learning algorithms reported in the literature for machine fault detection, their implementation is mainly confined to laboratory-scale demonstrations. The complexity and black-box nature of machine learning models, the processing cost involved in appropriate feature extraction, limited access to labeled data, and varying operating conditions are some of the key reasons that curtail their implementation in practical applications. Furthermore, most such models serve as decision support tools, aiding domain experts in root cause analysis, and are not truly autonomous by themselves. To address these challenges, we present a lightweight autoencoder-based unsupervised learning framework to accurately identify machine faults against the changing operating conditions in a real-world scenario. The fault detection strategy is further strengthened by a model agnostic Shapley Additive exPlanations (SHAP)-based method (kernel SHAP) for identifying the most prominent features contributing to fault detection inference, the findings of which are then explored for identifying trends and correlations among prominent features and various types of faults. The framework is validated using two widely used and publicly available datasets for machine condition monitoring, as well as a large industrial dataset comprising 18 machines installed at three factories in India, monitored for several months.

SYSTEM DEVELOPMENT FOR ENHANCING BOILER PERFORMANCE: FROM PID CONTROL TO ADAPTIVE AND MODEL PREDICTIVE CONTROL FOR MORE EFFECTIVE OPTIMIZATION OF TEMPERATURE, PRESSURE, AND LEVEL CONTROL IN BOILER SYSTEM

When discussing control systems for boilers, the PID (Proportional-Integral-Derivative) control algorithm is one of the most well-known and widely used. The PID control algorithm has been proven effective in controlling temperature and pressure within boiler systems. However, as technology advances, there have been advancements in more sophisticated and efficient control systems. One of these advancements is the utilization of adaptive control and model predictive control. Adaptive control can adapt to changes in operational conditions and provide better performance compared to PID control. Meanwhile, model predictive control utilizes a mathematical model of the system to predict future behavior and make control decisions based on these predictions. Nevertheless, the PID control algorithm remains crucial and can deliver satisfactory performance in temperature and pressure control within boilers. The key to effective use of PID control lies in the selection of appropriate parameters and proper tuning. By employing the correct tuning methods, PID control can achieve optimal performance and high efficiency in boiler systems. Overall, the use of the PID control algorithm remains a good choice for temperature and pressure control in boiler systems, despite advancements in more advanced and efficient control systems. Model predictive control, on the other hand, utilizes a mathematical model of the boiler system to predict its future behavior. By solving an optimization problem over a predictive horizon, it determines the optimal control actions to be applied. This proactive approach allows model predictive control to account for constraints, nonlinearity, and system dynamics, leading to improved control performance and energy efficiency. In conclusion, while PID control remains a viable option for temperature and pressure control in boiler systems, the advancements in adaptive control and model predictive control offer more sophisticated and efficient alternatives. The choice of control strategy depends on the specific requirements, complexity, and desired performance of the boiler system. Combining different control techniques or employing advanced control algorithms can further enhance the overall control performance and optimize the operation of the boiler system.

On-demand tuning of mechanical stiffness and stability of Kresling origami harnessing its nonrigid folding characteristics

Being adaptive with respect to changing operating conditions and the environment is an imperative demand for advanced engineering structures. In this research, two Kresling origami units are stacked together to form an online and on-demand tunable dual-Kresling origami structure (D-KOS), allowing easy and significant change in the multistability profile and apparent stiffness without redesign. Specifically, by harnessing its nonrigid folding nature together with geometric characteristics, the D-KOS can be programmed to follow distinct paths of its strain energy contour as varying (rotational angle of the top plate of the D-KOS), which can be utilized to explicitly derive different mechanical properties. The D-KOS with identical Kresling origami units can exhibit various symmetric bistable behaviors with different energy barriers via adjusting . Such a structure becomes a monostable system as the tuning variable reaches a critical value. This change in the energy landscape and stability profile gives rise to a change in stiffness at its stable equilibria, which, as a result, creates a wide range of stiffness value one can achieve, including quasi-zero stiffness. Such tuning of does not require redesign of the structure and thus can achieve online and on-demand tailoring. To verify the concept, proof-of-concept prototypes are developed and utilized to validate the D-KOS’s mechanical tunability experimentally. Moreover, the different design parameters of the D-KOS can change its tunability. For example, the D-KOS with nonidentical Kresling origami units can possess even richer stability properties (being asymmetric bistable, symmetric bistable, and monostable) with various energy landscapes. In addition, one can also tailor the range of the adjustable stiffness by changing the design parameters, such as the ratio of the angle between the polygon side and the valley crease of the triangular facets, and the height of the Kresling origami unit.

Experimental investigation of a novel variable geometry radial ejector

This work focuses on improving ejector performance in variable conditions by developing a radial flow variable geometry radial ejector (VGRE). The research objective was to demonstrate the performance of the improved VGRE, which can operate more effectively under changing operating conditions compared to a fixed geometry design. The improved VGRE was developed and experimentally tested to enable adjustment of the nozzle and ejector throat areas to suit different ejector operating conditions in various applications. Radial ejectors have a smaller size than axial ejectors, which is a significant advantage in some applications. Results showed that the improved VGRE achieved higher entrainment ratio and critical compression ratio compared to a fixed geometry design and that optimal performance was obtained with nozzle and duct throat separations of 0.5 mm and 3.0 mm, respectively. The results also indicated that the wall static pressure in the throat region of the improved VGRE was similar to that observed in conventional axial ejectors and showed an average improvement of 107 % in entrainment ratio and 76 % in critical compression ratio relative to previous studies of air ejectors. The findings suggest that the improved VGRE concept could be applied to a wide range of applications in the future.

The method of ensuring the road-holding ability of the motor grader during the performance of technological operations

Problem. The article proposes a method of ensuring course stability of a motor grader during technological operations. As a system for adapting the motor grader to changing operating conditions, it is proposed to simultaneously use the mechanism for turning the front wheels in the horizontal plane and the mechanism for tilting the front wheels in the vertical plane. Goal. Development of a method of adapting a motor grader to changing external load conditions during technological operations. Methodology. As a criterion for adaptation, the index of permissible deviation of the motor grader from the planned trajectory of movement at the distance of the given length was used. Originality. The proposed criterion depends on the type of technological operation performed and the type of parameters of the developed environment. Results. The method of ensuring road-holding ability of the motor grader involves the analysis of the movement of the machine on the work site and the development of recommendations for the effective use of the adaptation system. Practical value. On the basis of the obtained results of experimental studies, a graphical and analytical analysis was performed, which made it possible to provide recommendations on the choice of turning angles and inclination of the wheels as a function of the angle of the transverse inclination of the support surface and the coefficient of adhesion.

Large-scale comparison and demonstration of continual learning for adaptive data-driven building energy prediction

Data-driven models have been increasingly employed in smart building energy management. To avoid performance degradation over time, data-driven models need to be continually updated to adapt to the changes in building operations. However, several critical issues in the model update process raised wide concerns, especially the concept drift and catastrophic forgetting issues. The concept drift issue happens when the statistical properties of target variable change over time in unforeseen ways. The catastrophic forgetting issue refers to the process that the previously learnt knowledge or patterns may be diluted and eventually lost in model update. Although a few model update methods were proposed, there is a lack of comprehensive comparison of the methods for adaptive data-driven building energy prediction. This paper conducted a comprehensive investigation on the performance of three conventional model update methods and five emerging continual learning methods using 2-year data of 100 buildings extracted from an open-source dataset. The results show that continual learning methods are more effective in ensuring long-term accuracy while cutting down on computation time and data storage expenses. The CV-RMSE of Elastic weight consolidation and Gradient episodic memory decreased by around 14% and 8% on average compared with static model and accumulative learning. The comparison results are valuable to the development of adaptive data-driven building energy prediction models which are more reliable over time and robust against changing operation conditions, thus more practically applicable in smart building energy management.

Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller

The Photovoltaic (PV) system is an eco-friendly renewable energy system that is integrated with a DC-DC buck-boost converter to generate electrical energy as per the variations in solar irradiance and outdoor temperature. This article proposes a novel Adaptive Fractional Order PID (A-FOPID) compensator with self-adjusting fractional orders to extract maximum power from a stand-alone PV system as ambient conditions change. The reference voltage is generated using a feed-forward neural network. The conventional FOPID compensator, which operates on the output voltage error of the interleaved buck-boost converter, is employed as the baseline maximum-power-point-tracking (MPPT) controller. The baseline controller is retrofitted with an online state-error-driven adaptation law that dynamically modifies the fractional orders of the controller’s integral and differential operators. The adaptation law is formulated as a nonlinear hyperbolic scaling function of the system’s state error and error-derivative variables. This augmentation supplements the controller’s adaptability, enabling it to manipulate flexibly the tightness of the applied control effort as the operating conditions change. The efficacy of the proposed control law is analyzed by carrying out customized simulations in the MATLAB Simulink environment. The simulation results show that the proposed MPPT control scheme yields a mean improvement of 25.4% in tracking accuracy and 11.3% in transient response speed under varying environmental conditions.

Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm

Since low-frequency oscillation seriously threatens the safe operation of the power system, the power system stabilizer (PSS) can effectively suppress the oscillation. In this paper, a hybrid parameter optimization method combining the moth-flame optimization (MFO) algorithm and fuzzy logic controller (FLC) is proposed to address the problem of poor adaptability of the parameter tuning method in the conventional power system stabilizer (CPSS). This method can optimize the parameters of PSS in different processes. Initially, the optimal parameters of PSS under the current perturbation are given by the MFO algorithm. During the online operation of the system, as perturbation changes, the parameters of the PSS will also be adaptively tuned by the FLC in real-time when the system operating conditions change. According to this method, a fuzzy adaptive proportional–integral–differential (FPID) controller is designed based on the moth-flame optimization algorithm (MFO-FPID), and it is used as PSS to improve dynamic stability performance during oscillation. Moreover, its parameters can be adaptively adjusted in different perturbation scenarios. The designed MFO-FPID controller is applied to the single machine infinite bus (SMIB) power system to compare the dynamic performance with other controllers, that is, proportional–integral–differential (PID) and CPSS. The result shows that the MFO-FPID controller can suppress the oscillation very well, and the control effect is the best.

Effect of operating conditions and micro-porous layer on the water transport and accumulation in proton exchange membrane fuel cells

Accurate measurement of water transport in an operating proton exchange membrane fuel cell (PEMFC) and water accumulation in the electrodes is crucial for understanding the impact of operating conditions and transport layer configurations on cell performance. A water balance setup, based on in-line gas flowmeter, pressure, relative humidity and temperature sensors, was developed and demonstrated to track the real-time water transport and accumulation within operating PEMFCs. Current results indicate that changing operating conditions have a dramatic effect on the water transport across the membrane, while the ratio of water transported to produced remains relatively constant with current density. Under dry conditions, water moves from anode to cathode while increasing humidity and decreasing temperature enhance cathode to anode water movement. Adding a micro-porous layer (MPL) to the cathode gas diffusion layer (GDL) increases the water back-diffusion from the cathode to the anode at all operating conditions, but the increase is not very significant and only results in a prominent performance increase at 60 °C and 70% RH. The improved performance at 60 °C and 70% RH is likely due to: (a) increased water vaporization, since less water accumulation was measured in the electrode; and (b) creation of in-plane oxygen pathways in the MPL around localized liquid water blockages in the GDL. Based on these results, the use of different operating conditions might have been the key contributing factor for the different behavior observed when an MPL was introduced in previous literature.

Composite anion exchange membranes based on polysulfone and silica nanoscale ionic materials for water electrolyzers

Anion exchange membrane water electrolysis (AEMWE) offers the ambition of combining the advantages of alkaline electrolysers, i.e. the use of cheap and plentiful catalytic materials, with those of proton exchange membrane electrolysis of water, i.e. high performance and fast response to changing operating conditions. However, the development of performing and durable anion exchange membranes is still the major challenge for the ultimate industrial adoption of AEMWE. Here, we introduce an innovative nanocomposite AEM based on trimethylammonium functionalized silica nanoscale ionic materials (NIM-N+) incorporated in quaternized polysulfone (qPSU). The presence of NIM-N+ in the hydrophilic clusters of qPSU produces a remarkable enhancement in the dimensional and thermo-mechanical stability of the composite AEM. Furthermore, Nuclear Magnetic Resonance (NMR) and Electrochemical Impedance Spectroscopy (EIS) investigations highlighted that the nanoparticles are directly involved in the transport process of hydroxide ions. This enabled qPSU/NIM-N+ composite membrane to achieve impressive anionic conductivity values, e.g. about 110 mS cm−1 at 80 °C and 95% relative humidity (RH). The AEMWE single cell, equipped with this membrane, operated properly both with 1 or 0.5 M KOH solution, displaying a current density larger than 3.5 A cm−2 at 2.2 V and 80 °C.

On extraction, ranking and selection of data-driven and physics-informed features for bearing fault diagnostics

Many traditional bearing fault detection techniques rely on pattern recognition using black box machine learning models, which lack generalizability to out of sample cases and are generally only effective for an a priori defined operating condition set. Therefore, the developed models are required to be retrained each time the operating condition changes because of the dependence of the dynamic response on them. The lack of effective techniques that can be applied and adapted to different operation domains of the system is a well-established need, which motivates the development of hybrid approaches which integrate both physics and data based techniques. This paper discusses the development of a hybrid physics-informed method that integrates information from physics and data for fault diagnostics of a rotor-bearing system. A physics-based model is used to simulate the dynamics of the system in a defect free condition and a machine fault simulator is used to generate experimental data. Physics-informed features are extracted by residual-based and cross-sample entropy techniques, while the data-based features are obtained using Proper and Smooth Orthogonal Decomposition techniques. Subsequently, these two collections of extracted features are fused into a hybrid feature set. Then, the features are ranked using the Shapley values ranking technique. Comparing the accuracy of classifications upon using the features of physics-informed, data-based and hybrid sets shows improved capability of the proposed framework to generalize diagnostics for various operating conditions.