Energy and AI最新文献

Sustainable aviation fuel (SAF) blend has been confirmed to benefit for greenhouse gases reduction, and thus the property of blend fuel should be understanded the detail to support the utilization in aircraft. Low flammability limit (LFL) is a key property of jet fuel which should be sufficiently flammable to burn in combustor of aeroengine and meanwhile should be non-flammable for safety storage in fuel tank of aircraft. LFL of fuel could be influenced by integrating effects including molecule structure, intramolecular chemical bond energy and binding energy of molecule to molecule. Three types of theoretical models, based on different individual view including LFL of every pure hydrocarbon, stoichiometric concentration, and combustion enthalpy, present unsatisfactory simulation results, which can be deduced without integrating all potential influence factors together. The artificial neural network (ANN) approaches have been involved to bridge the relationship of the complex compositions in jet fuels with LFL. For providing adequate and available composition input, the boundary of fuel compositions has been extracted based on constrains of boiling point, flash point and freeze point coupling with statistic petroleum-based jet fuels. By clustering analysis, 43 critical classes of compositions, extracted as surrogate hydrocarbons based on with similar LFL within 1 % deviation, have been deployed as input matrix. ANN-LFL model, trained by only drop-in fuel with feature of Sigmoid function as an activation function, can distinguish drop-in fuel with non-drop-in fuel. ANN LFL model can predict LFL of drop-in fuel with 0.988 accuracy. The predict output value of non-drop-in fuel could present obvious deviation with traditional jet fuel. The optimization methodologies of ANN-LFL model could be improved the understanding of LFL and extend ANN in SAF utilization.

Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.

Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.

The performance degradation is a crucial factor affecting the commercialization of proton exchange membrane electrolyzer. However, it is difficult to establish a mechanism model incorporating all degradation categories due to their different time and spatial scales. In this paper, the data-driven method is employed to predict the electrolyzer voltage variation over time based on a convolutional neural network-long short term memory (CNN-LSTM) model. First, two datasets including constant operation for 1140 h and start-stop load for 660 h are collected from the durability tests. Second, the data-driven models are trained through the experimental data and the model hyper-parameters are optimized. Finally, the electrolyzer degradation in the next few hundred hours is predicted, and the prediction accuracy is compared with other time-series algorithms. The results show that the model can predict the degradation precisely on both datasets, with the R² higher than 0.98. Compared to conventional models, the algorithm shows better fitting characteristic to the experimental data, especially as the prediction time increases. For constant and start-stop operations, the electrolyzers degradate by 4.5 % and 2.5 % respectively after 1000 h. The proposed method shows great potential for real-time monitoring in the electrolyzer system.

Offshore wind energy offers several advantages relative to its onshore counterpart – not least stronger and steadier winds, the possibility of larger turbines, and no land occupation. The operational complexities, environmental challenges, and higher maintenance costs of offshore wind turbines necessitate innovative solutions. Traditional approaches are insufficient, and new ”big data” techniques, notably machine learning and deep learning, are poised to play a significant role in the design and optimisation of offshore wind turbines and farms. The objective of this paper is to conduct a scientometric analysis of machine learning and deep learning techniques applied to offshore wind energy. The research methodology employs a circular framework, integrating data acquisition and statistical analysis to provide a comprehensive scientometric insight into the state of the art. As regards the country of origin, most of the publications stem from just five countries, which signals a need of greater geographical diversity in this field of research. Most importantly, the rapid, steady increase in the annual number of publications since 2017 reveals the interest of the research community in this novel topic.

In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.

Electrified transportation requires batteries with high energy density and high-rate capability for both charging and discharging. Li-ion batteries (LiBs) face a dilemma: increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer path. Tailoring cell design parameters to balance these metrics is essential but challenging. Here, we present a multi-objective optimization framework targeting energy density, fast charging, high-rate discharging, and lifespan simultaneously. Four cell parameters—cathode areal capacity, N-P ratio, cathode porosity, and anode porosity—along with operating temperature, are selected as design variables. A physics-based pseudo-2D model, validated against experimental data, generates data to train the surrogate model, which is combined with the NSGA-II algorithm for rapid optimization. Three different objective calculation methods are compared to identify the maximum sum of energy densities, lowest polarization, and most balanced performance, respectively. Cell design parameters are optimized at different temperatures using the most balanced optimization method. Results demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density, mitigating the energy-power trade-off and broadening battery design parameter ranges.

Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.

This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe₂O₃-based ฺBCLpro combining steam gasification for H₂ production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H₂ yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe₂O₃/Al₂O₃ mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H₂ yield in BCLpro systems. Access to the tool can be obtained through the following link: http://bclh2pro.pythonanywhere.com/.

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.