Assessment of the Flue Gas Recycle Strategies on Oxy-Coal Power Plants using an Exergy-based Methodology

A presentation of this paper was given at the 16th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction, Rodes(Greece) 29 September- 2 October 2013This article is available online at http://www.aidic.it/cet/13/35/057.pdfInternational audienceWhile oxy-combustion CO2 capture was foreseen to have higher improvement potential than post-combustion a decade ago, research has not been carried out at the same pace since then and today, the latter exhibits higher technological maturity along with low energy penalty thanks to advanced process integration and solvents formulation. Thus, significant efficiency improvement is needed for the oxy-combustion route to be competitive with post-combustion for carbon capture on coal-fired power plants. In order to achieve such improvements, process integration at system level is required to assess the true energy savings potential of oxy-combustion. In this study, an exergy-based methodology is performed to compare various flue gas recirculation strategies on a state-of-the-art 1100 MWe gross oxy-fired power plant. Exergy analysis at unit operation level allows the identification of the location and the magnitude of the thermodynamic irreversibilities occurring in the process, leading to an enhanced understanding of the studied system. In addition to the reference case in which the secondary recycle is fully depolluted and dehydrated; three alternative flue gas recirculation options have been investigated. Among the studied strategies, recirculation of the secondary flow prior the regenerative heat exchanger with a high temperature particle removal device leads to the highest net plant efficiency. This option not only allows the minimal exergy losses in the boiler but also minimizes the flowrate going through the downstream depollution devices. The net plant efficiency obtained for this architecture is 38.0%LHV, which represents a 3% increase compared to the reference oxy-combustion plant. Comparing this figure to an air-fired power plant modeled with the same set of hypotheses, the energy penalty is 8.1%-pts

Improved Fault Classification and Localization in Power Transmission Networks Using VAE-Generated Synthetic Data and Machine Learning Algorithms

The reliable operation of power transmission networks depends on the timely detection and localization of faults. Fault classification and localization in electricity transmission networks can be challenging because of the complicated and dynamic nature of the system. In recent years, a variety of machine learning (ML) and deep learning algorithms (DL) have found applications in the enhancement of fault identification and classification within power transmission networks. Yet, the efficacy of these ML architectures is profoundly dependent upon the abundance and quality of the training data. This intellectual explanation introduces an innovative strategy for the classification and pinpointing of faults within power transmission networks. This is achieved through the utilization of variational autoencoders (VAEs) to generate synthetic data, which in turn is harnessed in conjunction with ML algorithms. This approach encompasses the augmentation of the available dataset by infusing it with synthetically generated instances, contributing to a more robust and proficient fault recognition and categorization system. Specifically, we train the VAE on a set of real-world power transmission data and generate synthetic fault data that capture the statistical properties of real-world data. To overcome the difficulty of fault diagnosis methodology in three-phase high voltage transmission networks, a categorical boosting (Cat-Boost) algorithm is proposed in this work. The other standard machine learning algorithms recommended for this study, including Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN), utilizing the customized version of forward feature selection (FFS), were trained using synthetic data generated by a VAE. The results indicate exceptional performance, surpassing current state-of-the-art techniques, in the tasks of fault classification and localization. Notably, our approach achieves a remarkable 99% accuracy in fault classification and an extremely low mean absolute error (MAE) of 0.2 in fault localization. These outcomes represent a notable advancement compared to the most effective existing baseline methods.publishedVersio

Addressing Technology Uncertainties in Power Plants with Post-Combustion Capture

AbstractRisks associated with technology, market and regulatory uncertainties for First-Of-A-Kind fossil power generation with CCS can be mitigated through innovative engineering approaches that will allow solvent developments occurring during the early stage of the deployment of post-combustion CO2 capture to be subsequently incorporated into the next generation of CCS plants. Power plants capable of improving their economic performance will benefit financially from being able to upgrade their solvent technology. One of the most important requirements for upgradeability is for the base power plant to be able to operate with any level of steam extraction and also with any level of electricity output up to the maximum rating without capture. This requirement will also confer operational flexibility and so is likely to be implemented in practice on new plants or on any integrated CCS retrofit project

CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.Comment: AAAI-21 Camera Read

CFD Applications in Energy Engineering Research and Simulation: An Introduction to Published Reviews

Computational Fluid Dynamics (CFD) has been firmly established as a fundamental discipline to advancing research on energy engineering. The major progresses achieved during the last two decades both on software modelling capabilities and hardware computing power have resulted in considerable and widespread CFD interest among scientist and engineers. Numerical modelling and simulation developments are increasingly contributing to the current state of the art in many energy engineering aspects, such as power generation, combustion, wind energy, concentrated solar power, hydro power, gas and steam turbines, fuel cells, and many others. This review intends to provide an overview of the CFD applications in energy and thermal engineering, as a presentation and background for the Special Issue “CFD Applications in Energy Engineering Research and Simulation” published by Processes in 2020. A brief introduction to the most significant reviews that have been published on the particular topics is provided. The objective is to provide an overview of the CFD applications in energy and thermal engineering, highlighting the review papers published on the different topics, so that readers can refer to the different review papers for a thorough revision of the state of the art and contributions into the particular field of interest