Porphyrin-silica gel hybrids as effective and selective copper(II) adsorbents from industrial wastewater

Porphyrins are an important class of ligands with a tremendous ability to capture metal ions closely related to the rich coordination chemistry of porphyrins. Herein we use this characteristic to develop silica gel grafted derivatives for water remediation applications. Therefore, two porphyrin derivatives, one with three and the other with four mercaptopyridyl units were grafted on silica gel functionalized with 3-aminopropyltriethoxysilane. The new adsorbents Si3PyS and Si4PyS were characterized using a suitable set of techniques confirming the covalent attachment of the porphyrins to the silica surface. Additionally, microscopy and N2 adsorption analysis confirmed the structural integrity and preservation of the mesoporous structure of Si during surface modification. The results show that both hybrid materials exhibit good chemical and thermal stability and an outstanding Cu2+ removal capability, with a chemical adsorption capacity of 176.32 mg g–1 and 184.16 mg g–1, respectively. These materials have also been used in real water and industrial wastewater samples with minimal interference in their adsorption capabilities. Density Functional Theory calculations were performed to confirm the good performance of the hybrid materials Si3PyS and Si4PyS towards metal ions. The functionalization of silica surface with porphyrin-based ligands bearing additional binding motifs drastically improves the adsorption capability of the new hybrids towards metal ions. The presence of pyridyl units brings a meaningful advantage, since both porphyrin core and appended pyridyl groups are able of binding Cu2+ ions with high affinity, contributing to the enhancement of the chelating features of the adsorbents prepared when compared with other ligands supported in silica-based materials.publishe

PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting

This paper proposes an effective deep learning framework for Short-Term Load Forecasting (STLF) of multivariate time series. The proposed model consists of a hybrid Convolutional neural network-Bidirectional Long Short-Term Memory (CBiLSTM) based on the Evolution Strategy (ES) method and the Savitzky–Golay (SG) filter (SG-CBiLSTM). The adopted methodology incorporates the virtue of different prepossessing blocks to enhance the performance of the CBiLSTM model. In particular, a data-augmentation strategy is employed to synthetically improve the feature representation of the CBiLSTM model. The augmented data is forwarded to the Partial Least Square (PLS) method to select the most informative features above the predefined threshold. Next, the SG algorithm is computed for smoothing the load to enhance the learning capabilities of the underlying system. The structure of the SG-CBiLSTM for the ISO New England dataset is optimized using the ES technique. Finally, the CBiLSTM model generates output forecasts. The proposed approach demonstrates a remarkable improvement in the performance of the original CBiLSTM model. Furthermore, the experimental results strongly confirm the high effectiveness of the proposed SG-CBiLSTM model compared to the state-of-the-art techniques

Prévision et gestion de la production d'énergie photovoltaïque à base des données météorologiques

Energy management is an indispensable part of today’s electrical systems and Smart Grid (SG) paradigms, especially with the high penetration of Renewable Energy Sources (RES). Thus, significant attention is paid from academia and industry to foster the synergy between these two paradigms as a means to accelerate the transition to a more diverse generation portfolio that includes an unprecedented amount of RES such as Photovoltaic (often shortened as PV) energy. Due to the ever-growing electricity consumption, state-of-the-art Artificial Intelligence (AI)-based techniques play a central role in providing necessary system flexibility to deal with the bulk integration of the PV energy for power-and-energy-efficient computing. AI lies at the core of forecasting methods to enhance the power delivery service between the grid-connected PV stations and end-consumers. In other words, the futuristic power grid infrastructure should rely on accurate PV Power Forecasting (PVPF) methods as a cornerstone of achieving unit commitment and stable energy supply. Nevertheless, designing effective energy management systems is complex because it involves designing components and hardware-software interfaces across the computing stack. So ubiquitous and complex are energy management mechanisms requiring a high level of scalability and generalization potential to gratify the load needs and cope with the meteorological factors' stochastic nature in tandem with optimal power grid stability. In an effort to break this stalemate, this research aims to explore the potential AI techniques for energy management between the energy-mix on the supply side and the load side. Consequently, the present work proposes efficient techniques to tackle the instability and intermittency of PV power production and its significant impact on the load demand. First, a comprehensive overview of SG and energy management has been conducted. Next, various ML models-based PVPF have been introduced and applied to real-world scenarios to ensure an uninterrupted power supply. Afterward, innovative load forecasting methods have been proposed to cope with the volatile PV energy supply and accommodate the stochasticity of the customers' demand with efficient energy management strategies. Finally, SG stability methods have been proposed to predict the grid's state. This will aid in shaping the best strategies for preventive maintenance and risk hedging policies. This research thesis applies data science methods to the SG paradigm for effective dynamic control and management.La gestion de l'énergie est un élément indispensable des systèmes électriques d'aujourd'hui et des paradigmes des réseaux intelligents, en particulier avec la forte pénétration des sources d'énergies renouvelables. Ainsi, les universités et l'industrie accordent une attention particulière à la synergie entre ces deux paradigmes afin d'accélérer la transition vers un portefeuille de production plus diversifié comprenant une quantité importante d'énergies renouvelables telles que l'énergie Photovoltaïque (souvent abrégée en PV). En raison de l'augmentation de la consommation énergetique, les techniques de pointe basées sur l'intelligence artificielle jouent un rôle central en fournissant la flexibilité nécessaire au système pour faire face à l'intégration en masse de l'énergie photovoltaïque. L'intelligence artificielle est au cœur des méthodes de prévision pour améliorer le service de livraison d'électricité entre les stations photovoltaïques connectées au réseau et les consommateurs finaux. En outre, l'infrastructure futuriste du réseau électrique devrait s'appuyer sur des méthodes précises de prévision de la puissance photovoltaïque pour l'engagement des unités et de l'approvisionnement énergétique stable. Néanmoins, la conception de systèmes de gestion de l'énergie efficace est complexe, car elle implique la conception de composants et d'interfaces matériel-logiciel sur l'ensemble de la pile informatique. Les mécanismes de gestion de l'énergie sont si omniprésents et complexes, nécessitant un haut niveau d'évolutivité et un potentiel de généralisation pour satisfaire les besoins de charge et faire face à la nature stochastique des facteurs météorologiques en tandem avec une stabilité optimale du réseau électrique. Dans un effort pour sortir de cette impasse, cette recherche vise à explorer les techniques potentielles d'intelligence artificielle pour la gestion de l'énergie entre le mix énergétique entre l'offre énergétique et la demande énergétique. Par conséquent, le présent travail propose des techniques efficaces pour lutter contre l'instabilité et l'intermittence de la production d'énergie photovoltaïque et son impact significatif sur la demande de charge.Tout d'abord, un aperçu complet du réseau intelligent et de la gestion de l'énergie a été réalisé. Ensuite, diverses méthodes de prévision de la puissance photovoltaïque basées sur l’apprentissage automatique ont été introduites et appliquées à des scénarios du monde réel pour assurer une alimentation électrique ininterrompue. Par la suite, des méthodes innovantes de prévision de la charge ont été proposées pour faire face à l'approvisionnement en énergie photovoltaïque volatile et s'adapter à la stochasticité de la demande des clients avec des stratégies de gestion de l'énergie efficaces. Enfin, des méthodes de stabilité de réseau intelligent ont été proposées pour prédire l'état du réseau. Cela aidera à définir les meilleures stratégies pour les politiques de maintenance préventive et de couverture des risques. Cette thèse de recherche applique des méthodes de science des données au paradigme de réseau intelligents pour un contrôle et une gestion dynamiques efficaces

Intrusion Detection Method Based on SMOTE Transformation for Smart Grid Cybersecurity

© 2022 IEEE.Real-Time Intrusion Detection Systems (IDSs) have attracted greater attention for secured and resilient smart grid operations. IDSs are employed to identify unknown cyberattacks and malware from network traffics. In this paper, an efficient model-based machine learning is proposed to detect a variety of cyberattacks. The proposed method enhanced Extremely randomized Trees (ET) classifier based on Synthetic Minority Oversampling Technique (SMOTE) accurately classifies imbalanced IDSs data. The proposed ET-SMOTE uses a virtue of data processing blocks to enable multi-layer network cyber-security assessment in smart grids by acquiring the essential knowledge of attack dynamics. The proposed computing framework provides an accurate multiclass classification of five network traffic categories: denial of service attacks, probing attacks, root to local attacks, user to root attacks, and normal. The experimental results demonstrate the high accuracy of the proposed ET-SMOTE algorithm in detecting various types of cyber threats compared to benchmark models with an accuracy of 99.79% using the NSL-KDD networks data set

Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review

The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies