Contribution of studies of sub-seismic fracture populations to paleo-hydrological reconstructions (Bighorn Basin, USA)

This work reports on the reconstruction of the paleo-hydrological history of the Bighorn Basin (Wyoming, USA) and illustrates the advantages and drawbacks of using sub-seismic diffuse fracture populations (i.e., micrometric to metric joints and veins forming heterogeneous networks), rather than fault zones, to characterize paleo-fluid systems at both fold and basin scales. Because sub-seismic fractures reliably record the successive steps of deformation of folded rocks, the analysis of the geochemical signatures of fluids that precipitated in these fractures reveals the paleo-fluid history not only during, but also before and after, folding. The present study also points out the need for considering pre-existing fluid systems and basin-scale fluid migrations to reliably constrain the evolution of fluid systems in individual folds

Factorial design analysis of phosphate removal from model solution by iron-loaded pomegranate peel

This study investigated the removal of phosphate (PO4 3- ) from disodium hydrogen phosphate (Na2HPO4) solution using Iron-loaded pomegranate peel (IL-PP) as a bio-adsorbent. The full factorial design using Minitab19 software was applied to analyze the effects of influencing parameters and their interactions and derive the equation that adequately describe PO4 3- removal by IL-PP. Effective PO4 3- removal up to 90% was achieved within 60 min, at pH 9 and 25 °C temperature using a 150 mg dose of IL-PP

L’intelligence artificielle au service de l’optimisation de l’énergie électrique dans un réseau intelligent

RÉSUMÉ : Afin de maintenir l’équilibre entre l’offre et la demande d’électricité, l’utilisation d’une planification opérationnelle fiable est un aspect important de la gestion du système électrique. Pour de nombreux services, les consommateurs doivent payer plus cher l’électricité pendant les périodes de pointes. Par conséquent, si les consommateurs possèdent une connaissance à l’avance des informations sur les pointes de la demande électrique, ces coûts supplémentaires peuvent être évités car ils pourront mieux gérer leur utilisation de l’énergie électrique. Pour cela, des prévisions précises de la demande d’électricité et donc des informations crédibles sur les pointes de puissance vont assurer un approvisionnement en électricité fiable, car d’un point de vue du fournisseur elles donnent le temps d’anticiper son offre, et d’un point de vue du consommateur elles peuvent être d’une grande utilité pour réduire le coût de l’électricité. Maintenant, lorsqu’il s’agit de prédire la consommation de puissance future d’un groupe de consommateurs avec des profils hétérogènes comme c’est le cas dans les bâtiments résidentiels, commerciaux ou institutionnels, il est nécessaire de savoir que les consommateurs peuvent avoir selon plusieurs critères leur propre comportement, une autre difficulté s’ajoute donc au problème. En effet, il devient primordial de tenir en compte l’aspect comportemental de chacun et donc de comprendre comment les consommateurs agissent à chaque moment.Sans bien connaître cet aspect, le modèle de prédiction ne sera pas robuste aux changements brusques dans les habitudes quotidiennes de chacun. Il est également crucial de pouvoir porter de l’assistance chaque consommateur dans la gestion de sa consommation d’énergie, tout en essayant de maximiser son confort car cela peut permettre un gaspillage minimal d’énergie avec des changements de comportement mineurs de la part des consommateurs puisque la flexibilité permise par la demande d’électricité combinée aux incitatifs de la part du système électrique peuvent créer davantage de possibilités pour la gestion de la demande. Globalement, appliquer ce principe sur une plus grande échelle contribuera pleinement à la réduction des émissions de gaz à effet de serre. Ce mémoire est donc construit à travers deux grands objectifs : le premier étant de créer des modèles précis de prédiction de la charge électrique, le second concerne la gestion de la demande électrique et la maximisation du confort du consommateur, ce dernier sera traduit par le contrôle de la température de chaque pièce à une consigne du consommateur. Le premier objectif s’oriente autour d’une étude comparative et approfondie de trois principaux algorithmes d’apprentissage profond largement utilisés pour la prédiction de séries chronologiques à court terme. Cette étude fera rentrer en jeu un ensemble de données se composant de l’historique de puissance consommée sur cinq bâtiments hétérogènes d’un campus universitaire à Montréal. L’historique de puissance est récolté à l’aide de compteurs intelligents chaque 15 minutes pendant les années 2015 et 2016. D’autres données externes liées à la météo et le calendrier seront ajoutées par la suite afin d’extraire les effets saisonniers et prendre en compte le comportement des consommateurs. Le but de cette étude sera de trouver le meilleur algorithme et la meilleure stratégie afin de construire un modèle capable de prédire avec une grande précision la consommation de puissance future à court terme au niveau du campus et sur plusieurs horizons. Aussi, ce modèle nous permettra d’avoir des informations fiables concernant les pointes d’électricité. La deuxième partie de cette recherche, faite en collaboration avec un étudiant doctorant, vise à fournir une étude approfondie sur la gestion de l’énergie et du confort en utilisant l’apprentissage par renforcement, qui est un processus essai-erreur où un agent autonome apprend à remplir une tâche en interagissant avec son environnement et en prenant ses propres décisions de façon à optimiser une récompense au cours du temps. Par conséquent, l’agent devra trouver la bonne politique qui lui permettra d’obtenir une récompense maximale sur toute la période de contrôle. Des agents capables de bien agir sur des charges contrôlées par thermostat ont été construits en utilisant des données synthétiques, puis testés sur diverses combinaisons de pièces, chaque pièce contenant une charge contrôlée par thermostat. Par la suite cette étude a pu prouver que donner les résultats de prédiction de la charge électrique comme information à l’agent était très efficace car comparée au schéma où ces résultats ne sont pas fournis, elle a permis de réduire jusqu’à 73 % la consommation d’énergie pendant les périodes de pic. Enfin, cette étude a été également l’objet d’une évolution vers la mise en œuvre d’un agent généralisé fiable, appelé GA2TC, capable de bien gérer l’énergie électrique et de maximiser le confort dans des bâtiments, quelle que soit leur architecture ou leur dimension. Cet agent a été développé dans le but d’éviter de construire une solution spécialement pour chaque bâtiment et, à la place, d’utiliser un seul agent adaptable à tous types de pièces et donc de bâtiments. Ce travail permettra une fluidité et une avancée rapide dans le domaine électrique ainsi que dans des domaines similaires, où la difficulté réside principalement dans la modélisation de solutions spécifiques à certains types de problèmes. En conclusion, ce mémoire contribue à la découverte de nouvelles approches basées sur de l’apprentissage machine ainsi qu’à la mise en œuvre de leurs applications dans le réseau intélligent (Smart Grid). Il fournit également une avancée et une nouvelle approche dans la manière de gérer les systèmes électriques dans les bâtiments, en prenant en compte dans certains cadres les prévisions de consommations futures. Toutefois, des limites techniques liées au manque de ressources de calcul pour perfectionner nos modèles de prévision et de gestion,et à la modélisation simplifiée du monde réel quant aux environnements des bâtiments, offrent de nouveaux défis à relever et des perspectives d’études élargies.----------ABSTRACT : In order to maintain the balance between electricity demand and supply, the use of reliable operational planning is an important aspect of power system management. For many services, consumers must pay more for electricity during periods of high demand like during cold weather, big events or football matches. Therefore, if consumers have prior knowledge of electricity peak information, these additional costs can be avoided as they will help them better manage their use of electrical energy. For this, accurate forecasts of energy demand and thus reliable information on the expected peak of power will ensure a reliable supply of electricity, because from a supplier’s point of view, it gives time to anticipate its supply, and from a consumer’s point of view, it can be of great use in reducing the cost of electricity. Now, when it comes to predicting the future power consumption of a group of consumers with heterogeneous profiles, as it is the case in residential, commercial or institutional buildings, it is necessary to know that consumers have their own behaviour according to several criteria, so another difficulty is added to the problem. Indeed, it becomes essential to take into account the behavioural aspect of each person, and therefore to understand how consumers act at each moment. Without a good knowledge of this aspect, the prediction model will not be robust enough to abrupt changes in the daily habits of each individual. It is also crucial to be able to assist each consumer in managing their energy consumption, while trying to maximize their comfort, as this can allow minimal energy waste with minor changes in consumer behaviour since the flexibility allowed by the electricity demand combined with incentives from the power system can create more opportunities for energy saving. On a broader level, applying this principle on a larger scale will make a significant contribution to reducing global greenhouse gas emissions. This paper is therefore built around two main objectives: the first is to create accurate models for predicting the electrical demand, and the second is to minimize and manage the consumed electrical power and maximize consumer comfort, which will be translated into the control of the temperature set point each room at a consumer instruction. The first objective is based on a comparative and in-depth study of three main deep learning algorithms widely used for short-term time series prediction. This study will bring into a real data set consisting of the power consumption history of five heterogeneous buildings on a university campus in Montreal. The power history is collected using smart meters every 15 minutes during the years 2015 and 2016. Other external data related to weather and calendar will be added later on to extract seasonal effects and take into account consumer behaviour. The goal of this study will be to find the best algorithm and strategy to build a model capable of predicting with high accuracy the future power consumption at the campus level, and thus to have reliable information about electricity peaks. The second part of this research, done in collaboration with a PhD student, aims to provide an in-depth study of energy and comfort management using reinforcement learning, which is a trial-and-error process where an autonomous agent learns to perform a task by interacting with its environment and making its own decisions in order to optimize a reward over time. Therefore, the agent will need to find the right policy to reach maximum reward over the entire control period. Agents capable of acting well on thermostatically controllable loads have been constructed using synthetic data and then tested on various combinations of rooms, each room containing a thermostatically controllable load. Subsequently this study proved that giving the prediction results of the electrical load as information to the agent was very effective as it allowed us to reduce energy consumption by up to 73% compared with having no prediction information. Finally, this study was also the subject of an evolution towards the implementation of a reliable generalized agent, called GA2TC, capable of managing electrical energy and maximizing comfort in buildings, regardless of their architecture or size. This agent has been developed with the aim of avoiding constructing a solution specifically for each building, and instead using a single agent adaptable to all types of rooms and therefore buildings. This work will allow fluidity and rapid progress in the electrical field as well as in similar fields, where the difficulty lies mainly in modeling specific solutions to specific types of problems. In conclusion, this research contributes to the discovery of new approaches based on machine learning as well as to the implementation of their applications in the smart grid field. It also provides an advanced and innovative approach in the way of managing electrical systems in buildings, taking into account in some frameworks the forecasts of future consumption. However, technical limitations related to the lack of computational resources to refine our prediction and control models, and simplified real-world modeling of building environments, offer new challenges and expanded study opportunities

Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data

We analyse multimodal time-series data corresponding to weight, sleep and steps measurements. We focus on predicting whether a user will successfully achieve his/her weight objective. For this, we design several deep long short-term memory (LSTM) architectures, including a novel cross-modal LSTM (X-LSTM), and demonstrate their superiority over baseline approaches. The X-LSTM improves parameter efficiency by processing each modality separately and allowing for information flow between them by way of recurrent cross-connections. We present a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters. Finally, we visualise the model's predictions, revealing implications about latent variables in this task.Comment: To appear in NIPS ML4H 2017 and NIPS TSW 201

Phytotoxicity evaluation of nutrient-fortified pomegranate peel powders prepared from food waste and their feasibility as biofertilizers

Pomegranate peel powder (PPP) is increasingly used as a bioadsorbent to decontaminate wastewaters due to its adsorptive characteristics. The application of nutrient-fortified bioadsorbents as alternatives to chemical fertilizers can provide an innovative and eco-friendly approach for sustainable waste management. Nevertheless, there is extremely limited information regarding their effects on the growth of agricultural crops. We investigated the effects of raw and nutrient-fortified PPPs on oilseed rape ( Brassica napus ). Our results showed that the concentration-dependent in vitro phytotoxicity of high PPP doses (germination indices were 109.6%, 63.9%, and 8.9% at the applied concentrations of 0.05%, 0.5%, and 5%) was diminished by the application of nutrient-fortified PPPs (germination indices were 66.0–83.4% even at the highest doses). In pot experiments, most PPP treatments (especially Raw-PPP and the mixture of N- and P-fortified PPPs) promoted the development of aboveground plant parts. Reorganization of the pattern of protein tyrosine nitration in the root tissues indicated that the plants were acclimated to the presence of PPPs, and thus, PPP treatment induced no or low-level stress. Our findings confirmed that several doses of PPP supplementation were beneficial for the model crop plant when applied in soil. We anticipate that our study will be a foundation for future investigations involving more plant species and soil types, which can contribute to the introduction of nutrient-fortified PPPs as sustainable biofertilizers

Stress and strain patterns, kinematics and deformation mechanisms in a basement-cored anticline: Sheep Mountain Anticline, Wyoming

International audienceIn order to characterize and compare the stress-strain record prior to, during, and just after folding at the macroscopic and the microscopic scales and to provide insights into stress levels sustained by folded rocks, we investigate the relationship between the stress-strain distribution in folded strata derived from fractures, striated microfaults, and calcite twins and the development of the Laramide, basement-cored Sheep Mountain Anticline, Wyoming. Tectonic data were mainly collected in Lower Carboniferous to Permian carbonates and sandstones. In both rock matrix and veins, calcite twins recorded three different tectonic stages: the first stage is a pre-Laramide (Sevier) layer-parallel shortening (LPS) parallel to fold axis, the second one is a Laramide LPS perpendicular to the fold axis, and the third stage corresponds to Laramide late fold tightening with compression also perpendicular to the fold axis. Stress and strain orientations and regimes at the microscale agree with the polyphase stress evolution revealed by populations of fractures and striated microfaults, testifying for the homogeneity of stress record at different scales through time. Calcite twin analysis additionally reveals significant variations of differential stress magnitudes between fold limbs. Our results especially point to an increase of differential stress magnitudes related to Laramide LPS from the backlimb to the forelimb of the fold possibly in relation with motion of an underlying basement thrust fault that likely induced stress concentrations at its upper tip. This result is confirmed by a simple numerical model. Beyond regional implications, this study highlights the potential of calcite twin analyses to yield a representative quantitative picture of stress and strain patterns related to folding

Paleostress magnitudes in folded sedimentary rocks

International audienceUsing Sheep Mountain Anticline (Wyoming, USA) as a case study, we propose a new approach to quantify effective paleo-principal stress magnitudes in the uppermost crust. The proposed mechanical scenario relies on a well-documented kinematic and chronological sequence of development of faults, fractures and microstructures in the folded strata. Paleostress orientations and regimes as well as differential stress magnitudes based on calcite twinning paleopiezometry are combined with rock mechanics data in a Mohr construction to derive principal stress magnitudes related to the successive steps of layer-parallel shortening and to late stage fold tightening. Such quantification also provides original insights into the evolution of the fluid (over)pressure and amount of syn-folding erosion

Continental break-up history of a deep magma-poor margin based on seismic reflection data (northeastern Gulf of Aden margin, offshore Oman)

International audienceRifting between Arabia and Somalia started around 35 Ma followed by spreading at 17.6 Ma in the eastern part of the Gulf of Aden. The first-order segment between Alula-Fartak and Socotra-Hadbeen fracture zones is divided into three second-order segments with different structure and morphology. Seismic reflection data were collected during the Encens Cruise in 2006 on the northeastern margin. In this study, we present the results of Pre-Stack Depth Migration of the multichannel seismic data from the western segment, which allows us to propose a tectono-stratigraphic model of the evolution of this segment of the margin from rifting to the present day. The chronological interpretation of the sedimentary sequences is mapped out within relation to the onshore observations and existing dating. After a major development of syn-rift grabens and horsts, the deformation localized where the crust is the thinnest. This deformation occurred in the distal margin graben (DIM) at the northern boundary of the ocean-continent transition (OCT) represented by the OCT ridge. At the onset of the OCT formation differential uplift induced a submarine landslide on top of the deepest tilted block and the crustal deformation was restricted to the southern part of the DIM graben, where the continental break-up finally occurred. Initial seafloor spreading was followed by post-rift magmatic events (flows, sills and volcano-sedimentary wedge), whose timing is constrained by the analysis of the sedimentary cover of the OCT ridge, correlated with onshore stratigraphy. The OCT ridge may represent exhumed serpentinized mantle intruded by post-rift magmatic material, which modified the OCT after its emplacement