Recycling flows in emergy evaluation: A mathematical paradox?

cited By (since 1996)4International audienceThis paper is a contribution to the emergy evaluation of systems involving recycling or reuse of waste. If waste exergy (its residual usefulness) is not negligible, wastes could serve as input to another process or be recycled. In cases of continuous waste recycle or reuse, what then is the role of emergy? Emergy is carried by matter and its value is shown to be the product of specific energy with mass flow rate and its transformity. This transformity (τ) given as the ratio of the total emergy input and the useful available energy in the product (exergy) is commonly calculated over a specific period of time (usually yearly) which makes transformity a time dependent factor. Assuming a process in which a part of the non-renewable input is an output (waste) from a previous system, for the waste to be reused, an emergy investment is needed. The transformity of the reused or recycled material should be calculated based on the pathway of the reused material at a certain time (T) which results in a specific transformity value (τ). In case of a second recycle of the same material that had undergone the previous recycle, the material pathway has a new time (T+T 1) which results in a transformity value (τ 1). Recycling flows as in the case of feedback is a dynamic process and as such the process introduces its own time period depending on its pathway which has to be considered in emergy evaluations. Through the inspiration of previous emergy studies, authors have tried to develop formulae which could be used in such cases of continuous recycling of material in this paper. The developed approach is then applied to a case study to give the reader a better understanding of the concept. As a result, a 'factor' is introduced which could be included on emergy evaluation tables to account for subsequent transformity changes in multiple recycling. This factor can be used to solve the difficulties in evaluating aggregated systems, serve as a correction factor to up-level such models keeping the correct evaluation and also solve problems of memory loss in emergy evaluation. The discussion deals with the questions; is it a pure mathematical paradox in the rules of emergy? Is it consistent with previous work? What were the previous solutions to avoid the cumulative problem in a reuse? What are the consequences?. © 2011 Elsevier B.V

Mineral resource assessment: Compliance between Emergy and Exergy respecting Odum's hierarchy concept

International audienceIn this paper, authors suggest to combine the exergoecology and the emergy concept in order to evaluate mineral resources, taking into account their abundance, their chemical and physical properties and the impact of their extraction. The first proposition of this work is to consider that every group of mineral, dispersed in the Earth's crust, is a co-product of the latter. The specific emergies of dispersed minerals are, then, inversely proportional to their abundance. The results comply with the material hierarchy as the specific emergy of a dispersed mineral rise with its scarcity. The second is an emergy evaluation model based on the chemical and concentration exergy of the mineral, its condition in the mine and its abundance. This model permits to assess the decline of mineral reserves and its impact on the ecosystem. The dispersed specific emergy of 42 main commercially used minerals has been calculated. Furthermore, the emergy decrease of some Australian mineral reserves has been studied, as well as the land degradation of US copper mines

Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach

Building's energy consumption prediction is a major concern in the recent years and many efforts have been achieved in order to improve the energy management of buildings. In particular, the prediction of energy consumption in building is essential for the energy operator to build an optimal operating strategy, which could be integrated to building's energy management system (BEMS). This paper proposes a prediction model for building energy consumption using support vector machine (SVM). Data-driven model, for instance, SVM is very sensitive to the selection of training data. Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model. In addition, to encompass thermal inertia of building, pseudo dynamic model is applied since it takes into account information of transition of energy consumption effects and occupancy profile. Relevant days data selection and whole training data model is applied to the case studies of Ecole des Mines de Nantes, France Office building. The results showed that support vector machine based on relevant data selection method is able to predict the energy consumption of building with a high accuracy in compare to whole data training. In addition, relevant data selection method is computationally cheaper (around 8 minute training time) in contrast to whole data training (around 31 hour for weekend and 116 hour for working days) and reveals realistic control implementation for online system as well.Comment: Proceedings of ECOS 2015-The 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems , Jun 2015, Pau, Franc

Analyse du recyclage par la méthode émergétique

International audienceLe recyclage matière est a priori considéré comme éco-favorable par rapport à l'exploitation de minerais provenant de mines, par nature en quantité finie et donc s'épuisant. Néanmoins, une vision holistique du recyclage requiert l'analyse de l'ensemble des ressources mobilisées pour effectuer cette opération. De plus, en suivant le déplacement/cheminement de la matière dans le temps, il devient alors nécessaire de connaître l'ensemble de l'historique de la matière considérée. Les auteurs établissent une équation théorique décrivant l'évolution à temps discret de l'émergie d'un produit : le pas d'échantillonnage est le temps du cycle. L'équation peut se réduire à une suite géométrique de raison, la fraction à recycler, sous certaines hypothèses. L'aluminium est ensuite proposé comme exemple d'application

Impact of building material recycle or reuse on selected emergy ratios

cited By (since 1996)1International audienceWhile the emergy evaluation method has been used successfully in recycling processes, this area of application still requires further development. One of such is developing emergy ratios or indices that reflect changes depending on the number of times a material is recycled. Some of these materials may either have been recycled or reused continuously as inputs to a building, for example, and thus could have various impacts on the emergy evaluation of the building. The paper focuses on reuse building materials in the context of environmental protection and sustainable development. It presents the results of an emergy evaluation of a low-energy building (LEB) in which a percentage of input materials are from recycled sources. The corresponding impacts on the emergy yield ratio (EYR B) and the environmental loading ratio (ELR B) are studied. The EYR which is the total emergy used up per unit of emergy invested, is a measure of how much an investment enables a process to exploit local resources in order to further contribute to the economy. The ELR however, is the total nonrenewable and imported emergy used up per unit of local renewable resource and indicates the stress a process exhibits on the environment. The evaluation provides values for the selected ratios based on different recycle times. Results show that values of the emergy indices vary, even more, when greater amounts of material is recycled with higher amount of additional emergy required for recycling. This provides relevant information prioritizing the selection of materials for recycling or reuse in a building, and the optimum number of reuse or recycle times of a specific material. © 2012 Elsevier B.V. All rights reserved

Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network

International audienceThis paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system

Carbon footprint and emergy combination for eco-environmental assessment of cleaner heat production

cited By (since 1996)0; Article in PressInternational audienceThe aim of this paper is to study via environmental indicators to which extent, replacing fossil fuel with biomass for heating is an environmentally friendly solution. The environmental impact of using biomass depends mostly on the transportation process. Authors define the notion of maximum supply distance, beyond which biomass transportation becomes too environmentally intensive compared to a fossil fuel fired heating system. In this work a carbon footprint analysis and an emergy evaluation, has been chosen to study the substitution of wood for natural gas. The comparative study seeks to examine, via the two approaches, two heating systems: one is fired with wood, transported by trucks and the other one is fired with natural gas transported by pipelines. The results are expressed in terms of maximum supply distance of wood. In the emergy evaluation it represents the maximum supply distance permitting wood to be more emergy saving than natural gas. In the carbon footprint analysis, it represents the maximum supply distance permitting wood to be a carbon saving alternative to natural gas. Furthermore, the unification of carbon footprint and emergy evaluation permits to define, for both approaches, the minimum theoretical wood burner first law efficiency that allows, CO 2 or emergy to be saved, when there is no wood transport. In order to identify the impacts of the main parameters of the study a sensitivity analysis has been carried out. The case study investigated in this paper shows that there is a large gap between the results. The maximum supply distances calculated via carbon footprint and emergy evaluation are about 5000 km and 1000 km, respectively, anthe minimum theoretical wood burner efficiencies are about 5% and 54%, respectively. © 2012 Elsevier Ltd. All rights reserved

Exergy analysis and thermo-economic optimization of a district heating network with solar- photovoltaic and heat pumps

International audienceElectrification of district heating networks, especially using heat pumps, is widely recommended in literature. Installing heat pumps affects both electricity and heating networks. Due to lack of suitable modelling tools, size optimization of heat pumps in the heating network with the full consideration of the electric distribution network is not well addressed in literature. This paper presents an optimization of a district heating network consisting of solar photovoltaic and heat pumps with the consideration of the detail parameters of heating and electric distribution networks. An extended energy hub approach is used to model the energy system. Exergy and energy analyses are applied to identify and isolate lossy branches in a meshed heating network. Both methods resulted into the same reduced topology. Particle swarm optimization is then applied on the reduced topology in order to find out the most economical temperature profiles and size of distributed heat pumps. The thermo-economic results are found to be highly influenced by the heat demand distribution, the power loss in both electric and heat distribution network, the cost of generation, the temperature limits and the coupling effect of the heat pumps