Ainhoa Alonso-Vicario

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay.

The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

Regression Based Emission Models for Vehicle Contribution to Climate Change

The reduction of carbon emissions within the transportation sector is one of the most important steps against the threat of global warming. Unless strict emissions-reduction and fuel economy policies are in place, the resulting pollution is expected to increase dramatically along with the amount of vehicles on the roads. An accurate quantification of the emissions produced by each type of vehicle is essential in order to evaluate the social and environmental impacts derived. The literature shows a wide range of pollutant emission models, whether empirical, database centric or regression based. In this paper, we propose and analyze 3 regression based models built on data from pollutant emission databases and knowledge models. The first model is based on an exponential regression that improves the results given in the state of the art. In contrast, the other two models are based on different Artificial Intelligence techniques, namely Artificial Neural Networks and Support Vector Regression, which further improve the results.

Bridging the Gap between Real and Simulated Environments: A Hybrid Agent-Based Smart Home Simulator Architecture for Complex Systems

Deployment, maintenance of Smart Homes, Smart Grids in real environments is an expensive, lengthy process. In this paradigm, simulations play an important role by providing means of emulating the behaviour of the aforementioned systems. However, these simulations may suffer from lack of accuracy due to the inability to properly reproduce the operation of complex technologies such as solar panels, Heating, Ventilating, Air Conditioning systems (HVAC), sewerage networks or water provisioning. Within this context, this paper presents a Smart-Home Simulation architecture that is able to carry out more representational simulations by merging agent-based simulation of human behaviour with real-world modelling capabilities such as those provided by the Simulink software. Based on the simulation of human behaviour, water, electricity consumption profiles are generated, sent to Simulink models using the TCP/IP communication protocol. The obtained results show that a synchronized connection of both platforms in feasible, enabling a more accurate representation of the systems involved.

On-demand energy monitoring and response architecture in a ubiquitous world

Energy demand is increasing globally, and in consequence greenhouse-gas emissions from this sector are on the rise as well. This trend is set to continue, driven primarily by the economic growth and the rising population. Solutions in this area go hand in hand with the worldwide deployment of policies that look forward a better management and usage of energy in both domestic and industrial scopes. In this line, load balancing through demand-response strategies comes out as one of the most effective and immediate actions aimed at achieving efficiency in the use of energy resources. We present GeoWorldSim, an agent-based simulation platform that integrates the development of a human activity model as well as the communication middleware known as FI-WARE in order to test the best communication architectures available for the implementation of demand-response strategies.

MASSHA: An agent-based approach for human activity simulation in intelligent environments

Abstract Human activity recognition has the potential to become a real enabler for ambient assisted living technologies. Research on this area demands the execution of complex experiments involving humans interacting with intelligent environments in order to generate meaningful datasets, both for development and validation. Running such experiments is generally expensive and troublesome, slowing down the research process. This paper presents an agent-based simulator for emulating human activities within intelligent environments: MASSHA. Specifically, MASSHA models the behaviour of the occupants of a sensorised environment from a single-user and multiple-user point of view. The accuracy of MASSHA is tested through a sound validation methodology, providing examples of application with three real human activity datasets and comparing these to the activity datasets produced by the simulator. Results show that MASSHA can reproduce behaviour patterns that are similar to those registered in the real datasets, achieving an overall accuracy of 93.52% and 88.10% in frequency and 98.27% and 99.09% in duration for the single-user scenario datasets; and a 99.3% and 88.25% in terms of frequency and duration for the multiple-user scenario.

Artificial neural network modelling of the bioethanol-to-olefins process on a HZSM-5 catalyst treated with alkali

Abstract In this work the kinetic modelling of the transformation of bioethanol-to-olefins (BTO) process over a HZSM-5 catalyst treated with alkali using artificial neural networks (ANN) is presented. The main goal has been to obtain a BTO process neuronal model with the desired accuracy that allows the simplification and reduction of the computational cost with respect to a mechanistic knowledge model. To check the goodness of ANN base model structures, during the study a comparison with other alternative modelling techniques such as support vector machines was performed. Following a parameters optimization procedure and testing different training methods, the optimal ANN structure results to be a feed-forward 3–5–1 network with the Bayesian regularization training method. Using a set of experimental data obtained in a laboratory scale fixed bed reactor, we have obtained a similar fit to the knowledge model but with the advantage of being up to 43 times faster. These results are important for moving forward real time automatic control strategies in the biorefinery context.

GTPlat: Geosimulation for Assessing the Application of Incentives to Transport Planning

Motor vehicle abuse entails emitting large amounts of greenhouse gases to the atmosphere. In order to reduce climate change and life expectancy loss, authorities want to launch a set of sustainable travel policies which should be evaluated before their deployment. Although multi-agent systems for traffic analyses are very popular, they mainly focus on faithfully reproducing vehicle displacement and interaction between vehicles. It is therefore necessary to go one step further and integrate the transport choice factors that take place before starting everyday journeys. We present the baseline Geosimulation that integrates all the steps of citizens home-to-work commutes for assessing the impact green travelling policies would have.

Agent Based Simulations for the Estimation of Sustainability Indicators

Abstract We present a methodology to improve the estimation of several Sustainability Indicators based on the measurement of walking distance to infrastructures combining Agent Based Simulation with Volunteer Geographic Information. Joining these two forces we construct a more realistic and accurate distribution of the infrastructures based on knowledge created by citizens and their perceptions instead of official data sources. A Situated Multi-Agent System is in charge of simulating not only the functional disparity and sociodemographic characteristics of the population but also the geographic reality in a dynamic way. Namely, the system will analyze different geographic barriers for each collective bringing new possibilities to improve the assessment of the needs of the population for a more sustainable development of the city. In this article we will describe the methodology to carry on several sustainability indicator measurements and present the results of the proposed methodology applied to several municipalities.

Evolutionary Dynamic Optimization of Control Trajectories for the Catalytic Transformation of the Bioethanol-To-Olefins Process using Neural Networks

This paper presents a study on dynamic optimization of the catalytic transformation of Bioethanol-To-Olefins process. The main objective is to maximize the total production of Olefins by calculating simultaneously the optimal control trajectories for the main operating variables of the process. Using Neural Networks trained with two different types of Evolutionary Algorithms, the optimal trajectories have been automatically achieved, defining both an adequate shape and their corresponding parameters. The results suggest that, comparing with constant setpoints, the maximum production is increased up to 37.31% when using Neural Networks. The optimization procedure has become totally automatic and therefore very useful for real implementation.

A Comparative Study For Energetic ValorisationOf Partially Digested Sewage Sludge

The common final disposition of the sewage sludge obtained after their anaerobic digestion, whose heating value is about a quarter of the coke or peat, is often used in agricultural soils or as substitute of fossil fuels in clinker kilns. In this paper, a comparative study between incineration and gasification with CO2 as oxidant agent has been carried out. After the physical and chemical characterization of the sludge, the combustion and gasification properties have been studied by means of thermogravimetric analysis. According to the combustion profile, while the ignition temperature (200oC) and peak temperature (288oC) are of the order of any kind of plant biomass, the maximum combustion velocity is much lower, around 0.25 mg/min. Moreover, although gasification requires higher temperatures (around 790oC) for the complete transformation of the organic matter, the resulting gas, with a heating value much higher than the one contained in the dried sludge, may be used in the wastewater treatment plant, reducing both the gas natural requirements and the final sludge volume by 37%, and consequently the costs associated to the transport. The final inorganic matter may also be incorporated directly in construction materials, closing the whole loop.