Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.Part of this work has been developed from results obtained during the H2020 “Optimised Energy Efficient Design Platform for Refurbishment at District Level” (OptEEmAL) project, Grant No. 680676

Normalization Influence on ANN-Based Models Performance: A New Proposal for Features’ Contribution Analysis

Artificial Neural Networks (ANNs) are weighted directed graphs of interconnected neurons widely employed to model complex problems. However, the selection of the optimal ANN architecture and its training parameters is not enough to obtain reliable models. The data preprocessing stage is fundamental to improve the model’s performance. Specifically, Feature Normalisation (FN) is commonly utilised to remove the features’ magnitude aiming at equalising the features’ contribution to the model training. Nevertheless, this work demonstrates that the FN method selection affects the model performance. Also, it is well-known that ANNs are commonly considered a “black box” due to their lack of interpretability. In this sense, several works aim to analyse the features’ contribution to the network for estimating the output. However, these methods, specifically those based on network’s weights, like Garson’s or Yoon’s methods, do not consider preprocessing factors, such as dispersion factors , previously employed to transform the input data. This work proposes a new features’ relevance analysis method that includes the dispersion factors into the weight matrix analysis methods to infer each feature’s actual contribution to the network output more precisely. Besides, in this work, the Proportional Dispersion Weights (PWD) are proposed as explanatory factors of similarity between models’ performance results. The conclusions from this work improve the understanding of the features’ contribution to the model that enhances the feature selection strategy, which is fundamental for reliably modelling a given problem.This work was supported in part by DATA Inc. Fellowship under Grant 48-AF-W1-2019-00002, in part by Tecnalia Research and Innovation Ph.D. Scholarship, in part by the Spanish Centro para el Desarrollo Tecnológico Industrial (CDTI, Ministry of Science and Innovation) through the ‘‘Red Cervera’’ Programme (AI4ES Project) under Grant CER-20191029, and in part by the 3KIA Project funded by the ELKARTEK Program of the SPRI-Basque Government under Grant KK-2020/00049

A Multi-objective Harmony Search Algorithm for Optimal Energy and Environmental Refurbishment at District Level Scale

Nowadays municipalities are facing an increasing commitment regarding the energy and environmental performance of cities and districts. The multiple factors that characterize a district scenario, such as: refurbishment strategies’ selection, combination of passive, active and control measures, the surface to be refurbished and the generation systems to be substituted will highly influence the final impacts of the refurbishment solution. In order to answer this increasing demand and consider all above-mentioned district factors, municipalities need optimisation methods supporting the decision making process at district level scale when defining cost-effective refurbishment scenarios. Furthermore, the optimisation process should enable the evaluation of feasible solutions at district scale taking into account that each district and building has specific boundaries and barriers. Considering these needs, this paper presents a multi-objective approach allowing a simultaneous environmental and economic assessment of refurbishment scenarios at district scale. With the aim at demonstrating the effectiveness of the proposed approach, a real scenario of Gros district in the city of Donostia-San Sebastian (North of Spain) is presented. After analysing the baseline scenario in terms of energy performance, environmental and economic impacts, the multi-objective Harmony Search algorithm has been employed to assess the goal of reducing the environmental impacts in terms of Global Warming Potential (GWP) and minimizing the investment cost obtaining the best ranking of economic and environmental refurbishment scenarios for the Gros district.OptEEmAL project, Grant Agreement Number 68067

Novel Light Coupling Systems Devised Using a Harmony Search Algorithm Approach

We report a critical assessment of the use of an Inverse Design (ID) approach steamed by an improved Harmony Search (IHS) algorithm for enhancing light coupling to densely integrated photonic integratic circuits (PICs) using novel grating structures. Grating couplers, performing as a very attractive vertical coupling scheme for standard silicon nano waveguides are nowadays a custom component in almost every PIC. Nevertheless, their efficiency can be highly enhanced by using our ID methodology that can deal simultaneously with many physical and geometrical parameters. Moreover, this method paves the way for designing more sophisticated non-uniform gratings, which not only match the coupling efficiency of conventional periodic corrugated waveguides, but also allow to devise more complex components such as wavelength or polarization splitters, just to cite some

Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-ofthe- art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.This research received no external funding

Underwater Robot Task Planning Using Multi-Objective Meta-Heuristics

Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by devising three heuristic solvers aimed at efficiently scheduling tasks in robotic swarms, which collaborate together to accomplish a mission, and by presenting experimental results obtained over realistic scenarios in the underwater environment. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks and the relative execution order of such tasks within the schedule of certain robots. The obtained results reveal interesting differences in terms of Pareto optimality and spread between the algorithms considered in the benchmark, which are insightful for the selection of a proper task scheduler in real underwater campaigns

Cooperation in clusters: A study case in the furniture industry in Colombia

Cooperation is increasingly been used in the industrial sector because of its benefits. This have motivated companies to establish alliances and agreements with others in order to reduce cost or access new markets, for example. In the literature, we could find many works aimed at cooperation in supply chains. A smaller amount was focused at cluster cooperation and few of them propose methodologies or models to facilitate the development and implementation of cooperation in industrial clusters. This paper provides a methodology for cooperation in clusters, which was applied to the furniture industry of Atlántico region in Colombia. Shapley value was used in order to evaluate the different coalitions and to split the benefits obtained with these coalitions. The methodology is useful for cluster members in order to encourage the formation of alliances within the cluster in order to overcome the prevailing mistrust, strengthening the cluster and gaining competitiveness

Propuesta estratégica de mejora en la implementación de los estándares mínimos del sistema de gestión de la seguridad y salud en el trabajo (SG-SST) en la Cooperativa de servidores públicos y jubilados de Colombia (COOPSERP) para el año 2020.

Se realiza una propuesta para la identificación del cumplimiento de los estándares mínimos del Sistema de Gestión de la Seguridad y Salud en el Trabajo acorde a la normatividad vigente,con el fin de plantear acciones de mejoras que garanticen la integridad y el bienestar de todos los trabajadores y con el objetivo de anticiparse, reconocer, evaluar y controlar los riesgos que afecten la seguridad y salud en el trabajo; planificando y programando diversas actividades, capacitaciones y/ o asesorías que permitan evaluar el pleno cumplimiento de las políticas del sistema, controlar los riesgos en forma práctica y efectiva para mejorar y velar por el bienestar y el clima laboral.A proposal is made for the identification of compliance with the minimum standards of the Occupational Safety and Health Management System in accordance with current regulations, in order to propose improvement actions that guarantee the integrity and well-being of all workers and with the aim of anticipating, recognizing, evaluating and controlling the risks that affect safety and health at work; planning and scheduling various activities, trainings and / or consultancies that allow evaluating full compliance with the system's policies, controlling risks in a practical and effective way to improve and ensure well-being and the working environment

Prevalence and Characteristics of Self-Reported Hypothyroidism and Its Association with Nonorgan-Specific Manifestations in US Sarcoidosis Patients: A Nationwide Registry Study

Little is known about the prevalence, clinical characteristics and impact of hypothyroidism in patients with sarcoidosis. We aimed to determine the prevalence and clinical features of hypothyroidism and its relation to organ involvement and other clinical manifestations in patients with sarcoidosis. We conducted a national registry-based study investigating 3835 respondents to the Sarcoidosis Advanced Registry for Cures Questionnaire between June 2014 and August 2019. This registry is based on a self-reported, web-based questionnaire that provides data related to demographics, diagnostics, sarcoidosis manifestations and treatment. We compared sarcoidosis patients with and without self-reported hypothyroidism. We used multivariable logistic regression and adjusted for potential confounders to determine the association of hypothyroidism with nonorgan-specific manifestations. 14% of the sarcoidosis patients self-reported hypothyroidism and were generally middle-aged white women. Hypothyroid patients had more comorbid conditions and were more likely to have multiorgan sarcoidosis involvement, especially with cutaneous, ocular, joints, liver and lacrimal gland involvement. Self-reported hypothyroidism was associated with depression (adjusted odds ratio (aOR) 1.3, 95% CI 1.01–1.6), antidepressant use (aOR 1.3, 95% CI 1.1–1.7), obesity (aOR 1.7, 95% CI 1.4–2.1), sleep apnoea (aOR 1.7, 95% CI 1.3–2.2), chronic fatigue syndrome (aOR 1.5, 95% CI 1.2–2) and was borderline associated with fibromyalgia (aOR 1.3, 95% CI 1–1.8). Physical impairment was more common in patients with hypothyroidism. Hypothyroidism is a frequent comorbidity in sarcoidosis patients that might be a potentially reversible contributor to fatigue, depression and physical impairment in this population. We recommend considering routine screening for hypothyroidism in sarcoidosis patients especially in those with multiorgan sarcoidosis, fatigue and depression

La imagen y la narrativa como herramientas para el abordaje psicosocial en escenarios de violencia. Bogotá, D.C., Zipaquirá, Facatativá, Cundinamarca

En el siguiente trabajo, se analiza mediante estudio de relatos en diferentes contextos de violencia, donde se realizan actividades a través de un enfoque narrativo, con el propósito de crear estrategias psicosociales para abordar los casos de violencia y sus consecuencias en las victimas dejadas por conflicto armado. Se presenta la historia de Ana Ligia Higinio López quien a su vez nos cuenta cómo este conflicto armado la marcó a ella, quien fue exiliada dos veces de su propia tierra junto a sus hijos, esta situación ha tenido un impacto emocional, psicológico y social al ser desplazada, y no poder continuar con sus proyectos, todas estas experiencias han causado una pérdida en el tejido social, pero a pesar de las adversidades que ha tenido que enfrentar ha salido adelante, a través de la resiliencia cada experiencia vivida le ha servido como inspiración para componer poemas, a partir de la experiencia de Ana ligia se plantearon 9 preguntas, tres de tipo circulares, estratégicas, y reflexivas. Se trabajó el caso de Peñas Coloradas se trata de una población que fue víctima del conflicto armado, y de qué manera sus tierras fueron destruidas, al punto de dejarlas en total abandono, además no cuentan con el apoyo del gobierno, lo que conlleva a proponer estrategias psicosociales para fortalecer el tejido social. Finalmente, se expone en el informe analítico y reflexivo que trato de 5 presentaciones de foto voz, donde se cuenta la experiencia en cada comunidad, según el escenario de violencia en que han estado inmersos.In the following work, it is analyzed through the study of stories in different contexts of violence, where activities are carried out through a narrative approach, with the purpose of creating psychosocial strategies to address cases of violence and its consequences on victims left behind by conflict. armed. The story of Ana Ligia Higinio López is presented, who in turn tells us how this armed conflict marked her, who was exiled twice from her own land along with her children, this situation has had an emotional, psychological and social impact on being displaced, and not being able to continue with her projects, all these experiences have caused a loss in the social fabric, but despite the adversities she has had to face, she has come out ahead, through resilience each lived experience has served as a inspiration to compose poems, from the experience of Ana ligia, 9 questions were raised, three of a circular, strategic, and reflective type. The case of Peñas Coloradas was worked on, it is about a population that was a victim of the armed conflict, and how their lands were destroyed, to the point of leaving them in total abandonment, in addition they do not have the support of the government, which leads to proposing psychosocial strategies to strengthen the social fabric. Finally, it is exposed in the analytical and reflective report that I deal with 5 photo voice presentations, where the experience in each community is told, according to the scenario of violence in which they have been immersed