A near zero consumption building as an urban acupuncture for a vertical slum. A case study in the city of Malaga, Spain

Vertical slum is defined as a particularly vulnerable height building, with serious problems of functionality, safety and habitability. Venezuela’s Tower of David is a famous example. Vertical slums are associated with an important level of physical degradation, coupled with a precarious socioeconomic situation of its occupants. Their inability to create a community for proper and mandatory maintenance increases their physical deterioration. The abandonment of the original owners is replaced by a system of occupation and illegal activities. In many cases, with an interest in maintaining the building in a state of precariousness, which annuls any attempt to rehabilitate it Facing this situation, the intervention is proposed through an urban acupuncture project, understood as a project of expropriation and physical rehabilitation of the building, associated to a project of social rehabilitation in a disadvantaged environment. It is about creating a hybrid building associated with four objectives 1- Create a hybrid building with a mixed offer of social and housing services: sheltered housing for seniors, residence and accommodation for young entrepreneurs. The idea of a social condenser is related to studies of the hybrid building such as the Downtown Athletic Club in New York, or the Rokade Tower and Maartenshof residence (Groningen, The Netherlands). 2- Incorporate the sustainability parameters directed to a building almost zero. 3- Incorporate a model of provision of housing services, managed by the municipality, but with the possibility of incorporating NGOs 4- Design a social rehabilitation project that facilitates the creation of a web of social-based companies or cooperatives that fosters entrepreneurship, and that can actively participate in the rehabilitation and maintenance of the neighborhood itself. This paper applies these principles to a building in Malaga as a case study and 10 strategies are developed and analysed in regards to its physical, social and sustainable transformation.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

The potential of additive manufacturing in the smart factory industrial 4.0: A review

Additive manufacturing (AM) or three-dimensional (3D) printing has introduced a novel production method in design, manufacturing, and distribution to end-users. This technology has provided great freedom in design for creating complex components, highly customizable products, and efficient waste minimization. The last industrial revolution, namely industry 4.0, employs the integration of smart manufacturing systems and developed information technologies. Accordingly, AM plays a principal role in industry 4.0 thanks to numerous benefits, such as time and material saving, rapid prototyping, high efficiency, and decentralized production methods. This review paper is to organize a comprehensive study on AM technology and present the latest achievements and industrial applications. Besides that, this paper investigates the sustainability dimensions of the AM process and the added values in economic, social, and environment sections. Finally, the paper concludes by pointing out the future trend of AM in technology, applications, and materials aspects that have the potential to come up with new ideas for the future of AM explorations

Improving building energy efficiency through novel hybrid models and control approaches including a data center case study

The building sector consumes the most energy and emits the greatest quantity of greenhouse gases of any sector. Energy savings in this sector can make a major contribution to tackling the threat of climate change. Research has produced a variety of solutions, for example, net zero and positive-energy buildings. At the same time, both models and controls are being challenged by increasingly complex buildings equipped with advanced information and communications technologies (ICT). This dissertation addresses these challenges by proposing a multidisciplinary, wide-ranging modeling methodology that enables new strategies for saving building energy. The core methodology utilizes novel modeling approaches to improve predictive models and produce innovative energy solutions. Models are validated and investigated using a variety of buildings and controls. Data centers and demand controlled ventilation (DCV) are the focus because they represent both "multifunctional buildings" and general energy system controls. This dissertation makes the following seven original contributions: (1) The first systematic, complete case study of a data center in which infrastructure, energy and air management performance, and waste heat recovery systems were investigated, analyzed, and quantified using long-term power consumption data. (2) A novel and tuning-free DCV building control strategy that is far superior to proportional control and more competitive than proportional-integral-derivative (PID) control. (3) An artificial neural network (ANN) model for predicting the water evaporation rate in a swimming hall. (4) A new ANN model for estimating prediction intervals and accounts for the uncertainty of point estimation for indoor conditions in an office building. (5) A new Maximum Likelihood Estimation (MLE) model for predicting constant and time-varying air change rates and a coupled model for estimating the number of occupants in an office. (6) Discovery of a new physical law for run-around heat recovery systems that can be used to develop a simulation model to estimate the system performance for constant volume air (CAV) and DCV systems. This new law was verified in different sites. (7) A new hybrid numerical-ANN model for building performance simulation. The hybrid model can improve not only the model accuracy but also the generalizability of ANN. The results demonstrate the applicability of the modeling techniques and the models, and significant energy savings in buildings. The resulting improvements in model accuracy, forecasting capability, and energy efficiency were published in eight journals. By unifying the results of eight publications, this dissertation presents a comprehensive and coherent study that advances the state-of-the-art building energy research

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Beyond rules: The next generation of expert systems

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations

Exploring the Design Space of Immersive Urban Analytics

Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices, such as HTC VIVE, Oculus Rift, and Microsoft HoloLens. These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence. In this paper, we propose an theoretical model to characterize the visualizations in immersive urban analytics. Further more, based on our comprehensive and concise model, we contribute a typology of combination methods of 2D and 3D visualizations that distinguish between linked views, embedded views, and mixed views. We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations. Finally, based on existing works, possible future research opportunities are explored and discussed.Comment: 23 pages,11 figure