Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future

A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

OptEEmAL: Decision-Support Tool for the Design of Energy Retrofitting Projects at District Level

Designing energy retrofitting actions poses an elevated number of problems, as the definition of the baseline, selection of indicators to measure performance, modelling, setting objectives, etc. This is time-consuming and it can result in a number of inaccuracies, leading to inadequate decisions. While these problems are present at building level, they are multiplied at district level, where there are complex interactions to analyse, simulate and improve. OptEEmAL proposes a solution as a decision-support tool for the design of energy retrofitting projects at district level. Based on specific input data (IFC(s), CityGML, etc.), the platform will automatically simulate the baseline scenario and launch an optimisation process where a series of Energy Conservation Measures (ECMs) will be applied to this scenario. Its performance will be evaluated through a holistic set of indicators to obtain the best combination of ECMs that complies with user's objectives. A great reduction in time and higher accuracy in the models are experienced, since they are automatically created and checked. A subjective problem is transformed into a mathematical problem; it simplifies it and ensures a more robust decision-making. This paper will present a case where the platform has been tested.This research work has been partially funded by the European Commission though the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 680676. All related information to the project is available at https://www.opteemal-project.eu

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Sustainable operations of industrial symbiosis: an enterprise input-output model integrated by agent-based simulation

Industrial symbiosis (IS) is a key for implementing circular economy. Through IS, wastes produced by one company are used as inputs by other companies. The operations of IS suffers from uncertainty barriers since wastes are not produced upon demand but emerge as secondary outputs. Such an uncertainty, triggered by waste supply-demand quantity mismatch, influences IS business dynamics. Accordingly, companies have difficulty to foresee potential costs and benefits of implementing IS. The paper adopts an enterprise input-output model providing a cost–benefit analysis of IS integrated to an agent-based model to simulate how companies share the total economic benefits stemming from IS. The proposed model allows to explore the space of cooperation, defined as the operationally favourable conditions to operate IS in an economically win-win manner. This approach, as a decision-support tool, allows the user to understand whether the IS relationship is created and how should the cost-sharing policy be. The proposed model is applied to a numerical example. Findings show that cost-sharing strategies are dramatically affected by waste supply-demand mismatch and by the relationship between saved and additional costs to run IS. Apart from methodological and theoretical contributions, the paper proposes managerial and practical implications for business strategy development in IS

Crossing the death valley to transfer environmental decision support systems to the water market

Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development.The authors would like to thank the Catalan Water Agency (Agència Catalana de l’Aigua), Besòs River Basin Regional Administration (Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding (CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and ICRA were recognized as consolidated research groups by the Catalan Government under the codes 2014-SGR-1168, 2013-SGR-1304 and 2014-SGR-291.Peer ReviewedPostprint (published version

Sustainable machining - correlation of the optimization by minimum energy, minimum manufacturing time and cost of production

The machining process leads the indices of productivity and employability in a world level and has an enormous influence at social and economics standards, however it requires machinery that consume high levels of energy, chemical fluids and has great emissions of greenhouse gases. In our days as governments and clients increase their demands for the degradation of ecosystems, also increase the need for companies to implement sustainable policies and improve their environmental performances. The reduction of energy consumption and consequently the reduction of fossil supplies are a major source of concern at this level. This article establish a bridge between the classical approaches of optimization models of machining processes (Maximizing Production Rate and Minimizing Production Cost), and reduction of electricity. For a single pass turning it was used a mathematical model to analyse the data taken as a reference, optimizing the critical parameters of consumption of time, money and energy