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On the verge of Cyber-Physical Systems (CPS), companies will need, sooner or later, to adapt their systems in order to follow the emergent visions of Industrie 4.0 and Industrial Internet demanding the digitalization of their processes, preventing the losing of their competiveness levels. The engineering of such innovative manufacturing control systems assumes a crucial challenge without which becomes hard to convince researchers and, primarily, practitioners of the proposed architecture potentials. This paper describes the engineering aspects of deploying an ADACOR (ADAptive holonic COntrol aRchitecture for distributed manufacturing systems) based CPS for a real small-scale production system. Since the solution is using agent technology, a special attention is devoted to the interface from the agent control layer to the physical resources using the industrially adopted OPC-UA (OPC Unified Architecture). At the end, some lessons learned in engineering this CPS are drawn.

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Intelligent Compaction (IC), which is a part of compaction management, is a real-time automatic adjustment and continuous compaction control technology of geomaterials and asphalt layers. Adjustment of the compaction parameters by the equipment is conducted simultaneously to the compaction process, as well as the continuous measurement of a dynamic compaction value, which is an indicator of the material's degree of compaction. This chapter seeks to assess the advantages and disadvantages of IC, as well as formulating a comparison with conventional compaction methods in terms of efficiency. This goal was achieved through in situ application of various technologies to two distinct types of material: a soil-rockfill mixture and a sandy soil. Data was obtained and analysed by the IC continuous information, as well as by the application of several different conventional compaction control tests and methods. Results show that the IC technology presents a superior performance, as well as various advantages when compared to conventional compactors.

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Wind power probabilistic forecast is being used as input in several decision-making problems, such as stochastic unit commitment, operating reserve setting and electricity market bidding. This work introduces a new on-line quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. Its application to the field of wind power forecasting involves a discussion on the choice of the bias term of the quantile models, and the consideration of the operational framework in order to mimic real conditions. Benchmark against linear and splines quantile regression models was performed for a real case study during a 18 months period. Model parameter selection was based on k-fold cross-validation. Results showed a noticeable improvement in terms of calibration, a key criterion for the wind power industry. Modest improvements in terms of Continuous Ranked Probability Score (CRPS) were also observed for prediction horizons between 6 and 20 h ahead.

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Assessing eco-efficiency performance of a production system is of great importance, since such assessment enables one to make an informed decision concerning economic and environmental performance of elementary systems within industrial productions systems. The framework presented in this paper is based on the eco-efficiency principles and four cornerstones i) Data inventory, ii) Environmental performance evaluation, iii) Environmental impact assessment and iv) Cost models/Value data. The Eco-Efficiency Integrated Methodology for Production Systems (ecoPROSYS) approach relies on the use of a systematized and organized set of indicators easy to understand/analyse promoting continuous improvement and a more efficient use of resources and energy. The goal is to assess eco-efficiency performance in order to support decision and enable the maximization of product/processes value creation and minimization of environmental burdens. The methodology was applied to a Medium Density Fibreboard (MDF) finishing line. The results of the study intend to validate the applicability of ecoPROSYS. The case study showed that the cutting and the feeding table have superior eco-efficiency performance while packing and sanding have lower eco-efficiency performance. The presented framework is a powerful tool that can be used to identify and quantify key variables, assess alternative scenarios, evaluate environmental aspects, environmental influence and assess unit processes and overall eco-efficiency performance. (C) 2016 The Authors. Published by Elsevier

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