In recent years, the integration of Digital Twins (DT) for the adoption of smarter maintenance strategies has grown exponentially in different industrial sectors. New IoT and edge computing systems are being developed for this purpose, however, there are still some open issues and challenges to be solved. Firstly, this paper presents new approaches to the initial dependencies of the studied solution and make a new proposal to improve the interoperability of the presented system. Secondly, this paper provides a methodology applicable to similar developments of edge-based AI (Artificial Intelligence) solution, which comprises of four phases: the presentation of the multi-objective problem and the pre-selection of AI-based models, the description of the evaluation architecture, the profiling of the different models for the selection of the most suitable one and explainable AI strategies for getting insights of the selected model. Finally, it presents a use case of an edge-solution for the railway catenary geometry diagnostic (stagger amplitude of the overhead wire), saving the interoperability of the message exchange with other systems is provided.

Maintenance is one of the major concerns of the industrial sector. Acquiring better levels of maintenance maturity is one of the objectives to be achieved. Therefore, prescriptive maintenance is one of the areas of recent research. Current works in literature are focused on specifics of maintenance strategies (from preventive to prescriptive), usually related to a fixed asset. No previous work has been identified regarding the methodology and guidelines to be followed to be able to evolve within the different strategies from a generic perspective. To address the lack of a methodology that shows a more evolutionary path between maintenance strategies, this paper presents Maintenance Maturity Model M3: a maturity model that identifies three areas of action, four levels of maturity, and the activities to be carried out in each of them to make progress in the maturity level of maintenance strategies. The implementation of prescriptive maintenance should be done in a gradual way, starting at the lowest levels. M3 approaches the problem from a broader perspective, analyzing the 18 different domains and the different levels of prior maturity to be considered for prescriptive maintenance. A study has also been carried out on the different maintenance actions and the applicability of the proposed M3 maturity model to the railway infrastructure maintenance is discussed. In addition, this paper also highlights future research lines and open issues.

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human-machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)(-1) ) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts.

The monitoring of overhead contact lines (OCL) is a key part of railway infrastructure maintenance. This paper proposes a methodology to assess the lateral geometry of contact wire, the so-called stagger, by using the dynamic response of a pantograph. The methodology is tested in a validated virtual environment that resembles the behaviour of the pantograph when it interacts with the OCL. A signal processing is developed to define features relating the lateral position of the contact wire with the vertical acceleration of the contact strip. It is demonstrated that these features have a clear and close connection with the lateral position of the contact wire. Subsequently, model-driven machine learning algorithms are defined using these features to address the OCL stagger prediction and the detection of out-of-range lateral displacement due to a faulty steady-arm. The methodology shows a good prediction performance in the estimation of the stagger amplitude/central position and the steady-arms diagnosis. The prediction of the stagger amplitude is performed with a root-mean-square error of 4.7(10) mm. In addition, the area under the Precision-Recall curve is 0.952 CI95 [0.940, 0.962] for the steady-arms diagnosis.

Advanced real time - Instrumentation, Control, and Automation (Art-ICA) controllers are an advanced control solution for biological nutrient removal wastewater treatment plants. Art-ICA has been previously shown to be capable of enhancing nutrient removal performance in BNR plants, at lower energy expenditures. However, the impact that this control solution has on the greenhouse gas emissions from full-scale wastewater treatment plants has not previously been addressed. This work addresses the effect of art-ICA on the performance, energy consumption and greenhouse gas emissions of two full-scale WWTPs, Chelas and Castelo Branco (Portugal). The raw wastewater, nitrous oxide emissions, energy consumption and water discharges were quantified in two independent trains operated under different operational modes, conventional operation and art-ICA control. The implementation of the art-ICA strategy improved the effluent quality and reduced the operational costs, resulting in a better performance of these WWTPs. The art-ICA controllers activation led to a reduction of 54% and 7-10% of the total nitrogen effluent and in the specific energy consumption, respectively. Moreover, process control with art-ICA did not have a negative impact on the N2O emissions of the plants, and contributed to lower global warming potential by the facilities. The lower indirect carbon dioxide production due to lower energy consumption contributes to the observation that art-ICA control is environmentally preferable to conventional control. (C) 2018 Elsevier Ltd. All rights reserved.

Internal Circulation (IC) anaerobic systems are especially suitable when plant designs that involve both small footprints and high organic loading rates (>25 kg COD m(-3) d(-1)) are required. However, given that operating anaerobic processes at high organic loads increases their vulnerability to external disturbances, real-time indicators of the stability conditions become particularly pertinent for IC reactors. This paper addresses the design and full-scale validation of a software sensor that uses only feeding flow-rate and biogas flow-rate measurements to classify the operating conditions of the reactor as "strongly", "moderately" or "weakly" stable. A simulation-based procedure was used to design the software sensor and configure its parameters. Then, the performance of the software sensor was tested under real conditions in a full-scale IC reactor of 1679 m(3) installed in a recycled paper mill (RPM). (C) 2018 Elsevier Ltd. All rights reserved.

This paper introduces a new mathematical model built under the PC-PWM methodology to describe the aeration process in a full-scale WWTP. This methodology enables a systematic and rigorous incorporation of chemical and physico-chemical transformations into biochemical process models, particularly for the description of liquid-gas transfer to describe the aeration process. The mathematical model constructed is able to reproduce biological COD and nitrogen removal, liquid-gas transfer and chemical reactions. The capability of the model to describe the liquid-gas mass transfer has been tested by comparing simulated and experimental results in a full-scale WWTP. Finally, an exploration by simulation has been undertaken to show the potential of the mathematical model. (C) 2017 Elsevier Ltd. All rights reserved.

Given the shift in perception of wastewater treatment plants as water resource recovery facilities, conventional mathematical models need to be updated. The resource recovery perspective should be applied to new processes, technologies and plant layouts. The number and level of models proposed to date give an overview of the complexity of the new plant configurations and provides a wide range of possibilities and process combinations in order to construct plant layouts. This diversity makes the development of standard, modular and flexible tools and model libraries that allow the incorporation of new processes and components in a straightforward way a necessity. In this regard, the plant-wide modelling (PWM) library is a complete model library that includes conventional and advanced technologies and that allows economic and energetic analyses to be carried out in a holistic way. This paper shows the fundamentals of this PWM library that is built upon the above-mentioned premises and the application of the PWM library in three different full-scale case studies.

Although often perceived as tools for use by scientists, mathematical modelling and simulation become indispensable when control engineers have to design controllers for real-life wastewater treatment plants (WWTPs). Nonetheless, the design of effective controllers in the wastewater domain using simulations requires effects, such as the nonlinearity of actuators, the time response of sensors, plant model uncertainties, etc. to have been reproduced beforehand. Otherwise, control solutions verified by simulation can completely underperform under real conditions. This study demonstrates that, when all the above effects are included at the outset, a systematic use of simulations guarantees high quality controllers in a relatively short period of time. The above is exemplified through the Mekolalde WWTP, where a comprehensive simulation study was conducted in order to develop a control product for nitrogen removal. Since its activation in May 2011, the designed controller has been permanently working in the plant which, from this time onwards, has experienced significant improvements in the quality of water discharges combined with a lower utilization of electricity for wastewater treatment.

The principles of economic evaluation and cost benefit analysis implemented in sewage treatment plants; Economic evaluation of innovative technologies aiming to increase the energy efficiency of the sewage treatment plants; Instrumentation, monitoring and real-time control strategies for efficient sewage treatment plant operation; Process integration to improve carbon, nitrogen and phosphorus removal with less aeration requirements and less sludge production; Bioreactor development with less aeration requirement; Improvement of anaerobic digestion of sewage wastewater and sludge; Development of microbial fuel cells for electricity production from sewage; Nutrient recovery from sewage; Cost saving management strategies or policies; Economic evaluation of innovative technologies aiming to increase the energy efficiency of the sewage treatment plants: Case studies.