Data centers have evolved dramatically in recent years, due to the advent of social networking services, e-commerce and cloud computing. The conflicting requirements are the high availability levels demanded against the low sustainability impact and cost values. The approaches that evaluate and optimize these requirements are essential to support designers of data center architectures. Our work aims to propose an integrated approach to estimate and optimize these issues with the support of the developed environment, Mercury. Mercury is a tool for dependability, performance and energy flow evaluation. The tool supports reliability block diagrams (RBD), stochastic Petri nets (SPNs), continuous-time Markov chains (CTMC) and energy flow (EFM) models. The EFM verifies the energy flow on data center architectures, taking into account the energy efficiency and power capacity that each device can provide (assuming power systems) or extract (considering cooling components). The EFM also estimates the sustainability impact and cost issues of data center architectures. Additionally, a methodology is also considered to support the modeling, evaluation and optimization processes. Two case studies are presented to illustrate the adopted methodology on data center power systems.

Due to the high demands of new technologies such as social networks, e-commerce and cloud computing, more energy is being consumed in order to store all the data produced and provide the high availability required. Over the years, this increase in energy consumption has brought about a rise in both the environmental impacts and operational costs. Some companies have adopted the concept of a green data center, which is related to electricity consumption and CO₂ emissions, according to the utility power source adopted. In Brazil, almost 70% of electrical power is derived from clean electricity generation, whereas in China 65% of generated electricity comes from coal. In addition, the value per kWh in the US is much lower than in other countries surveyed. In the present work, we conducted an integrated evaluation of costs and CO₂ emissions of the electrical infrastructure in data centers, considering the different energy sources adopted by each country. We used a multi-layered artificial neural network, which could forecast consumption over the following months, based on the energy consumption history of the data center. All these features were supported by a tool, the applicability of which was demonstrated through a case study that computed the CO₂ emissions and operational costs of a data center using the energy mix adopted in Brazil, China, Germany and the US. China presented the highest CO₂ emissions, with 41,445 tons per year in 2014, followed by the US and Germany, with 37,177 and 35,883, respectively. Brazil, with 8459 tons, proved to be the cleanest. Additionally, this study also estimated the operational costs assuming that the same data center consumes energy as if it were in China, Germany and Brazil. China presented the highest kWh/year. Therefore, the best choice according to operational costs, considering the price of energy per kWh, is the US and the worst is China. Considering both operational costs and CO₂ emissions, Brazil would be the best option.

In aluminium production, anode effects occur when the alumina content in the bath is so low that normal fused salt electrolysis cannot be maintained. This is followed by a rapid increase of pot voltage from about 4.3 V to values in the range from 10 to 80 V. As a result of a local depletion of oxide ions, the cryolite decomposes and forms climate-relevant perfluorocarbon (PFC) gases. The high pot voltage also causes a high energy input, which dissipates as heat. In order to ensure energy-efficient and climate-friendly operation, it is important to predict anode effects in advance so that they can be prevented by prophylactic actions like alumina feeding or beam downward movements. In this paper a classification model is trained with aggregated time series data from TRIMET Aluminium SE Essen (TAE) that is able to predict anode effects at least 1 min in advance. Due to a high imbalance in the class distribution of normal state and labeled anode effect state as well as possible model’s weaknesses the final F₁ score of 32.4% is comparatively low. Nevertheless, the prediction provides an indication of possible anode effects and the process control system may react on it. Consequent practical implications will be discussed.

In the smart manufacturing sector, analyzing time series data is essential for monitoring plants and machinery to prevent costly failures or shutdowns. In order to gain new insights and make better control decisions, new methods are needed for extracting information and interpreting sensor data from hundreds of systems. In this paper, we present an approach for visualizing and interpreting sensor data from TRIMET Aluminium SE Essen (TAE) using time series meta-features and principal component analysis (PCA). We describe our general approach of generating multiple two-dimensional feature spaces to identify salient and implausible sensor data. Using a set of 20 time series meta-features, we applied our approach to sensor data from TAE which were generated by thermocouples. Each step of the approach was integrated into a dashboard to ensure a user-friendly and approachable interaction in finding salient and implausible sensor data.