Big Data on Decision Making in Energetic Management of Copper Mining

Indexado en: Web of Science; Scopus.It is proposed an analysis of the related variables with the energetic consumption in the process of concentrate of copper; specifically ball mills and SAG. The methodology considers the analysis of great volumes of data, which allows to identify the variables of interest (tonnage, temperature and power) to reach to an improvement plan in the energetic efficiency. The correct processing of the great volumen of data, previous imputation to the null data, not informed and out of range, coming from the milling process of copper, a decision support systems integrated, it allows to obtain clear and on line information for the decision making. As results it is establish that exist correlation between the energetic consumption of the Ball and SAG Mills, regarding the East, West temperature and winding. Nevertheless, it is not observed correlation between the energetic consumption of the Ball Mills and the SAG Mills, regarding to the tonnages of feed of SAG Mill. In consequence, From the experimental design, a similarity of behavior between two groups of different mills was determined in lines process. In addition, it was determined that there is a difference in energy consumption between the mills of the same group. This approach modifies the method presented in [1].(a)http://www.univagora.ro/jour/index.php/ijccc/article/view/2784/106

Intelligent energy management using data mining techniques at Bosch Car Multimedia Portugal facilities

The fusion of emerged technologies such as Artificial Intelligence, cloud computing, big data, and the Internet of Things in manufacturing has pioneered this industry to meet the fourth stage of the industrial revolution (industry 4.0). One major approach to keeping this sector sustainable and productive is intelligent energy demand planning. Monitoring and controlling the consumption of energy under industry 4.0, directly results in minimizing the cost of operation and maximizing efficiency. To advance the research on the adoption of industry 4.0, this study examines CRISP-DM methodology to project data mining approach over data from 2020 to 2021 which was collected from industrial sensors to predict/forecast future electrical consumption at Bosch car multimedia facilities located at Braga, Portugal. Moreover, the influence of indicators such as humidity and temperature on electrical energy consumption was investigated. This study employed five promising regression algorithms and FaceBook prophet (FB prophet) to apply over data belonging to two HVAC (heating, ventilation, and air conditioning) sensors (E333, 3260). Results indicate Random Forest (RF) algorithms as a potential regression approach for prediction and the outcome of FB prophet to forecast the demand of future usage of electrical energy associated with HVAC presented. Based on that, it was concluded that predicting the usage of electrical energy for both data points requires time series techniques. Where "timestamp" was identified as the most effective feature to predict consume of electrical energy by regression technique (RF). The result of this study was integrated with Intelligent Industrial Management System (IIMS) at Bosch Portugal.- (undefined

Utilising the Cross Industry Standard Process for Data Mining to reduce uncertainty in the Measurement and Verification of energy savings

This paper investigates the application of Data Mining (DM) to predict baseline energy consumption for the improvement of energy savings estimation accuracy in Measurement and Verification (M&V). M&V is a requirement of a certified energy management system (EnMS). A critical stage of the M&V process is the normalisation of data post Energy Conservation Measure (ECM) to pre-ECM conditions. Traditional M&V approaches utilise simplistic modelling techniques, which dilute the power of the available data. DM enables the true power of the available energy data to be harnessed with complex modelling techniques. The methodology proposed incorporates DM into the M&V process to improve prediction accuracy. The application of multi-variate regression and artificial neural networks to predict compressed air energy consumption in a manufacturing facility is presented. Predictions made using DM were consistently more accurate than those found using traditional approaches when the training period was greater than two months

EMPOWERING, a smart Big Data framework for sustainable electricity suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability

Energy consumption modelling using deep learning technique — a case study of EAF

Energy consumption is a global issue which government is taking measures to reduce. Steel plant can have a better energy management once its energy consumption can be modelled and predicted. The purpose of this study is to establish an energy value prediction model for electric arc furnace (EAF) through a data-driven approach using a large amount of real-world data collected from the melt shop in an established steel plant. The data pre-processing and feature selection are carried out. Several data mining algorithms are used separately to build the prediction model. The result shows the predicting performance of the deep learning model is better than the conventional machine learning models, e.g., linear regression, support vector machine and decision tree