Detection of outliers in pollutant emissions from the Soto de Ribera coal-fired power plant using functional data analysis: a case study in northern Spain.

For more than a century, air pollution has been one of the most important environmental problems in cities. Pollution is a threat to human health and is responsible for many deaths every year all over the world. This paper deals with the topic of functional outlier detection. Functional analysis is a novel mathematical tool employed for the recognition of outliers. This methodology is applied here to the emissions of a coal-fired power plant. This research uses two different methods, called functional high-density region (HDR) boxplot and functional bagplot. Please note that functional bagplots were developed using bivariate bagplots as a starting point. Indeed, they are applied to the first two robust principal component scores. Both methodologies were applied for the detection of outliers in the time pollutant emission curves that were built using, as inputs, the discrete information available from an air quality monitoring data record station and the subsequent smoothing of this dataset for each pollutant. In this research, both new methodologies are tested to detect outliers in pollutant emissions performed over a long period of time in an urban area. These pollutant emissions have been treated in order to use them as vectors whose components are pollutant concentration values for each observation made. Note that although the recording of pollutant emissions is made in a discrete way, these methodologies use pollutants as curves, identifying the outliers by a comparison of curves rather than vectors. Then, the concept of outlier goes from being a point to a curve that employs the functional depth as the indicator of curve distance. In this study, it is applied to the detection of outliers in pollutant emissions from a coal-fired power plant located on the outskirts of the city of Oviedo, located in the north of Spain and capital of the Principality of Asturias. Also, strengths of the functional methods are explained.

Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.