Abstract
The significance of coal in the world economy remains unquestionable for decades. It is also expected to be the dominant fossil fuel in the foreseeable future. The increased awareness of sustainable development reflected in the relevant regulations implies, however, the need for the development and implementation of clean coal technologies on the one hand, and adequate analytical tools on the other. The paper presents the application of the quantitative Partial Least Squares method in modeling the concentrations of trace elements (As, Ba, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sr, V and Zn) in hard coal based on the physical and chemical parameters of coal, and coal ash components. The study was focused on trace elements potentially hazardous to the environment when emitted from coal processing systems. The studied data included 24 parameters determined for 132 coal samples provided by 17 coal mines of the Upper Silesian Coal Basin, Poland. Since the data set contained outliers, the construction of robust Partial Least Squares models for contaminated data set and the correct identification of outlying objects based on the robust scales were required. These enabled the development of the correct Partial Least Squares models, characterized by good fit and prediction abilities. The root mean square error was below 10% for all except for one the final Partial Least Squares models constructed, and the prediction error (root mean square error of cross–validation) exceeded 10% only for three models constructed. The study is of both cognitive and applicative importance. It presents the unique application of the chemometric methods of data exploration in modeling the content of trace elements in coal. In this way it contributes to the development of useful tools of coal quality assessment.
Highlights
In the world of growing energy demand and increasing awareness of sustainable development coal still remains the dominant fossil fuel representing the significant share in energy supply [1]
The Partial Least Squares Method (PLS) method was applied to investigate the quantitative relationships between the concentrations of trace elements in coal and the remaining 24 parameters
The correctly constructed PLS models need to be characterized by good fit and predictive abilities. These are assessed based on the values of the Root Mean Square Error (RMS) and the RMSCV, respectively
Summary
In the world of growing energy demand and increasing awareness of sustainable development coal still remains the dominant fossil fuel representing the significant share in energy supply [1]. The environmental concerns and economic factors related to the present-day coal mining and energy sectors are the main driving forces of the development of solutions contributing to the more sustainable management of natural resources, as well as cleaner and more efficient energy generation and utilization. An attention is given to the potential recycling of valuable elements from coal ash [7,8]. This vast amount of research and applications need to be supported by the development of suitable analytical solutions, including efficient methods of experimental, operating and monitoring data sets exploration and modeling
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