Coal recovery from a coal waste dump

The possibilities and efficiency of coal recovery from the waste material located at the Central Coal Waste Dump in Poland were presented in this paper. The waste material includes significant amount of fly ash. Research conducted into determination of energetic properties of such wastes showed that the average ash content was 75.75% and the average gross calorific value was 7.81 MJ/kg. Coal was gravitationally separated from the waste material in a pulsatory jig and in a spiral washer including size fractions: 30-5 and 8-0 mm (this was crushed to a size <3.2 mm), respectively. The application of the pulsatory jig (pulse classifier) allowed to obtain a high-quality energetic concentrate with the ash content lower than 12% and the gross calorific value higher than 26 MJ/kg (with average yield 7.8%). The spiral separator gave much worse results. The average gross calorific value for the concentrate was 11.6 MJ/kg, with the high ash content 56.5% and yield approximately 26%

Coal recovery from a coal waste dump

The possibilities and efficiency of coal recovery from the waste material located at the Central Coal Waste Dump in Poland were presented in this paper. The waste material includes significant amount of fly ash. Research conducted into determination of energetic properties of such wastes showed that the average ash content was 75.75% and the average gross calorific value was 7.81 MJ/kg. Coal was gravitationally separated from the waste material in a pulsatory jig and in a spiral washer including size fractions: 30-5 and 8-0 mm (this was crushed to a size <3.2 mm), respectively. The application of the pulsatory jig (pulse classifier) allowed to obtain a high-quality energetic concentrate with the ash content lower than 12% and the gross calorific value higher than 26 MJ/kg (with average yield 7.8%). The spiral separator gave much worse results. The average gross calorific value for the concentrate was 11.6 MJ/kg, with the high ash content 56.5% and yield approximately 26%

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018