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Date submitted2022-05-13
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Date accepted2022-09-24
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Date published2022-11-03
Rapid detection of coal ash based on machine learning and X-ray fluorescence
Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.
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Date submitted2015-12-01
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Date accepted2016-02-29
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Date published2016-12-23
Simulation of diesel engine energy conversion processes
- Authors:
- A. S. Afanasev
- A. A. Tretyakov
In order to keep diesel engines in good working order the troubleshooting methods shall be improved. For their further improvement by parameters of associated processes a need has arisen to develop a diesel engine troubleshooting method based on time parameters of operating cycle. For such method to be developed a computational experiment involving simulation of diesel engine energy conversion processes has been carried out. The simulation was based on the basic mathematical model of reciprocating internal combustion engines, representing a closed system of equations and relationships. The said model has been supplemented with the engine torque dynamics taking into account the current values of in-cylinder processes with different amounts of fuel injected, including zero feed. The torque values obtained by the in-cylinder pressure conversion does not account for mechanical losses, which is why the base simulation program has been supplemented with calculations for the friction and pumping forces. In order to determine the indicator diagram of idle cylinder a transition to zero fuel feed mode and exclusion of the combustion process from calculation have been provisioned.