Method of fuzzy controller adaptation

Muravyova, E. A.; Sharipov, M. I.

doi:10.2991/aime-17.2017.82

Cited by 14 publications

(1 citation statement)

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“…With such a steam supply the heating of the entire space of the autoclave is very slow. The 2nd mode of operation is the worst since in this case the steam is supplied to the left side, the autoclave heats up rather slowly and unevenly and there are large temperature deviations: in the left part of the autoclave the sensors show a temperature of 190° C and in the right part 179° C [9].…”

Section: Analysis Of the Resulting Modelmentioning

confidence: 99%

Simulation Model of an Autoclave Operation Using iThink Software

Muravyova

Shokurov

2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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This research paper is focused on temperature control in an autoclave used for the production of slag blocks from ash and slag materials of heat and power plants. This is done in order to increase the autoclave performance, reduce the amount of rejects in the finished slag blocks, increase the quality of slag blocks and optimize the consumption of energy resources. To solve this problem, it is proposed to use the simulation model of the autoclave in iThink simulation software package with the simultaneous supply of saturated steam not only through one pipe, but through several pipes. This allows to heat up the entire volume of the autoclave more evenly and adjusts the steam supply based on the uniformity of heating-up of slag blocks subject to the constantly changing chemical composition of raw materials. Using this model, depending on a number of input parameters, it is possible to simulate the autoclave operation process by displaying the operation dynamics in graphical form over time, as well as simulate the mutual influence of control loops on each other without spending a lot of time and resources. The paper also provides an analysis of the obtained experimental model, which allows us to assess the performance capability, adequacy and effectiveness of the model.

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Section: Analysis Of the Resulting Modelmentioning

confidence: 99%