Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation


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In the process of recycling chemical product in coking object, ammonia and tar were indispensable both metallurgy and agriculture, so the control of separation process for tar-ammonia was one of the most important control problems. Due to the density difference between the tar and ammonia was greater, easier to separate, the control method based on PID was used in field at present. But the control effect of traditional PID was not good because of environment change and fluctuation in material composition. Separation process for tar-ammonia was analyzed firstly, in view of the shortcoming of traditional PID control algorithm, single neuron PID control algorithm based on variable scale method was adopted through using optimization method. Detailed algorithm steps were designed and applied to tar-ammonia separation system. Simulation results show that by comparison with traditional PID algorithm, the algorithm have the following advantages: faster learning speed, shorter adjusted time and good convergence performance.



Advanced Materials Research (Volumes 139-141)

Edited by:

Liangchi Zhang, Chunliang Zhang and Tielin Shi




T. P. Zhou and W. F. Huang, "Application of Single Neuron PID Control Based on Variable Scale Algorithm in Tar-Ammonia Separation", Advanced Materials Research, Vols. 139-141, pp. 1945-1949, 2010

Online since:

October 2010




[1] W.H. Zheng, H. CH. Liu and K. Zhou: Iron and Steel, Vol. 39 (2004) No. 3, pp.67-73. (In Chinese).

[2] Q.L. Gu, M.H. Hai and R. Zhang: Basic Automation, Vol. 10 (2003) No. 2, pp.149-152. (In Chinese).

[3] G.Q. Li and J.F. Jiang: Journal of Changde Teachers University, Vol. 15 (2003) No. 2, pp.31-33. (In Chinese).

[4] Q. He and Y.N. Wang: Electrical Drive Automation, Vol. 22 (2000) No. 1, pp.27-28. (In Chinese).

[5] H.Y. Cao, W.Q. Li and W.H. Li: Microcomputer Development, Vol. 11 (2001) No. 4, pp.50-53. (In Chinese).

[6] Y.N. Guo: Study of coking intelligent optimization control based on MAS (Ph.D., China University of Mining and technology, China 2003), p.139.

[7] Q.F. Teng, CH.L. Qing and J.W. Dang: Journal of Lanzhou Railway Institute, Vol. 21 (2003) No. 2, pp.87-89. (In Chinese).

[8] J.M. Sun: Optimal Design of Mechanical (Machinery Industry Press, China 1989).

[9] J. Zhang: Application of MATLAB in the control system (Electronic Industry Press, China 2007).

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