[vc_empty_space][vc_empty_space]
Applying neuro-fuzzy approach for ratio control of primary reformer in a petrochemical plant
Nazaruddin Y.Y.a, Dewi S.a, Ekawati E.a, Nugroho S.b
a Instrumentation and Control Research Group, Department of Engineering Physics, Institut Teknologi Bandung, Indonesia
b Engineering Department, PT. Petrokimia Gresik, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This paper is concerned with a development of an alternative ratio control method using adaptive neuro-fuzzy approach and its implementation to control steam-gas ratio of primary reformer in a petrochemical plant in Gresik, Indonesia. Ratio control is used to ensure that two flows, i.e steam and gas, are kept at the same ratio even if the flows are changed. Adaptive neuro-fuzzy approach is implemented to model the dynamic inverse of the non-linear control valve system which controls the gas input to the reformer. The modeling is carried out in the learning phase using off-line technique, while in the design process of the neuro-fuzzy controller, an adaptive network will be employed as a building block. An hybrid learning rule is also used to minimize the difference between the actual and given desired trajectory. Results of simulation study demonstrate how the designed control technique performs well in tracking the changing of desired set-point. Performance comparison is also made between the designed and PI controllers. © 2013 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Desired trajectories,Hybrid learning rule,Inverse learning,Neuro-Fuzzy,Neuro-fuzzy controller,Performance comparison,Primary reformer,Ratio control[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]inverse learning control,neuro-fuzzy,primary reformer,ratio control[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICA.2013.6734050[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]