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Design of Nonlinear Adaptive-Predictive Control System with ANFIS Modeling for Urea Plant Reactor Unit
Muhammad A.a, Nazaruddin Y.Y.b, Siregar P.I.b
a PT. Pupuk Kalimantan Timur, Instrumentation and Control Department, Bontang, Indonesia
b Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung, 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]© 2019 IEEE.The reactor unit in the urea fertilizer plant is one of the units that functions to react the components forming the urea fertilizer, so that this unit is one of the most critical part in the plant. During the operation of this unit, the fluid height of the liquid inside the reactor will be kept stable so that the reaction in the reactor can react perfectly according to the product specifications. In order to maintain this level and to observe the dynamics of reactor unit, a model that can describe the behavior of the reactor is needed. In this investigation, a model for the reactor unit will be developed by applying the Adaptive Neuro Fuzzy Inference System (ANFIS) method using collected real time operational data. Moreover, to keep the level of reactor steady, a level controller designed using inverse dynamics method based on ANFIS is also proposed in this paper. The results from modelling investigation show that ANFIS based model is able to follow the actual system output quite satisfactorily with an RMSE value of 0.47995. In addition, by using the proposed control scheme it is found that the ANFIS inverse dynamics controller yields better performance than the currently existing conventional PI controller, with RSME value of 0.1592 while RSME of using conventional PI controllers is 0.6447.[/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]Adaptive neuro-fuzzy inference system,Adaptive predictive control,ANFIS,Conventional-PI controller,Fertilizer plant,Inverse control,Inverse dynamics controllers,Reactor[/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]ANFIS,Identification,Inverse Control,Reactor,Urea Fertilizer Plant[/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.2019.8916735[/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]