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Design of Neuro-Fuzzy based inferential measurement of stripper unit in a fertilizer plant
Nazaruddin Y.Y.a, Hakim I.R.a, Tamba T.A.a, Nugroho S.b
a Instrumentation and Control Research Group, Department of Engineering Physics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
b PT. Pupuk Kalimantan Timur, Bontang, Kalimantan Timur, 75313, 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]© 2016 IEEE.This paper proposes an alternative solution to overcome the difficulties in measuring the primary variable of a stripper unit in a fertilizer plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique. Inferential measurement is a method to predict the value of the primary variable of the model generated by the input-output relationships of the process affecting the primary variable the process. Using the real-time operational data collected from stripper unit of the fertilizer plant, the technique was able to estimate the value of the primary variable (benfield solution) with error criteria (RMSE value) of 0.467 in the learning stage, and 0.447 at the validation stage.[/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,Alternative solutions,Fertilizer plant,inferential estimator,Inferential measurement,Real time operational data,stripper unit,Validation stages[/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]adaptive neuro-fuzzy inference system,Ammonia process,inferential estimator,Inferential measurement,stripper unit[/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.2016.7811482[/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]