[vc_empty_space][vc_empty_space]
Predicting Sulfur Content of Desulfurizer using Data-Driven based Inferential Measurement: An Ammonia Plant Case
Heryuano B.T.a, Nazaruddin Y.Y.a, Hadisupadmo S.a
a Instrumentation Control Research Group Institut Teknologi Bandung, Department of Engineering Physics, 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]© 2020 IEEE.The parameters of the sulfur content in the process gas of the desulfurizer unit is an important issue for the production process of ammonia (NH3) in the ammonia plant. Due to limited conditions and equipments at the plant, quite often that the measurement of sulfur content is still carried out indirectly (off-line) by sampling and analysis methods in the laboratory, causing significant delays. In this paper, an alternative method to predict the value of sulfur concentration will be proposed using the data-driven based inferential measurement. The sulfur concentration (as primary variable) will be predicted from the available measured data (as secondary data) using neuro-fuzzy based method. In this case, a model representing the desulfurizer unit needs to be reconstructed based on input and output relationships that affect the main variables in the ammonia plant. For verifying the applicability of the proposed method, real-time operational data from a running ammonia plant located in East Kalimantan, Indonesia will be used. After an extensive simulation studies, it is show that the sulfur concentration can be predicted quite successfully, with RMSE value of the designed inferential estimator is 0.003134.[/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]Extensive simulations,Inferential measurement,Input and outputs,Production process,Real time operational data,Secondary datum,Sulfur concentrations,Sulfur contents[/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 plant,data-driven,inferential measurement,sulfur concentration[/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]This research is supported by PT Pupuk Kalimantan Timur, Indonesia through the Study Assignment Program 2020 for employees.[/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/ICSPC50992.2020.9305785[/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]