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Artificial neural networks for corrosion rate prediction in gas pipelines
Sumarnia, Supriyatman D.a, Sidarto K.A.a, Suratman R.a, Dasilfa R.a
a Bandung Inst. Technol., Total E and P Indonesie, 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]An artificial neural network (ANN) is adopted from the human neural network systems and is among many tools to understand complex model including corrosion. Such predictive model can be developed through corrosion rate. By ANN application, one may update its model iteratively, hence it will be adaptable for all decisions. It can be learned from the previous data and recognize patterns of data always changing and dynamic, like the ones in oil and gas fields. This paper will describe ANN that creates a program to determine the corrosion rates of the carbon-steel materials containing flowing fluid (gas). The characteristics evaluated are gas composition, temperature and pressure then regression equation of inhibition percentage will be made in the characteristics function of earlier inputs. Only data having a high level of significance will be used, and the ones of very low level of significances will be excluded in the equation. The next step is building models with ANN for which significant data based on the regression test will be used as input data to the corrosion prediction program in gas pipelines. The deliverable of this study is getting the prediction program of gas pipeline corrosion of carbon steel using MATLAB software. The program accuracy of the program is compared with the actual data.[/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][/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][/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][/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]