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Data-Driven Construction of Chemical Reaction Network Graph Using Constrained LASSO
Tamba T.A.a, Nazaruddin Y.Y.b
a National Center for Sustainable Transportation Technology (NCSTT), Bandung, Indonesia
b Instrumentation 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.This paper proposes a data-driven method for the automatic construction of chemical reaction network graph. For a given set of chemical species measurements and their (approximate) time derivatives, the proposed method first estimates the nonlinear dynamic model of the reaction using an L1 penalty type sparse identification approach called ‘constrained least absolute shrinkage and selection operator’ (Con-strained LASSO). Using the estimated model, a network graph construction approach that takes into account the dynamical constraints (e.g. non-negativity and law of mass action kinetics) of chemical reaction networks is then presented. Simulation result is also presented to illustrate the proposed method.[/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]Automatic construction,Chemical reaction networks,constrained LASSO,Data-driven construction,Data-driven methods,Dynamical constraints,Identification approach,Least absolute shrinkage and selection operators[/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]Chemical reaction networks,constrained LASSO,graph theory,kinetic realization[/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][{‘$’: ‘This research was supported by the 2019 World Class University program at Institut Teknologi Bandung (ITB), Indonesia, and in part by USAID through the Sustainable Higher Education Research Alliances (SHERA)’}, {‘$’: ‘This research was supported by the 2019 World Class University program at Institut Teknologi Bandung (ITB), Indonesia, and in part by USAID through the Sustainable Higher Education Research Alliances (SHERA) program -Centre for Collaborative (CCR) National Center for Sustainable Transportation Technology (NCSTT) under grant number IIE00000078-ITB-1.’}][/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.2019.8916720[/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]