[vc_empty_space][vc_empty_space]
Integration of PSO-Based Virtual Sensor and PID to Control Benfield Concentration of a Stripper Unit in a Fertilizer Plant
Nazaruddin Y.Y.a, Anditio B.a, Andrini A.D.a
a Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung, 40132, 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 JSME.Production process in a fertilizer plant requires some physical variables, which are called primary variables, to be measured indirectly due to the limitation of measuring conditions and methods. Alternatively, virtual sensing system is used to predict and estimate these primary variables by using information from the secondary variables which are relatively easier to be measured. A development of virtual sensing system or virtual sensor to estimate benfield solution concentration of a stripper unit in a fertilizer plant is introduced in this paper. The virtual sensor consisting of a Diagonal Recurrent Neural Network (DRNN) method is used to model the relationship of primary and secondary variables, coupled with the Extended Kalman Filter (EKF) that will estimates the primary variables based on the measurement made by the secondary variables. During the learning process of DRNN, Particle Swarm Optimization (PSO) is proposed, to determine the optimal weights of the network. Using the estimated primary variable, a PID control strategy is designed and optimized by PSO to obtain an optimized controlled response. Using the real-time operational data acquired from the stripper unit of a fertilizer plant, results show that the proposed control strategy is able to control the benfield solution of the stripper unit satisfactorily.[/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]Diagonal recurrent neural networks,Fertilizer plant,Measuring conditions,PID controllers,Predict and estimate,Real time operational data,Solution concentration,Virtual sensor[/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]Extended Kalman Filter,Fertilizer Plant,Particle Swarm Optimization,PID Controller,Virtual Sensor[/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]