LAPSE:2025.0307
Published Article

LAPSE:2025.0307
Production scheduling based on Real-time Optimization and Zone Control Nonlinear Model Predictive Controller
June 27, 2025
Abstract
The motivation of this work is an application of a production scheduling based on Real-Time Optimization and Zone Control Nonlinear Model Predictive Controller on a liquid recovery unit of an LPG production plant. In this unit, the scheduling-relevant disturbances occur on a time scale relevant to the system dynamics; thus, we propose a novel combination of a well-known control strategies leading to a hierarchical two-layered strategy, where the lower layer employs a zone control nonlinear model predictive controller (NMPC) to define inventory setpoints while the upper layer uses real-time optimization (RTO) to determine optimal plant-wide flow rates from an economic perspective. Unlike a traditional RTO, the proposed upper-layer problem is parameterized by product demands, with a distinct optimization problem formulated for each demand scenario. Our novel approach allows for proactive mitigation of potential inventory issues by dynamically recalculating the distribution of plant products. This approach addresses the challenges of storage bottlenecks and inventory fluctuations, and it is more effective than the typical operator decisions from an economic perspective in the system of interest.
The motivation of this work is an application of a production scheduling based on Real-Time Optimization and Zone Control Nonlinear Model Predictive Controller on a liquid recovery unit of an LPG production plant. In this unit, the scheduling-relevant disturbances occur on a time scale relevant to the system dynamics; thus, we propose a novel combination of a well-known control strategies leading to a hierarchical two-layered strategy, where the lower layer employs a zone control nonlinear model predictive controller (NMPC) to define inventory setpoints while the upper layer uses real-time optimization (RTO) to determine optimal plant-wide flow rates from an economic perspective. Unlike a traditional RTO, the proposed upper-layer problem is parameterized by product demands, with a distinct optimization problem formulated for each demand scenario. Our novel approach allows for proactive mitigation of potential inventory issues by dynamically recalculating the distribution of plant products. This approach addresses the challenges of storage bottlenecks and inventory fluctuations, and it is more effective than the typical operator decisions from an economic perspective in the system of interest.
Record ID
Keywords
Model Predictive Control, Planning & Scheduling, Process Operations, Real-time Optimization, Zone Control
Subject
Suggested Citation
Matias J, Acevedo A. Production scheduling based on Real-time Optimization and Zone Control Nonlinear Model Predictive Controller. Systems and Control Transactions 4:968-973 (2025) https://doi.org/10.69997/sct.126350
Author Affiliations
Matias J: KU Leuven, Department of Chemical Engineering, Sint-Katelijne-Waver, Belgium
Acevedo A: YPFB Refinación S.A., Service Management, Cochabamba, Bolívia
Acevedo A: YPFB Refinación S.A., Service Management, Cochabamba, Bolívia
Journal Name
Systems and Control Transactions
Volume
4
First Page
968
Last Page
973
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
Other Meta
PII: 0968-0973-1170-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0307
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https://doi.org/10.69997/sct.126350
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Jun 27, 2025
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References Cited
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