Optimization of multipurpose process plant operations: A multi-time-scale maintenance and production scheduling approach

Biondi, Matteo; Sand, Guido; Harjunkoski, Iiro

doi:10.1016/j.compchemeng.2017.01.007

Cited by 43 publications

(41 citation statements)

References 22 publications

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“…In many processes, such as polymerization reactions, or purification processes, it is not uncommon for the unit capacity to "degrade" after a task has been processed on that unit [76][77][78][79][80][81]. This can be, for example, due to residue formation (e.g., scaling) or impurity accumulation (e.g., membrane pore blockages).…”

Section: Unit Capacity Degradation and Maintenancementioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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Abstract:We present a generalized state-space model formulation particularly motivated by an online scheduling perspective, which allows modeling (1) task-delays and unit breakdowns; (2) fractional delays and unit downtimes, when using discrete-time grid; (3) variable batch-sizes; (4) robust scheduling through the use of conservative yield estimates and processing times; (5) feedback on task-yield estimates before the task finishes; (6) task termination during its execution; (7) post-production storage of material in unit; and (8) unit capacity degradation and maintenance. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings. Finally, we demonstrate the effectiveness of this model on a case study from the field of bio-manufacturing.

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Section: Unit Capacity Degradation and Maintenancementioning

confidence: 99%

A General State-Space Formulation for Online Scheduling

Gupta

Maravelias

2017

Processes

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“…In comparison to Biondi et al 18 , the residual lifetime constraints have been replaced with the degradation signal based health model developed above (Eqn. 9).…”

Section: Case Studymentioning

confidence: 99%

“…Fig. 5 shows three maintenance schedules for the original STN instance (P1) by Kondili et al 19 Week Figure 5: Comparison of maintenance schedules between deterministic solution (α = 0.5) and two robust solutions with different values of α (Instance P1 19 , average demand scenario by Biondi et al 18 ). The number of required maintenance actions increases with increasing uncertainty set size (decreasing α).…”

Section: Instancementioning

confidence: 99%

Data-Driven Optimization of Processes with Degrading Equipment

Wiebe

Cecı́lio

Misener

2018

Ind. Eng. Chem. Res.

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In chemical and manufacturing processes, unit failures due to equipment degradation can lead to process downtime and significant costs. In this context, finding an optimal maintenance strategy to ensure good unit health while avoiding excessive expensive maintenance activities is highly relevant. We propose a practical approach for the integrated optimization of production and maintenance capable of incorporating uncertain sensor data regarding equipment degradation. To this end, we integrate data-driven stochastic degradation models from Condition-based Maintenance into a process level mixed-integer optimization problem using Robust Optimization. We reduce computational expense by utilizing both analytical and data-based approximations and optimize the Robust Optimization parameters using Bayesian Optimization. We apply our framework to five instances of the State-Task-Network and demonstrate that it can efficiently compromise between equipment availability and cost of maintenance.

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“…One of the major criteria focused in the development of power system is the optimization of power plant scheduling. Production and maintenance scheduling is very important to run an effective operation process plants [19]. Improper and suboptimal maintenance schedule can increase the production costs and decreases the system reliability.…”

Section: Maintenance Optimizationmentioning

confidence: 99%

“…In other words, there is no guarantee that the maintenance system produced is a reliable and systematic thus an optimal solution is not achieved. The purpose of an optimized maintenance scheduling is to identify the method of scheduled outages of generating units over a given period such that the amount of energy supplied to the customers is maintained [19]. This type of schedule is important mainly in a maintenance activity because will directly affects other planning activities [20].…”

Section: Maintenance Optimizationmentioning

confidence: 99%

A Review on Power Plant Maintenance and Operational Performance

Velayutham

Ismail

2018

MATEC Web Conf.

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This paper presents the power plant maintenance scheduling and particle swarm optimization (PSO) technique to ensure economical and reliable operation of power system. Initially problems related to the power plant maintenance scheduling in modern power systems are briefed, also explaining the need and importance of an optimum and reliable power plant maintenance system. It briefly describes the maintenance scheduling of power plant by application of PSO technique. This paper proposes power plant maintenance scheduling of a power system based on minimization of the objective function considering the economical and reliable operation of a power system while satisfying the crew/manpower and the load demand.

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Optimization of multipurpose process plant operations: A multi-time-scale maintenance and production scheduling approach

Cited by 43 publications

References 22 publications

A General State-Space Formulation for Online Scheduling

A General State-Space Formulation for Online Scheduling

Data-Driven Optimization of Processes with Degrading Equipment

A Review on Power Plant Maintenance and Operational Performance

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