This paper focuses on the optimization of crude oil operations scheduling in a refinery that is supplied with crude oil by ship. One of the main challenges associated with the crude oil operations scheduling problem i...
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This paper focuses on the optimization of crude oil operations scheduling in a refinery that is supplied with crude oil by ship. One of the main challenges associated with the crude oil operations scheduling problem is the management of crude storage in tanks. Since storage capacity is limited and there are several types of crude oil depending on their composition, it is necessary to store mixtures of crude oil in tanks. This feature makes necessary the inclusion of nonlinear, non-convex constraints, which complicates the resolution of mathematical programming models. To address this problem, we have developed a mathematical programming model based on a continuous-time formulation using time slots, along with a strategy based on piecewise McCormick relaxation that allows us to efficiently handle the nonlinear constraints generated by blending crude oils in tanks.
This paper focuses on solving the optimization of crude oil operations scheduling carried out in a real system composed of a refinery and a marine terminal, over a monthly horizon. In the present article, we introduce...
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This paper focuses on solving the optimization of crude oil operations scheduling carried out in a real system composed of a refinery and a marine terminal, over a monthly horizon. In the present article, we introduce a large-scale mixed-integer non-linear programming (MINLP) model that faithfully represents the operation and characteristics of the system. Considering the model's complexity and its non-linear and non-convex nature, the challenge lies in solving the model in a time frame that meets the user's needs. To tackle this problem, we develop a temporal decomposition method in conjunction with a linear approximation.
The paper aims to review recent developments and points to the challenges and opportunities for the instrumentation, control and management technologies in relationship with the emergence of novel Energy Storage Syste...
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This paper addresses the problem of closed-loop operation of scheduling, together with the interaction of control and scheduling, for a class of processes that appear very often in industry: those that combine continu...
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This paper addresses the problem of closed-loop operation of scheduling, together with the interaction of control and scheduling, for a class of processes that appear very often in industry: those that combine continuous production lines with parallel batch units that share some resources. The paper presents a novel approach to this problem, including batching and a new type of precedence in the assignment problem. It also considers the effect of shared resources on the duration of the cycle time of the batch units. The approach is illustrated with a real-life example of a canned tuna factory
This paper deals with the scheduling of batch processes that share common resources having consumption profiles that change over time and are subjected to some global constraints. In order to approach the problem, the...
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This paper deals with the scheduling of batch processes that share common resources having consumption profiles that change over time and are subjected to some global constraints. In order to approach the problem, the formulation mixes three different time basis. Scheduling is performed in continuous time, but the description of the shared resources and the global constraints uses discrete values. The method is applied to a challenging problem in a tuna canning factory that mixes continuous flows of cans with the batch operation of a set of sterilizers sharing the steam supply line.
Real-Time Optimization (RTO) is not always able to achieve optimal process operation due to the presence of significant uncertainty about the plant models that are used to make decisions and also due to the difference...
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This paper presents a methodology for the optimal operation of mixed continuous-batch processes based on a hierarchical decomposition of the problem: local dynamic optimizers perform the economic optimization of the b...
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Real-Time Optimization (RTO) is not always able to achieve optimal process operation due to different reasons, among them the presence of significant uncertainty in the plant models that are used to make decisions or ...
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ISBN:
(纸本)9781509007561
Real-Time Optimization (RTO) is not always able to achieve optimal process operation due to different reasons, among them the presence of significant uncertainty in the plant models that are used to make decisions or the differences between control architecture layers which operate at different time-scales and use different kind of models. To overcome these issues the economic optimization problem, solved in the RTO layer, is modified following the Modifier Adaptation methodology (MA) to bring the process to the real optimum despite the presence of uncertainty by using plant measurements. This paper presents the implementation of MA methodology to the optimal management of distillation columns as a depropanizer column. Specifically, two approaches have been implemented starting from the traditional gradient-based technique called Dual Modifier Adaptation (DMA) to the recent approach Nested Modifier Adaptation (NMA), updating modifiers at the steady state of the process using static information. The RTO layer is based on a really simplified static model of the process which implies the presence of strong structural plant-model mismatch.
A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need...
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A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust controller synthesis.
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