A Review of Optimal Scheduling Cleaning of Refinery Crude Preheat Trains Subject to Fouling and Ageing

Diaby, Abdullatif Lacina; Luong, Lee; Amer, Yousef; Addai‐Mensah, Jonas

doi:10.4028/www.scientific.net/amm.148-149.643

Cited by 7 publications

(14 citation statements)

References 40 publications

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“…The use of optimization to assist in the complicated task of scheduling cleaning activities in PHTs (which units to clean and when) has attracted the attention of many researchers over the past years . The decision is ultimately based on an economic trade‐off between (mainly) the energy losses due to fouling, the cost of cleaning, and loss of production, and on the other hand increased throughput and energy recovery after a cleaning.…”

Section: Introductionmentioning

confidence: 99%

A new dynamic model of crude oil fouling deposits and its application to the simulation of fouling‐cleaning cycles

2015

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Modelling of crude oil fouling in heat exchangers has been traditionally limited to a description of the deposit as a thermal resistance. However, consideration of the local change in thickness and the evolution of the properties of the deposit due to ageing or changes in foulant composition is important to capture the thermal and hydraulic impact of fouling. A dynamic, distributed, first-principles model of the deposit is

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Section: Introductionmentioning

confidence: 99%

A new dynamic model of crude oil fouling deposits and its application to the simulation of fouling‐cleaning cycles

2015

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“…Equality constraints are introduced to fix the selected cleaning actions in the current schedule to the values calculated in the previous one. Equation (13) shows this constraint for the binary decisions, where the asterisk denotes the optimal value in the previous schedule. These equality constraints assign the cleanings to the units and periods; however, because the periods have variable length (with a continuous time representation), the starting time of the cleanings is not fixed.…”

Section: Freezing Decisionsmentioning

confidence: 99%

“…Usually, the cleaning scheduling and the flow distribution problems of preheat trains have been considered independently, ignoring the inherent variability of the process, and solved using heuristics [13][14][15]. This leads to suboptimal operations because key elements of the problem are ignored, or to infeasible operations because operating limits (e.g., the firing limit of the furnace, the limit capacity of the pumps) are reached, causing a need for emergency cleaning actions or a reduction in production rates.…”

Section: Introductionmentioning

confidence: 99%

Stability of Optimal Closed-Loop Cleaning Scheduling and Control with Application to Heat Exchanger Networks under Fouling

Santamaria

Macchietto

2020

Processes

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Heat exchanger networks subject to fouling are an important example of dynamic systems where performance deteriorates over time. To mitigate fouling and recover performance, cleanings of the exchangers are scheduled and control actions applied. Because of inaccuracy in the models, as well as uncertainty and variability in the operations, both schedule and controls often have to be revised to improve operations or just to ensure feasibility. A closed-loop nonlinear model predictive control (NMPC) approach had been previously developed to simultaneously optimize the cleaning schedule and the flow distribution for refinery preheat trains under fouling, considering their variability. However, the closed-loop scheduling stability of the scheme has not been analyzed. For practical closed-loop (online) scheduling applications, a balance is usually desired between reactivity (ensuring a rapid response to changes in conditions) and stability (avoiding too many large or frequent schedule changes). In this paper, metrics to quantify closed-loop scheduling stability (e.g., changes in task allocation or starting time) are developed and then included in the online optimization procedure. Three alternative formulations to directly include stability considerations in the closed-loop optimization are proposed and applied to two case studies, an illustrative one and an industrial one based on a refinery preheat train. Results demonstrate the applicability of the stability metrics developed and the ability of the closed-loop optimization to exploit trade-offs between stability and performance. For the heat exchanger networks under fouling considered, it is shown that the approach proposed can improve closed-loop schedule stability without significantly compromising the operating cost. The approach presented offers the blueprint for a more general application to closed-loop, model-based optimization of scheduling and control in other processes.

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“…Fouling is the deposition of unwanted material over the surface of process equipment, which reduces the performance of heat transfer operations. It is a phenomena not yet fully understood, but it is known that it can be caused by the presence of impurities in the process streams, the crystallization of salts, biological reactions, deposition of suspended particles, thermal decomposition of certain components, and corrosion 1,2 . The causes of fouling vary among different processes.…”

Section: Introductionmentioning

confidence: 99%

Integration of Optimal Cleaning Scheduling and Control of Heat Exchanger Networks Undergoing Fouling: Model and Formulation

Santamaria

Macchietto

2018

Ind. Eng. Chem. Res.

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The performance and operability of heat exchanger networks (HENs) is strongly affected by fouling, which involves the deposition of unwanted material, which reduces the heat-transfer rate and increases the pressure drop, the operational costs, and the environmental impact of the process. Periodical cleaning and control of the flow rate distribution in the HEN are used to mitigate the effects of fouling and restore the performance of the units. The optimal cleaning scheduling has been formulated as a mixed-integer linear programming (MILP) or mixed-integer nonlinear programming (MINLP) problem and is solved using various approaches. The optimal control has been formulated as a nonlinear programming (NLP) problem and is used to define the flow rate distribution of the network. Both problems share the same objective: minimization of the total cost of the operation. In principle, the simultaneous solution of the optimal control problem and the optimal cleaning scheduling problem should provide greater savings than the independent or sequential solution of the two problems, since the interactions of the two mitigation alternatives are considered. However, these two problems have been typically considered separately, because of modeling and solution challenges. Also, it is not quite clear what additional benefit a simultaneous solution may bring. The challenges for solving the integrated problem are the large scale of the associated optimization problem and the different time scales involved in each operational layer. Here, a general and efficient formulation is proposed, using a continuous time discretization scheme for the integrated problem of scheduling and control of HENs subject to fouling. A dynamic model of the heat exchangers is proposed that is sufficiently detailed to represent the physics of interest with novel modifications to address simultaneously their control and scheduling in a network. The problem is formulated as a MINLP and solved using deterministic optimization algorithms. The flexibility of the model and variations of the formulation are demonstrated with two small case studies. The formulation complexity versus scale and advantages are analyzed. The results show that considering the two problems simultaneously has a very strong synergistic effect, with over a 20% decrease in operational cost achieved, in comparison to using either fouling mitigation alternative individually.

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A Review of Optimal Scheduling Cleaning of Refinery Crude Preheat Trains Subject to Fouling and Ageing

Cited by 7 publications

References 40 publications

A new dynamic model of crude oil fouling deposits and its application to the simulation of fouling‐cleaning cycles

A new dynamic model of crude oil fouling deposits and its application to the simulation of fouling‐cleaning cycles

Stability of Optimal Closed-Loop Cleaning Scheduling and Control with Application to Heat Exchanger Networks under Fouling

Integration of Optimal Cleaning Scheduling and Control of Heat Exchanger Networks Undergoing Fouling: Model and Formulation

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