Abstract
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.
Highlights
In batch plants, continuous plants, and general manufacturing plants with multiple processing units, multiple products or time-decaying performance, scheduling of production and maintenance is essential to ensure a feasible and economically profitable operation
The schedule variability penalty is divided into two independent terms: one for the changes in the allocation of tasks, Equation (15), which is related to the task allocation instability metric, and another for the changes in the starting time of the tasks, Equation (16), which is related to the task timing instability metric
These assumptions lead to a simpler problem than a real settings: for the control layer, a control horizon FPHC of 10 days and update intervals of one day; for the application of the online approach, while they allow isolating the analysis of the stability of the online scheduling layer, a scheduling horizon FPHS of 120 days, update intervals of 15 days, and 15 periods scheduling from other aspects
Summary
Continuous plants, and general manufacturing plants with multiple processing units, multiple products or time-decaying performance, scheduling of production and maintenance is essential to ensure a feasible and economically profitable operation. The previous survey indicated that schedule stabilityfor forthe online scheduling is still of anoperational open issue, aims of this paper are (i) to present a method online optimization and thereand is continuous no single, controls general under approach the trade-off between closed-loop schedules high that inputoptimizes and disturbance variability, while considering performance and schedule stability. Schedule stability explicitly in the closed loop, and (ii) to demonstrate its application and benefits for the online cleaning scheduling and flow distribution control of refinery preheat trains. The remainder of the paper is structured as follows: Section 2 briefly presents the modeling framework used to describe the dynamics of preheat trains under fouling and for online integration and optimization of the flow distribution and cleaning scheduling considering disturbances.
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