A Real-Time Optimization Strategy for Small-Scale Facilities and Implementation in a Gas Processing Unit

Pedro A Delou,Leonardo D Ribeiro,Argimiro R Secchi,Jacques Niederberger,Marcos Vinícius C Gomes,Carlos R Paiva

doi:10.3390/pr9071179

Pedro A Delou, Leonardo D Ribeiro + Show 4 more

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https://doi.org/10.3390/pr9071179

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Abstract

The rise of new digital technologies and their applications in several areas pushes the process industry to update its methodologies with more intensive use of mathematical models—commonly denoted as digital twins—and artificial intelligence (AI) approaches to continuously enhance operational efficiency. In this context, Real-time Optimization (RTO) is a strategy that is able to maximize an economic function while respecting the existing constraints, which enables keeping the operation at its optimum point even though the plant is subjected to nonlinear behavior and frequent disturbances. However, the investment related to the project of commercial RTOs may make its application infeasible for small-scale facilities. In this work, an in-house, small-scale RTO is presented and its successful application in a real industrial case—a Natural Gas Processing Unit—is shown. Besides that, a new method for enhancing the efficiency of using sequential-modular simulator inside an optimization framework and a new method to account for the economic return of optimization-based tools are proposed and described. The application of RTO in the industrial case showed an enhancement in the stability of the main variables and an increase in profit of 0.64% when compared to the operation of the regulatory control layer alone.

Highlights

Real-time optimization is a model-based adaptive optimization technique that attempts to find the optimal operating condition to an economic index of a plant subjected to a process model and a set of constraints that might be, for instance, physical limits, environmental restriction, product quality, or safety criteria [1]
The present paper describes the development of an Real-time Optimization (RTO) strategy for small-scale applications and its implementation to a Debutanizer section of a Natural Gas Processing Unit
The whole methodology is disclosed, including a proposition of a new modeling method for enhancing the effectiveness of the use of sequential-modular models inside an optimization framework and a new method to account for the economic benefit of applications based on optimization frameworks after a period of operation

Summary

Introduction

Real-time optimization is a model-based adaptive optimization technique that attempts to find the optimal operating condition to an economic index of a plant subjected to a process model and a set of constraints that might be, for instance, physical limits, environmental restriction, product quality, or safety criteria [1]. The so-called “twostep” approach proposed by Jang et al [2] has become the most widespread RTO strategies in industry [3,4,5]. In this approach, a parameter estimation step is performed followed by an economic optimization step, so that the available static model of the plant can be adjusted considering the most recent set of plant information and the optimization may be carried out considering a rigorous model with minimum plant-model mismatch. The development of a rigorous process model is the backbone of the two-step RTO approach and, it is possible to show that whenever a model satisfies the set of model adequacy criteria, the model-based optimization problem is able to drive the plant to its true optimum [9,10,11]

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