Challenges and problems with advanced control and optimization technologies

Campos, Mário Cesar Mello Massa de; Teixeira, Herbert; Liporace, Fábio S.; Gomes, Marcos V.C.

doi:10.3182/20090712-4-tr-2008.00003

Cited by 18 publications

(5 citation statements)

References 10 publications

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“…Control systems are often visualized as a pyramid [ 62 ] where higher levels mean a grouping of individual elements and a deeper of abstraction. Figure 1 represents current control, operation, and monitoring levels in a refinery.…”

Section: Current Operation In Refineriesmentioning

confidence: 99%

“…However, the data-driven approach is still not exploited in all its potential, and control models are built on PID controllers and multivariate optimizers [ 57 , 62 , 66 ] that establish the set-points of lower-level controllers by maximizing a cost function. The parameters of these cost functions are the ones that are established in the higher levels of the pyramid.…”

Section: Current Operation In Refineriesmentioning

confidence: 99%

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Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Olaizola

Quartulli

Unzueta

et al. 2022

Sensors

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Industry 4.0 concept has become a worldwide revolution that has been mainly led by the manufacturing sector. Continuous Process Industry is part of this global trend where there are aspects of the “fourth industrial revolution” that must be adapted to the particular context and needs of big continuous processes such as oil refineries that have evolved to control paradigms supported by sector-specific technologies where big volumes of operation-driven data are continuously captured from a plethora of sensors. The introduction of Artificial Intelligence techniques can overcome the current limitations of Advanced Control Systems (mainly MPCs) by providing better performance on highly non-linear and complex systems and by operating with a broader scope in terms of signals/data and sub-systems. Moreover, the state of the art of traditional PID/MPC based solutions is showing an asymptotic improvement that requires a disruptive approach in order to reach relevant improvements in terms of efficiency, optimization, maintenance, etc. This paper shows the key aspects in oil refineries to successfully adopt Big Data and Machine Learning solutions that can significantly improve the efficiency and competitiveness of continuous processes.

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Section: Current Operation In Refineriesmentioning

confidence: 99%

Section: Current Operation In Refineriesmentioning

confidence: 99%

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Olaizola

Quartulli

Unzueta

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Although the use of dynamic models for model adaptation and optimization may eliminate the requirements of steady state detection, solving a dynamic nonlinear optimization problem for large-scale systems may be challenging, even with today's computing power. The authors in [17] point that many numerical issues associated with DRTO must be addressed before it can be widely imple-250 mented in industrial applications. In addition, the dynamic model requires additional parameters including a model of the lower-layer control system.…”

Section: Dynamic Rto (Drto)mentioning

confidence: 99%

“…One of the main challenges with dynamic RTO, however, is computing power [17]. RTO often involves opti-525 mization of large-scale systems with large number of variables.…”

mentioning

confidence: 99%

Steady-state real-time optimization using transient measurements

Krishnamoorthy

Foss

Skogestad

2018

Computers & Chemical Engineering

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Real-time optimization (RTO) is an established technology, where the process economics are optimized using rigourous steady-state models. However, a fundamental limiting factor of current static RTO implementation is the steady-state wait time. We propose a "hybrid" approach where the model adaptation is done using dynamic models and transient measurements and the optimization is performed using static models. Using an oil production network optimization as case study, we show that the Hybrid RTO can provide similar performance to dynamic optimization in terms of convergence rate to the optimal point, at computation times similar to static RTO. The paper also provides some discussions on static versus dynamic optimization problem formulations.

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“…However, optimization of a large scale production network may be complex with several hundred decision variables. The computational cost of dynamic optimization problems has been a prohibitive factor for implementation in many oil and gas applications [7]. An oil and gas production network typically consists of several wells producing to a common production manifold, which takes the produced fluids to the processing facility.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Optimization Strategy for Large Scale Oil and Gas Production Systems

Krishnamoorthy

Aguiar

Foss

et al. 2018

2018 IEEE Conference on Control Technology and Applications (CCTA)

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In this paper, we consider the problem of realtime optimization of an oil and gas production network. The production network is often made of several wells from different reservoir sections producing to a common processing facility. In this work, we exploit this spatial structure of the system and cast the production optimization problem into a distributed optimization problem and solve it using Lagrangian decomposition. We extend previous work on decomposition for static real-time production optimization problem by considering a dynamic real-time optimization problem. We demonstrate the applicability of the proposed method using a gas-lifted well network with shared resources and common capacity constraints.

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Challenges and problems with advanced control and optimization technologies

Cited by 18 publications

References 10 publications

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Refinery 4.0, a Review of the Main Challenges of the Industry 4.0 Paradigm in Oil & Gas Downstream

Steady-state real-time optimization using transient measurements

A Distributed Optimization Strategy for Large Scale Oil and Gas Production Systems

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