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

Delou, Pedro de Azevedo; Ribeiro, Leonardo D.; Paiva, Carlos R.; Niederberger, Jacques; Gomes, Marcos V.C.; Secchi, Argimiro Resende

doi:10.3390/pr9071179

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…Process optimization requires the following steps: (1) data acquisition, (2) data treatment, (3) steady‐state detection, (4) data reconciliation, (5) optimization, and (6) solution validation. [ 20 ] In the data acquisition stage, the user specifies whether the algorithm is going to run off‐line, that is, independently of the actual state of the plant, or on‐line, that is, reading the values directly from the process database. In the second step, each input variable may undergo a linear transformation to scale or to perform unit change and excludes any unwanted or out‐of‐limit values.…”

Section: Theorymentioning

confidence: 99%

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Experimental methods in chemical engineering: Data processing and data usage in decision‐making

Thibault

Chioua

McKay³

et al. 2023

Can J Chem Eng

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Industrial facilities collect large volumes of data, store them according to prescribed protocols, and then interpret them for process decision‐making. Several sources and types of error contaminate these data for various reasons, but especially because they come from unreliable or unpredictable instruments. Data (or signal) processing corrects measurement errors to improve fidelity. Here, we highlight decision‐making applications and signal processing methods. To fully appreciate the state‐of‐the‐art, we interviewed plant data experts and software developers in the pulp and paper industry to examine how they apply signal processing methods in the context of decision‐making, including the value of process data, how these data are used, and the major barriers that prevent plants from using data. Process experts clean data thoroughly with basic approaches compared to the advanced techniques available in the recent literature. The interviews demonstrate that decisions in industry are primarily based on steady‐state process operating data. Challenges and barriers that prevent the use of process data to their full potential relate to resource limitations (people, time, and money), an entrenched culture, and access to recent technology. In practice, experts consider, implicitly or explicitly, data that represent the process operating under steady‐state conditions. A plant model that represents steady‐state operations is easier to interpret, is presented in a form that is usable by plant operators, and in this way, better enables decision‐making.

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Section: Theorymentioning

confidence: 99%

“…Regarding optimization, the literature [18,20,21] suggests reducing measurement noise, performing steady-state detection, and data reconciliation as data processing steps. The industry has applied bound checking and conditional filters and has performed data cleaning to remove null values and errors.…”

Section: Data Cleaningmentioning

confidence: 99%

Experimental methods in chemical engineering: Data processing and data usage in decision‐making

Thibault

Chioua

McKay³

et al. 2023

Can J Chem Eng

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“…[11] detailed the necessary steps to preprocess data before utilizing them in r optimization for implementation. Moreover, [12] proposed a sequence of data pr steps that lead to process optimization. For instance, they put forward that the steps that need to be followed include: (1) data acquisition, (2) data treatment, (3 state detection, (4) data reconciliation, (5) optimization, and (6) solution validati ing the data acquisition stage, the user specifies whether the algorithm will operat independent of the plant's actual state, or online, where values are directly retriev the process database.…”

Section: Review Of Data Processing Framework For Industrial Decision ...mentioning

confidence: 99%

“…Moreover, Ref. [12] proposed a sequence of data processing steps that lead to process optimization. For instance, they put forward that the essential steps that need to be followed include: (1) data acquisition, (2) data treatment, (3) steady-state detection, (4) data reconciliation, (5) optimization, and (6) solution validation.…”

Section: Review Of Data Processing Framework For Industrial Decision ...mentioning

confidence: 99%

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Thibault

Kelly²,

Désilets

et al. 2023

Processes

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Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operating periods detection, (4) data reconciliation and (5) operating regime detection and identification. The proposed framework is applied to the brownstock washing department of a dissolving pulp mill. Over a 5-month period, the process was found to be in steady-state 32% of the time. Twenty (20) distinct operating regimes were identified. Further processing with the help of data reconciliation techniques, principal component analysis and k-means clustering showed that the main drivers explaining the operating regimes are the pulp level in tanks, its density, and the shower wash water flow rate. Additionally, it was concluded that the top four persistently problematic sensors across the steady-state spans that would need to be verified are three flow meters (06FIC137, 06FIC152, and 06FIC433), and one consistency sensor (06NIC423). This information was relayed to process experts contacts at the plant for further investigation.

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“…O principal desafio neste tipo de estratégia é obter um modelo dinâmico preciso. Delou et al (2021) propuseram o uso de uma estrutura de modelagem Hammerstein para representar a dinâmica do sistema. Essa estrutura aproveita do modelo em estado estacionário e adiciona dinâmicas por meio de um modelo linear autoregressivo (linear autoregressive model -ARX) obtido a partir de dados da planta.…”

Section: Objetivosunclassified

Otimização em tempo real aplicada em um sistema experimental de Gas Lift.

Oliveira¹

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Real-time Optimization (RTO) is a production optimization technique that aims at improving plant economic performance in real-time. Many proposals of this technique have been made, being the two step RTO known as model parameter adaptation (MPA) the most traditional. Although being widely used in industrial application, the MPA uses stationary process information to perform the optimization and is not appropriated for dynamic systems. Recently a real time optimization with persistent adaptation (ROPA) has been proposed as a new RTO approach to handle and optimize dynamic systems. With a hybrid approach that uses stationary and transient information this method has shown promising results, but more studies are still necessary to support those results. The absence of a real system application, only implementation in simulated environments, and the lack of studies in different approaches regarding this method are some of the issues that still needs to be discussed. To contribute to the development of ROPA, this work presents an implementation of ROPA on an experimental rig that emulates a subsea oil extraction by Gas Lift. This experiment will evaluate ROPA performance in an real environment and compare the results with MPA. In order to apply ROPA two different online estimators are selected, the extended Kalman filter (EKF) and the moving horizon estimation (MHE). This work begins by modelling the experiment, since MPA and ROPA are model based, then MPA and ROPA-EKF are implemented and compared. Finally, ROPA-MHE is implemented and compared with ROPA-EKF. In order to implement ROPA-MHE a study on the arrival cost, a penalty term of MHE, was performed and it was verified the importance of this term on obtaining accurate estimations. The results obtained in this work have shown that ROPA-EKF had a better performance than MPA, specially in transient states, and ROPA-EKF and ROPA-MHE had similar performance.

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

Cited by 5 publications

References 49 publications

Experimental methods in chemical engineering: Data processing and data usage in decision‐making

Experimental methods in chemical engineering: Data processing and data usage in decision‐making

Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development

Otimização em tempo real aplicada em um sistema experimental de Gas Lift.

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