Data-driven self-optimization of processes in the presence of the model-plant mismatch

Memarian, Alireza; Varanasi, Santhosh Kumar; Huang, Biao

doi:10.1016/j.ifacol.2022.07.498

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In process industries, virtually one-third of the control loops experience oscillations, hence delivering unsatisfactory performance. Oscillations originated in one control loop can propagate to other control loops through interactions between the control loops. – An oscillating control loop cannot maintain process variables of interest at their desired values, leading to excessive consumption of energy and raw materials, curtailment in product quality, increased production cost, etc. – The oscillations are often the outcomes of poorly tuned controllers, control valve faults, sensor faults, or equipment faults. – The identification and localization of the source of oscillations are the first and foremost step in oscillation removal. This task can be performed by the manual inspection of each control loop.…”

Section: Introductionmentioning

confidence: 99%

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Memarian,

Damarla,

Huang

et al. 2024

Ind. Eng. Chem. Res.

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Oscillation in control loops is a frequent problem in the process industries. These oscillations directly impact product quality, leading to a decreased plant profit. Additionally, oscillations increase energy consumption and waste raw materials and pose a significant restriction on the performance of the operational unit. Therefore, it is essential to isolate the loops that exhibit oscillations. In this work, two practical and effective methods are proposed to detect oscillations in process control loops. Both methods are aimed at detecting the presence of a triangle-like shape in the "D vs process variable (PV)" [or "D vs controller output (OP)"] plot to identify oscillations in the control loops. Here, "D" represents the Euclidean distance, which involves the deviations from the mean values of the variables OP and PV. Method 1 accomplishes this objective in an unsupervised manner by fitting a nonlinear algebraic function to the data: "D" and "PV". Method 2 uses a deep convolutional neural network for detecting the triangle-like shape. The performance of both the methods was evaluated by applying them to benchmark control loops sourced from various industries, including chemical, paper, mining, and metal industries, along with control loops in a local refinery unit. While both methods have their own advantages and application scenarios, the results demonstrated that both the proposed methods identified oscillatory control loops for the majority of the cases studied.

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Section: Introductionmentioning

confidence: 99%

Shape-Based Pattern Recognition Approaches toward Oscillation Detection

Memarian,

Damarla,

Huang

et al. 2024

Ind. Eng. Chem. Res.

Self Cite

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“…Satisfactorily performing control loops aids in meeting production targets, adhering to product quality specifications, and obeying environmental regulations. [1,2] Practical issues such as control valve faults, abnormally behaving sensors, interactions between control loops, and upstream or downstream upsets have the potential to create oscillations in the control loops, making it difficult to accomplish the above-mentioned objectives. Therefore, it is of vital importance to detect the oscillations and their root causes in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Control valve stiction detection using Markov transition field and deep convolutional neural network

Memarian,

Damarla,

Huang

2023

Can J Chem Eng

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Control valve stiction is an industrial problem that often causes oscillations in process control loops. Oscillating control loops are not capable of maintaining key process variables near or at their desired values, thus yielding low‐quality products, inducing economic loss, and increasing environmental impacts. Therefore, it is of vital importance to detect stiction in industrial control valves. In this regard, the present work proposes a new method based on the Markov transition field and convolutional neural network (CNN) to identify sticky control valves in industrial control loops. The Markov transition field is employed to convert process variable (PV) and controller output (OP) into two‐dimensional images, which are then utilized by CNN to learn to distinguish stiction induced oscillations from oscillations brought out by a non‐stiction condition. A transfer learning strategy is adopted to improve the stiction detection capability of the proposed method. Its performance is evaluated via its application to benchmark control loops taken from the chemical, paper, mining, and metal industries. Results demonstrate that the proposed method obtains the correct verdict for the majority of the control loops studied.

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