Controller Design and Control Structure Analysis for a Novel Oil–Water Multi-Pipe Separator

Ohrem, Sveinung Johan; Skjefstad, Håvard Slettahjell; Stanko, Milan; Holden, Christian

doi:10.3390/pr7040190

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Furthermore, if the correlation between the training dataset and the testing dataset was weak, a new ANN architecture was used by adding nodes to the hidden layer or by increasing the number of hidden layers. The ANN model produced in the previous step was used to design the adaptive control system of the extraction machine [30].…”

Section: Machine Learning Model and Control System Designmentioning

confidence: 99%

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Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

et al. 2022

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Kecombrang flowers have bioactive components that can be used as food additives. The development of the kecombrang functional food industry for the production of food additives requires information on production parameters. The extraction process for kecombrang to obtain bioactive components, especially phenols and flavonoids, requires maximum temperature treatment and extraction time. This study aims to determine the standard for the kecombrang flower extraction process, create a machine learning model to estimate the quality parameters of the extraction results (phenol, flavonoid, pH, color, and viscosity), and design a strategy for controlling the extraction machine work to maintain the quality of the extraction, especially of phenols and flavonoids. This research was conducted at extraction temperatures of 60 °C, 65 °C, 70 °C, and 75 °C. During the extraction process, the quality of the material was checked by measuring phenol and flavonoid contents, as well as color, pH, and viscosity. Sampling was carried out at 5 min intervals. The data on the quality parameters during the extraction process were analyzed for trends. A machine learning model, which is an artificial neural network, was developed using a 2–6–1 architecture for each quality parameter. The two inputs of ANN were temperature of extraction and extraction time (duration). The output was the quality parameters of the products (phenols, flavonoids, pH, viscosity, and color), which were evaluated separately. The results show a good correlation between the model and the experimental data, with both the training dataset and the testing dataset. These results were then used to formulate a strategy for controlling the extraction process. A neuro-control system was used as a strategy. This control system was adaptive to changes that occurred during the extraction process so that phenols and flavonoids could be maintained.

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Section: Machine Learning Model and Control System Designmentioning

confidence: 99%

“…Data on the dynamics of phenol, flavonoid, pH, viscosity, and color at various temperatures (60 °C, 65 °C, 70 °C, and 75 °C) during the extraction process are described below. The ANN model produced in the previous step was used to design the adaptive control system of the extraction machine [30].…”

Section: Quality Parametersmentioning

confidence: 99%

Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

et al. 2022

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“…In the paper authored by Ohrem et al [19], control structure analysis and controller design were addressed for a novel multipipe separator, in order to enhance efficient production of hydrocarbons on the seabed in waters. PI controllers and model reference adaptive controllers were designed for different control loops.…”

Section: Control Applications For Complex Industrial Systemsmentioning

confidence: 99%

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Chen

Zhang

2021

Processes

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Experimental quantification of the performance of a horizontal multi-pipe bulk separator of water and oil with crude spiking

Asaadian

Stanko

2023

J Petrol Explor Prod Technol

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Subsea water separation with pipe separators is crucial for ensuring efficient and environmentally responsible extraction of oil and gas from the seabed. In this study, in a process called as “crude oil spiking,” two concentrations of crude oil (e.g., 185 and 400 ppm) are added to Exxsol D60 to mimic the separation characteristics of real crude oil mixtures in a multi-parallel pipe separator. The pipe separator performance for water–oil bulk separation such as separation efficiency, water cut ratio, the flow pattern at the separator inlet, and the thickness and evolution of the fluid layers in the separator is evaluated and compared to the values when operating with unspiked Exxsol D60. Crude oil spiking significantly reduces the efficiency of the pipe separator and reduces the water cut ratio for oil continuous regimes (low water cuts) up to 49%. Water continuous regimes with water fractions 90% have the highest efficiency values; thus, these are not affected significantly by crude oil spiking. With crude spiking, the flow regime dispersion of oil in water and water in oil (Dw/o + Do/w) occupies more area in the flow pattern map than unspiked Exxsol D60. It was observed through visual inspection that crude oil spiking induces a thicker and more stable emulsion in higher flow rates (e.g., 700 L/min). Therefore, the spiked mixture needs more time to separate. The findings of this study can help in a better understanding of the applicability of pipe separators and the usage of spiked oils to extrapolate experimental results to real field conditions. Graphical abstract

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Controller Design and Control Structure Analysis for a Novel Oil–Water Multi-Pipe Separator

Cited by 4 publications

References 18 publications

Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

Machine Learning Model for Quality Parameters Prediction and Control System Design in the Kecombrang Flower (Etlingera elatior) Extraction Process

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

Experimental quantification of the performance of a horizontal multi-pipe bulk separator of water and oil with crude spiking

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