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23 pages, 8814 KiB

Open AccessArticle

Machine Learning-Based Prediction of Controlled Variables of APC Systems Using Time-Series Data in the Petrochemical Industry

by Minyeob Lee, Yoseb Yu, Yewon Cheon, Seungyun Baek, Youngmin Kim, Kyungmin Kim, Heechan Jung, Dohyeon Lim, Hyogeun Byun, Chaekyu Lee and Jongpil Jeong

Processes 2023, 11(7), 2091; https://doi.org/10.3390/pr11072091 - 13 Jul 2023

Cited by 2 | Viewed by 2113

Abstract

For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term [...] Read more.

For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term forecasting with small data, where many systems are being applied to improve production efficiency. The problem considered in this study is the problem of predicting time-series Controlled Variables (CV) with machine learning, which is necessary to utilize an Advanced Process Control (APC) system in a petrochemical plant. In an APC system, the most important aspect is the prediction of the controlled variables and how the predicted values of the controlled variables should be modified to be in the user’s desired range. In this study, we focused on predicting the controlled variables. Specifically, we utilized various machine learning techniques to predict future controlled variables based on past controlled variables, Manipulated Variables (MV), and Disturbance Variables (DV). By using a time delay as a parameter and adjusting its value, you can analyze the relationship between past and future data and improve forecasting performance. Currently, the APC system is controlled through mathematical modeling and research, The time-series data of controlled variables, manipulated variables, and disturbance variables are predicted through machine learning models to compare performance and measure accuracy. It is becoming important to change from mathematical prediction models to data-based machine learning predictions. The R-Squared (R²) and Mean Absolute Percentage Error (MAPE) metric results of this study demonstrate the feasibility of introducing an APC system using machine learning models in petrochemical plants. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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16 pages, 5305 KiB

Open AccessArticle

Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends

by Esteban Lopez-Ramirez, Sandra Lopez-Zamora, Salvador Escobedo and Hugo de Lasa

Processes 2023, 11(7), 2026; https://doi.org/10.3390/pr11072026 - 6 Jul 2023

Cited by 2 | Viewed by 1143

Abstract

Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be [...] Read more.

Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R² Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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14 pages, 2564 KiB

Open AccessArticle

Using ANN and Combined Capacitive Sensors to Predict the Void Fraction for a Two-Phase Homogeneous Fluid Independent of the Liquid Phase Type

by Tzu-Chia Chen, Seyed Mehdi Alizadeh, Abdullah K. Alanazi, John William Grimaldo Guerrero, Hala M. Abo-Dief, Ehsan Eftekhari-Zadeh and Farhad Fouladinia

Processes 2023, 11(3), 940; https://doi.org/10.3390/pr11030940 - 20 Mar 2023

Cited by 8 | Viewed by 1560

Abstract

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the [...] Read more.

Measuring the void fraction of different multiphase flows in various fields such as gas, oil, chemical, and petrochemical industries is very important. Various methods exist for this purpose. Among these methods, the capacitive sensor has been widely used. The thing that affects the performance of capacitance sensors is fluid properties. For instance, density, pressure, and temperature can cause vast errors in the measurement of the void fraction. A routine calibration, which is very grueling, is one approach to tackling this issue. In the present investigation, an artificial neural network (ANN) was modeled to measure the gas percentage of a two-phase flow regardless of the liquid phase type and changes, without having to recalibrate. For this goal, a new combined capacitance-based sensor was designed. This combined sensor was simulated with COMSOL Multiphysics software. Five different liquids were simulated: oil, gasoil, gasoline, crude oil, and water. To estimate the gas percentage of a homogeneous two-phase fluid with a distinct type of liquid, data obtained from COMSOL Multiphysics were used as input to train a multilayer perceptron network (MLP). The proposed neural network was modeled in MATLAB software. Using the new and accurate metering system, the proposed MLP model could predict the void fraction with a mean absolute error (MAE) of 4.919. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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16 pages, 3848 KiB

Open AccessArticle

Introducing the Effective Features Using the Particle Swarm Optimization Algorithm to Increase Accuracy in Determining the Volume Percentages of Three-Phase Flows

by Tzu-Chia Chen, Seyed Mehdi Alizadeh, Marwan Ali Albahar, Mohammed Thanoon, Abdullah Alammari, John William Grimaldo Guerrero, Ehsan Nazemi and Ehsan Eftekhari-Zadeh

Processes 2023, 11(1), 236; https://doi.org/10.3390/pr11010236 - 11 Jan 2023

Cited by 2 | Viewed by 1724

Abstract

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A [...] Read more.

What is presented in this research is an intelligent system for detecting the volume percentage of three-phase fluids passing through oil pipes. The structure of the detection system consists of an X-ray tube, a Pyrex galss pipe, and two sodium iodide detectors. A three-phase fluid of water, gas, and oil has been simulated inside the pipe in two flow regimes, annular and stratified. Different volume percentages from 10 to 80% are considered for each phase. After producing and emitting X-rays from the source and passing through the pipe containing a three-phase fluid, the intensity of photons is recorded by two detectors. The simulation is introduced by a Monte Carlo N-Particle (MCNP) code. After the implementation of all flow regimes in different volume percentages, the signals recorded by the detectors were recorded and labeled. Three frequency characteristics and five wavelet transform characteristics were extracted from the received signals of each detector, which were collected in a total of 16 characteristics from each test. The feature selection system based on the particle swarm optimization (PSO) algorithm was applied to determine the best combination of extracted features. The result was the introduction of seven features as the best features to determine volume percentages. The introduced characteristics were considered as the input of a Multilayer Perceptron (MLP) neural network, whose structure had seven input neurons (selected characteristics) and two output neurons (volume percentage of gas and water). The highest error obtained in determining volume percentages was equal to 0.13 as MSE, a low error compared with previous works. Using the PSO algorithm to select the most optimal features, the current research’s accuracy in determining volume percentages has significantly increased. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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17 pages, 1961 KiB

Open AccessArticle

An Improved Line-Up Competition Algorithm for Unrelated Parallel Machine Scheduling with Setup Times

by Yuting Xu and Bin Shi

Processes 2022, 10(12), 2676; https://doi.org/10.3390/pr10122676 - 12 Dec 2022

Viewed by 1228

Abstract

It is well known that with the development of economic globalization and increasing competition in the market, enterprises are facing a huge challenge in the unrelated parallel machine scheduling problem with setup time (UPMST). Determining the processing order of all jobs and assigning [...] Read more.

It is well known that with the development of economic globalization and increasing competition in the market, enterprises are facing a huge challenge in the unrelated parallel machine scheduling problem with setup time (UPMST). Determining the processing order of all jobs and assigning machines to production scheduling has become more complex and has research implications. Moreover, a reasonable production scheduling scheme can not only complete the production plan efficiently but also contribute to reducing carbon emissions. In this paper, a mathematical model with the goal of the shortest completion time is studied for the UPMST problem. An improved line-up competition algorithm (ILCA) is proposed to solve this model, and the search accuracy and rate of the algorithm are improved by the newly proposed heuristic workpiece allocation rules and variation strategies. From the perspective of evaluation purposes, the effectiveness and stability of the method are significantly superior to other methods, and it is competitive in solving the UPMST problem. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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18 pages, 6108 KiB

Open AccessFeature PaperArticle

Predicting Enthalpy of Combustion Using Machine Learning

by Abdul Gani Abdul Jameel, Ali Al-Muslem, Nabeel Ahmad, Awad B. S. Alquaity, Umer Zahid and Usama Ahmed

Processes 2022, 10(11), 2384; https://doi.org/10.3390/pr10112384 - 14 Nov 2022

Cited by 5 | Viewed by 3551

Abstract

The present work discusses the development and application of a machine-learning-based model to predict the enthalpy of combustion of various oxygenated fuels of interest. A detailed dataset containing 207 pure compounds and 38 surrogate fuels has been prepared, representing various chemical classes, namely [...] Read more.

The present work discusses the development and application of a machine-learning-based model to predict the enthalpy of combustion of various oxygenated fuels of interest. A detailed dataset containing 207 pure compounds and 38 surrogate fuels has been prepared, representing various chemical classes, namely paraffins, olefins, naphthenes, aromatics, alcohols, ethers, ketones, and aldehydes. The dataset was subsequently used for constructing an artificial neural network (ANN) model with 14 input layers, 26 hidden layers, and 1 output layer for predicting the enthalpy of combustion for various oxygenated fuels. The ANN model was trained using the collected dataset, validated, and finally tested to verify its accuracy in predicting the enthalpy of combustion. The results for various oxygenated fuels are discussed, especially in terms of the influence of different functional groups in shaping the enthalpy of combustion values. In predicting the enthalpy of combustion, 96.3% accuracy was achieved using the ANN model. The developed model can be successfully employed to predict the enthalpies of neat compounds and mixtures as the obtained percentage error of 4.2 is within the vicinity of experimental uncertainty. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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10 pages, 1570 KiB

Open AccessArticle

Enhanced Multiphase Flow Measurement Using Dual Non-Intrusive Techniques and ANN Model for Void Fraction Determination

by Shivan Mohammed, Lokman Abdulkareem, Gholam Hossein Roshani, Ehsan Eftekhari-Zadeh and Ezadin Haso

Processes 2022, 10(11), 2371; https://doi.org/10.3390/pr10112371 - 11 Nov 2022

Cited by 3 | Viewed by 1814

Abstract

There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more [...] Read more.

There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases’ flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques’ data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques’ data to obtain more precise results than they can individually. The simulations and analyzing procedures were performed using MATLAB. The model shows that using gamma-ray and capacitance-based sensors gives Mean Absolute Errors (MAE) of 3.8% and 2.6%, respectively, while using both techniques gives a lower MAE that is nearly 1%. Consequently, measurements using two techniques have the ability to enhance the multiphase flows’ observation with more accurate features. Such a hybrid measurement system is proposed to be a forward step toward an adaptive observation system within related applications of multiphase flows. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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Review

Jump to: Research

25 pages, 2548 KiB

Open AccessReview

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

by Yasunari Matsuzaka and Yoshihiro Uesawa

Processes 2023, 11(4), 1296; https://doi.org/10.3390/pr11041296 - 21 Apr 2023

Cited by 1 | Viewed by 2160

Abstract

In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR [...] Read more.

In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR applications for in silico toxicity prediction have been studied for a long time. However, in recent years, the remarkable predictive performance of deep learning has attracted attention for practical applications. In this review, we summarize the application of deep learning to QSAR for constructing prediction models, including a discussion of parameter optimization for deep learning. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Chemical Science and Engineering)

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