Application of Artificial Intelligence (AI) in Chemical Science and Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 15731

Special Issue Editor


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Guest Editor
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
Interests: computational fluid dynamics; crystallization; microfluidics; AI in chemical engineering; process system engineering

Special Issue Information

Dear Colleagues,

Chemical science and engineering have been significantly influenced by artificial intelligence (AI), ranging from molecular dynamics to chemical process design. Machine learning (ML), a part of AI technology, can automatically analyze massive amounts of data from experiments and numerical simulations to find hidden principles and relationships. This data-driven technology has become an effective and irreplaceable tool for chemists and chemical engineers. This Special Issue will collect high-quality research and review papers on “Application of Artificial Intelligence (AI) in Chemical Science and Engineering”. Topics include, but not are limited to: the combination of deep learning with molecular dynamics and catalyst design; novel machine learning algorithms for chemical safety and chemical process control; the application of AI in the design and optimization of chemical unit equipment; AI in crystallization and other unit operations; and AI in other fields of chemical science and engineering.

Prof. Dr. Jingtao Wang
Guest Editor

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • molecular dynamics
  • chemical safety
  • process control
  • catalyst design
  • computational fluid dynamics
  • process system engineering
  • crystallization

Published Papers (8 papers)

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Research

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23 pages, 8814 KiB  
Article
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 1990
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
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16 pages, 5305 KiB  
Article
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 1084
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
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14 pages, 2564 KiB  
Article
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 1491
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
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16 pages, 3848 KiB  
Article
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 1681
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
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17 pages, 1961 KiB  
Article
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 1179
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
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18 pages, 6108 KiB  
Article
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 6 | Viewed by 3448
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
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10 pages, 1570 KiB  
Article
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 1731
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
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Review

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25 pages, 2548 KiB  
Review
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 2098
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
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