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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 25513

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Special Issue Editors

Prof. Dr. Wende Tian

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Guest Editor

College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Interests: dynamic simulation; fault diagnosis; virtual reality; artificial intelligence; safety analysis

Prof. Dr. Qingjie Guo

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Guest Editor

1. Shandong Provincial Key Laboratory of Clean Chemical Process, College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
2. State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering, Ningxia University, Yinchuan 750021, China
Interests: chemical looping combustion/gasification; CO₂ capture and utilization technology; renewable energy; particle technology; energy and environmental engineering
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Dr. Bin Liu

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College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Interests: CO₂ capture; CO₂ storage and resource utilization; dynamic simulation; artificial intelligence; process design

Dr. Zhe Cui

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Guest Editor

College of Chemical Engineering, Qingdao University of Science & Technology, Qingdao 266042, China
Interests: dynamic simulation; wastewater treatment; chemical looping gasification; intelligent traceability; multi-scale modeling

Special Issue Information

Dear Colleagues,

Process systems engineering (PSE) integrates chemical engineering, systems engineering, intelligent engineering, control engineering, information technology, computer technology, management science and other disciplines of theory and technology for system optimization and sustainability. With the development of artificial intelligence, 5G technology, big data, blockchain and robots, PSE aims at energy saving, environmental protection, safety control, optimal operation and process strengthening of complex process production systems. From the system perspective, PSE is used for the development of multi-scale processing methods and data integration technology platforms for industrial process data, integrated modeling and system simulation technology platforms for logistics and energy flow, as well as state inspection and safety analysis technology platforms for process integration and process-strengthening solutions.

This Special Issue on “Process Systems Engineering for Chemical Process Safety and Environmental Protection” seeks high-quality works focusing on the latest novel developments in process systems engineering related to chemical process safety and environmental protection, which is of great significance for essential safety design and operation for the chemical industry. Topics include, but are not limited to:

Chemical process fault detection and diagnosis;
Risk assessment and reliability engineering;
Application of artificial intelligence technology in chemical process safety;
Chemical process resources and waste management;
Chemical process pollution (waste water, gas and residue) prevention and treatment.

Prof. Dr. Wende Tian
Prof. Dr. Qingjie Guo
Dr. Bin Liu
Dr. Zhe Cui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

process systems engineering
fault diagnosis
risk assessment
artificial intelligence
waste management
pollution treatment
chemical process

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Published Papers (11 papers)

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Research

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14 pages, 1852 KiB

Open AccessArticle

Research on State Evaluation of Petrochemical Plants Based on Improved TOPSIS Method and Combined Weight

by Yang Lin, Zhuang Yuan, Chengdong Gou, Wei Xu, Chunli Wang and Chuankun Li

Processes 2023, 11(6), 1799; https://doi.org/10.3390/pr11061799 - 13 Jun 2023

Viewed by 1109

Abstract

Due to the involvement of hazardous materials and the potential serious accidents that may occur in petrochemical plants, it is of great significance to develop real-time state evaluation methods offering high performance. Data-driven methods have received widespread attention following the development of advanced condition-monitoring systems. However, scarce training samples evaluated under multiple operating conditions are available because of the high stability and reliability requirements of petrochemical plants. In this paper, a real-time state evaluation method based on the technique for order preference by similarity to ideal solution (TOPSIS) is proposed, which circumvents dependence on data samples. First, the positive and negative ideal solutions of TOPSIS are determined using expert experience and the process index control limits of process cards. Then, fixed-value and fixed-interval indices are proposed to address the interval-optimal parameters. Subsequently, a new combined weight is established using the entropy method and the subjective weight coefficient. Finally, the above steps are integrated into an improved TOPSIS for the state evaluation of petrochemical plants. Experiments conducted on a fluid catalytic cracking (FCC) unit show that the proposed method can quantify the real-time operating status of a petrochemical plant. Furthermore, compared with the equal weight method, the evaluation result of combined weights is more aligned with the actual operating status. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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15 pages, 17922 KiB

Open AccessArticle

Multi-Criteria Screening of Organic Ethanolamines for Efficient CO₂ Capture Based on Group Contribution Method

by Bin Liu, Yanan Yu, Hanlin Liu, Zhe Cui and Wende Tian

Processes 2023, 11(5), 1524; https://doi.org/10.3390/pr11051524 - 16 May 2023

Cited by 2 | Viewed by 1616

Abstract

Amine solvent has attracted much attention due to its high CO₂ capture level and wide application range, but its high energy consumption for recycling restricts its large-scale commercialization. In this work, a multi-objective optimization technology based on the group contribution method was used to select potential amine solvents for CO₂ capture. This computer-aided molecular design method considers the thermodynamic and kinetic properties of the candidate solvent and evaluates the influence of relevant parameters on solvent performance. Compared with previous experimental methods used to optimize solvent, this method selects potential solvents from a large number of solvent databases based on group contribution. Firstly, a corresponding classification database was established for various kinds of amine solvents. Then, the traditional experiments were used to verify and screen solvents. At the same time, the method was applied to 31 amine absorbents concerning solubility, molar volume, surface tension, heat capacity, viscosity, pKa, saturated vapor pressure, and so on, and seven solvents were found to have comparable performance to MEA, with higher absorption rates and solubility. This method provides guidance for screening CO₂ capture absorbents with economic viability, high efficiency, fast absorption rates, and low regeneration energy consumption. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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21 pages, 5587 KiB

Open AccessArticle

Equation of State Associated with Activity Coefficient Model Based on Elements and Chemical Bonds

by Xinyu Li, Baowei Niu, Wenjiao Ma, Wenying Zhao, Xiaoyan Sun, Li Xia and Shuguang Xiang

Processes 2023, 11(5), 1499; https://doi.org/10.3390/pr11051499 - 15 May 2023

Cited by 2 | Viewed by 2354

Abstract

A new element- and chemical bond-dependent G^E-EoS model(SRK-UNICAC) is proposed to consider the deviation of the vapor and liquid phases from the ideal state. The SRK-UNICAC model combines the UNICAC model and the SRK cubic equation of state. It uses the original interaction parameters of the UNICAC model and uses this model to calculate the G^E. The SRK-UNICAC model predicted vapor-liquid equilibria for 87 binary systems under low- and medium-pressure conditions, 12 binary systems under high-pressure conditions, and 14 ternary systems; a comparison of the predictions with five other activity coefficient models were also made. The new model predicted the vapor-phase fraction and bubble-point pressure, and temperature for the binary system at high pressure, with a mean relative error of 3.75% and 6.58%, respectively. The mean relative errors of vapor-phase fraction and bubble-point temperature or bubble-point pressure for ternary vapor–liquid phase equilibrium were 6.50%, 4.76%, and 2.25%. The SRK-UNICAC model is more accurate in predicting the vapor–liquid phase equilibrium of high-pressure, non-polar, and polar mixtures and has a simpler and wider range of prediction processes. It can therefore be applied to the prediction of vapor–liquid equilibrium. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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23 pages, 10136 KiB

Open AccessArticle

A Quantitative Risk Assessment Model for Domino Accidents of Hazardous Chemicals Transportation

by Jinhua Cheng, Bing Wang, Chenxi Cao and Ziqiang Lang

Processes 2023, 11(5), 1442; https://doi.org/10.3390/pr11051442 - 10 May 2023

Cited by 4 | Viewed by 2795

Abstract

In recent years, hazardous materials transportation accidents have received increasing attention. Previous studies have focused on accidents involving a single vehicle. When vehicles loaded with materials gather on a stretch of road, a potential domino accident might cause terrible incidents. This paper prompts a quantitative risk assessment (QRA) model to estimate the risk of multi-vehicle incidents. The model calculates the possibility of leakage and explosion of hazardous chemicals using a dynamic Bayesian network (DBN). For different types of hazardous chemicals, the model uses event trees to list different scenarios and analyzes the probability of domino accidents caused by each scenario. The FN-curve and potential loss of life (PLL) are used as an index to evaluate social risk. A case involving multiple vehicles in the JinShan District, Shanghai, is analyzed. The result of the case shows that the state of the driver, the type of road, weather factors and the distance between vehicles have vital impacts on the societal risk resulting from hazardous materials transportation accidents. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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21 pages, 4089 KiB

Open AccessFeature PaperArticle

An Energy-Efficiency Prediction Method in Crude Distillation Process Based on Long Short-Term Memory Network

by Yu Zhang, Zhe Cui, Mingzhang Wang, Bin Liu, Xiaomin Fan and Wende Tian

Processes 2023, 11(4), 1257; https://doi.org/10.3390/pr11041257 - 19 Apr 2023

Cited by 4 | Viewed by 2372

Abstract

The petrochemical industry is a pillar industry for the development of the national economy affecting people’s daily living standards. Crude distillation process is the core and leading unit of the petrochemical industry. Its energy consumption accounts for more than 20% of the total energy consumption of the whole plant, which is the highest energy consumption link. A model based on the long short-term memory network (LSTM) is proposed in this paper to predict and analyze energy efficiency. This model extracts the complex relationship between many process variables and predicts the energy efficiency of the crude distillation process. Firstly, the process simulation of crude distillation is carried out. By adding random disturbance, the data set in different working conditions is obtained, and the difference between the working conditions is expressed with the distance-coded heat map. Secondly, the Savitzky–Golay (SG) filter is used to smooth the data, which preserves the original characteristics of the data and improves the prediction effect. Finally, the LSTM model is used to predict and analyze the energy efficiency of products under different working conditions. The MAE, MSE, and MAPE results of the LSTM model under different working conditions in the test set are lower than 1.3872%, 0.0307%, and 0.2555%, respectively. Therefore, the LSTM model can be considered a perfect model for the test set, and the prediction results have high reliability to accurately predict the energy efficiency of the crude distillation process. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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16 pages, 2724 KiB

Open AccessArticle

Prediction, Application, and Mechanism Exploration of Liquid–Liquid Equilibrium Data in the Extraction of Aromatics Using Sulfolane

by Shilong Dong, Xiaoyan Sun, Lili Wang, Yanjing Li, Wenying Zhao, Li Xia and Shuguang Xiang

Processes 2023, 11(4), 1228; https://doi.org/10.3390/pr11041228 - 16 Apr 2023

Cited by 2 | Viewed by 3043

Abstract

Liquid–liquid equilibrium (LLE) data are critical for the design and optimization of processes for extracting aromatics. Partial LLE data for the non-aromatic–aromatic–sulfolane ternary system were acquired at 313.15 K and 101.3 kPa. The LLE data for the extraction of aromatics using sulfolane were predicted using the COSMO-RS model. Correspondingly, the predicted and experimental data were analyzed using the root mean square deviation (RMSD), distribution coefficient (D), and separation factor (S). The COSMO-RS model could better predict the LLE data for the extraction of aromatics by sulfolane. The results of quantum chemical calculation show that hydrogen bonds and van der Waals interactions between sulfolane–benzene and sulfolane–toluene were responsible for the strong selectivity of sulfolane for benzene and toluene over alkanes. The LLE data predicted by the COSMO-RS method using the UNIQUAC thermodynamic model were subjected to correlation analysis. The calculated RMSD values were all less than 0.0180, and the relative deviation (δ) between the simulated value of the main process index for the extraction column and the actual data was less than 2.5%, indicating that the obtained binary interaction parameters can be reliably used in designing and optimizing the extraction of aromatics using sulfolane. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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14 pages, 4731 KiB

Open AccessArticle

Study on Estimation Method of Enthalpy of Evaporation Based on Elements and Chemical Bonds

by Yule Pan, Wenjiao Ma, Baowei Niu, Xinyu Li, Shuguang Xiang and Li Xia

Processes 2023, 11(4), 1064; https://doi.org/10.3390/pr11041064 - 1 Apr 2023

Cited by 1 | Viewed by 1415

Abstract

A new Group Contribution Method based on elements and chemical bonds was proposed to predict the enthalpy of evaporation of organic compounds at their normal boiling points. A prediction model was built using 1266 experimental data points, and the accuracy of the model estimations was evaluated using 16 experimental data points. The new method has only 42 groups, a simple way of group splitting, and a wide range of predictions with an average relative error of 5.84%. Furthermore, the inclusion of silicon elements and their chemical bonds in the group library enables the effective prediction of silicon-containing compounds with an average relative error of 2.71%. By analyzing and comparing the other three commonly used methods, it can be concluded that the new method provides accurate and reliable estimation results and has a more comprehensive application range. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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20 pages, 4499 KiB

Open AccessArticle

Energy-Saving Analysis of Epichlorohydrin Plant Based on Entransy

by Wenjiao Ma, Shuguang Xiang and Li Xia

Processes 2023, 11(3), 954; https://doi.org/10.3390/pr11030954 - 20 Mar 2023

Cited by 3 | Viewed by 1474

Abstract

To improve energy efficiency and to recover energy, various mathematical models, such as pinch analysis, entropy analysis, exergy analysis, and entransy analysis, have been established to analyze heat transfer networks. In this study, these methods were applied to analyze the energy-saving effect of the epichlorohydrin unit in a certain enterprise. The results showed that when the minimum heat transfer temperature difference (ΔT_min) was 10K, 15K, and 20K, the efficiencies of the second law of thermodynamics calculated by entropy analysis were 88.02%, 93.52%, and 99.49%, respectively. The analytical method calculated an efficiency of 61.01%, 59.28%, and 57.27%, respectively, with public works’ savings of 16.59%, 14.86%, and 12.02%. The pinch analysis method achieved public works’ savings of 22.80%, 21.50%, and 19.35%. The entransy analysis method calculated an entransy transfer efficiency of 42.81%, 42.13%, and 41.00%, respectively, with public works’ savings of 19.41%, 18.01%, and 15.70%. Based on the results, entropy analysis was found to be contrary to the principle of minimum entropy production. Exergy analysis was not able to establish a heat transfer network. The pinch analysis method was not suitable for determining the thermal efficiency of a heat transfer network as the criterion for evaluating energy saving. On the other hand, the entransy analysis method was able to establish a heat transfer network and evaluate the heat utilization of the network by entransy transfer efficiency. Overall, the data analysis was reasonable. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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16 pages, 5123 KiB

Open AccessArticle

Modeling and Optimization of the Para-Xylene Continuous Suspension Crystallization Separation Process via a Morphology Technique and a Multi-Dimensional Population Balance Equation

by Zhenxing Cai, Jixiang Liu, Hui Zhao, Xiaobo Chen and Chaohe Yang

Processes 2023, 11(3), 770; https://doi.org/10.3390/pr11030770 - 5 Mar 2023

Viewed by 2081

Abstract

In this study, we carried out a para-xylene crystallization experiment at constant temperature and concentration levels. Throughout the process, the kinetics of nucleation, growth, breakage, and aggregation of para-xylene particles were measured and built using a morphological approach. An additional a three-stage continuous suspension crystallization separation experiment was carried out, the process for which was simulated using the population balance model based on correlated kinetic equations. The population balance equation was solved using an extended moment of classes algorithm, and the solving process was implemented in MATLAB. In this case, the predicted particle size distribution of the products matched well with the experiment. In order to provide references for the optimization of the industrial para-xylene crystallization process, a three-stage suspension crystallization separation experiment was designed and conducted, in which each crystallizer had a distinct operating temperature and mean residence time. The effects of operating parameters on the final product were investigated further. The proposed models and algorithms can also be applied in other cases and provide an alternative approach for optimizing continuous crystallization processes. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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21 pages, 2261 KiB

Open AccessArticle

A Quantitative Analysis of Chemical Plant Safety Based on Bayesian Network

by Qiusheng Song and Li Song

Processes 2023, 11(2), 525; https://doi.org/10.3390/pr11020525 - 9 Feb 2023

Cited by 4 | Viewed by 2479

Abstract

Once a chemical production accident occurs in a chemical plant, it often causes serious economic losses, casualties, and environmental damage. Statistics show that many major accidents in the production and storage of chemicals are mainly caused by human factors. This article considers the influence of the human factor and proposes a quantitative analysis model of a chemical plant based on a Bayesian network. The model takes into account the main human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. The Bayesian network modeling method and simulation were used to predict the safety quantitative value and safety level of the chemical plant. Using this model, we can quickly calculate the safe quantitative ratio of each factor in the chemical plant. Through the safety quantitative value, safety level, and sensitivity analysis, the safety hazards of chemical companies can be discovered. Immediate improvements of potential safety hazards in chemical plants are very effective in preventing major safety accidents. This model provides an effective method for chemical park managers to monitor and manage chemical plants based on quantitative safety data. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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Review

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16 pages, 2038 KiB

Open AccessReview

Research Progress of Co-Catalysts in Photocatalytic CO₂ Reduction: A Review of Developments, Opportunities, and Directions

by Cheng Zuo, Qian Su and Xueyuan Yan

Processes 2023, 11(3), 867; https://doi.org/10.3390/pr11030867 - 14 Mar 2023

Cited by 7 | Viewed by 3318

Abstract

With the development of the global economy, large amounts of fossil fuels are being burned, causing a severe energy crisis and climate change. Photocatalytic CO₂ reduction is a clean and environmentally friendly method to convert CO₂ into hydrocarbon fuel, providing a feasible solution to the global energy crisis and climate problems. Photocatalytic CO₂ reduction has three key steps: solar energy absorption, electron transfer, and CO₂ catalytic reduction. The previous literature has obtained many significant results around the first two steps, while in the third step, there are few results due to the need to add a co-catalyst. In general, the co-catalysts have three essential roles: (1) promoting the separation of photoexcited electron–hole pairs, (2) inhibiting side reactions, and (3) improving the selectivity of target products. This paper summarizes different types of photocatalysts for photocatalytic CO₂ reduction, the reaction mechanisms are illustrated, and the application prospects are prospected. Full article

(This article belongs to the Special Issue Process Systems Engineering for Chemical Process Safety and Environmental Protection)

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