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Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 9017

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Prof. Dr. Yufei Wang

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Guest Editor

State Key Laboratory of Heavy Oil Processing, China University of Petroleum, Beijing 102249, China
Interests: energy system synthesis; energy-water nexus; chemical industry layout design, modeling and optimization; process integration
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lei Zhang

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Guest Editor

School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: process systems engineering; machine learning; AI for science

Special Issue Information

Dear Colleagues,

Process systems engineering can improve system efficiency in multiple ways. Especially with the rise of artificial intelligence, its efficient modeling and analysis capabilities have made it easier to integrate and optimize complex systems. Meanwhile, with the increasing demand for carbon reduction in industry, more attention is paid to environmental factors. It is necessary to analyze and optimize the overall performance of industrial engineering based on multiple factors such as environmental and economic factors.

To achieve the carbon neutrality goal, emissions should be reduced throughout the life cycle of the industrial processes, from the microscopic-scale system to macroscopic-scale system, based on the basic process system engineering method including process simulation, process control, and process optimization.

The latest research in this intriguing field of research will be shown and discussed at the 2024 China PSE Annual Meeting held in Dalian, China, on 24–25 August 2024. This Special Issue is a reflection of the high-quality papers presented at 2024 China PSE Annual Meeting. This Special Issue aims at showing the most recent advances in process/product design, process control/scheduling/planning, artificial intelligence theory and methods in process engineering, process simulation/analysis/optimization, etc.

Prof. Dr. Yufei Wang
Prof. Dr. Lei Zhang
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 design
artificial intelligence
process control
supply chain
optimization
carbon neutrality
sustainable development

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Published Papers (11 papers)

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Research

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28 pages, 2977 KiB

Open AccessArticle

Optimizing Solar PV Deployment in Manufacturing: A Morphological Matrix and Fuzzy TOPSIS Approach

by Citlaly Pérez Briceño, Pedro Ponce, Aminah Robinson Fayek, Brian Anthony, Russel Bradley, Therese Peffer, Alan Meier and Qipei Mei

Processes 2025, 13(4), 1120; https://doi.org/10.3390/pr13041120 - 8 Apr 2025

Viewed by 285

Abstract

The growing energy demand of the industrial sector and the need for sustainable solutions highlight the importance of efficient decision making in solar photovoltaic (PV) implementation. Selecting optimal PV configuration is complex due to the interdependent technical, economic, environmental, and social factors involved. This study introduces an integrated decision-making method combining a morphological matrix and fuzzy TOPSIS to systematically select and rank optimal PV system configurations for manufacturing firms. While the morphological matrix exhaustively examines possible design solutions based on sensing, smart, sustainable, and social (S4) attributes, the fuzzy TOPSIS method ranks the alternatives by handling uncertainty in decision making. A case study conducted in a Mexican manufacturing company validates the methodology’s effectiveness. The optimal PV configuration identified comprehensively addresses operational and sustainability criteria, covering all lifecycle stages. This approach demonstrates quantitative superiority and greater robustness compared to existing fuzzy TOPSIS-based methods for solar PV applications. The findings highlight the practical value of data-driven, multi-criteria decision making for industrial solar energy adoption, enhancing project feasibility, cost efficiency, and environmental compliance. Future research will incorporate discrete event simulation (DES) to further refine energy consumption strategies in manufacturing. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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19 pages, 3735 KiB

Open AccessArticle

Multi-Objective Evaluation Strategy Based on Data Envelopment Analysis for Working Fluid Selection in the Organic Rankine Cycle

by Luoyu Zhang, Lili Wang, Xiaoyan Sun, Li Xia, Shaohui Tao, Shuguang Xiang and Siyi Jin

Processes 2025, 13(4), 1013; https://doi.org/10.3390/pr13041013 - 28 Mar 2025

Viewed by 240

Abstract

Currently, in Chinese industry substantial amounts of low-grade waste heat are underutilized. Effectively harnessing these low-temperature waste heat sources is instrumental in promoting energy conservation and emission reduction objectives. The organic Rankine cycle (ORC) serves as an effective method for utilizing low-grade waste heat. The selection of a suitable working fluid is a pivotal aspect of the design of an ORC system. There are many kinds of working fluid and they have complex molecular structures, which increases the difficulty of screening working fluids. A novel approach is proposed based on data envelopment analysis (DEA) for multi-objective evaluation of working fluids. This method takes into account the thermodynamic performance of the working fluid in the ORC (thermal efficiency, net power output, exergy efficiency), economic aspects (investment cost, exergy loss cost), and environmental considerations (exergy environmental factors, CO₂ emission reduction). DEA offers a distinct advantage by objectively balancing these conflicting objectives through data-driven optimization, eliminating the need for subjective weight assignment and enabling simultaneous evaluation of thermodynamic, economic, and environmental metrics in working fluid selection. A total of 62 different working fluids were evaluated in the integrated technology. Heptane working fluid screened out by DEA was compared with working fluid R245fa, a fluid commonly used in existing literatures. The exergy loss of the Heptane working fluid is reduced by 5.02%, the thermal efficiency is increased by 0.24%, and the net output work is increased by 2.04%. The proposed evaluation method introduces a novel perspective for the efficient screening of working fluids in the ORC system for low-temperature waste heat power generation. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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23 pages, 3815 KiB

Open AccessArticle

Deep Neural Network Model Based on Process Mechanism Applied to Predictive Control of Distillation Processes

by Zirun Wang, Hao Wang and Zengzhi Du

Processes 2025, 13(3), 811; https://doi.org/10.3390/pr13030811 - 10 Mar 2025

Viewed by 550

Abstract

In modern process industries, precise process modeling plays a vital role in intelligent manufacturing. Nevertheless, both mechanistic and data-driven modeling methods have their own limitations. To address the shortcomings of these two modeling methods, we propose a neural network model based on process mechanism knowledge, aiming to enhance the prediction accuracy and interpretability of the model. The basic structure of this neural network consists of gated recurrent units and an attention mechanism. According to the different properties of the variables to be predicted, we propose an improved neural network with a distributed structure and residual connections, which enhances the interpretability of the neural network model. We use the proposed model to conduct dynamic modeling of a benzene–toluene distillation column. The mean squared error of the trained model is 0.0015, and the error is reduced by 77.2% compared with the pure RNN-based model. To verify the prediction ability of the proposed predictive model beyond the known dataset, we apply it to the predictive control of the distillation column. In two tests, it achieves results far superior to those of the PID control. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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18 pages, 8692 KiB

Open AccessArticle

A Comprehensive Optimization Framework for Diesel Filtration in Nuclear Emergency Systems: Integrating Genetic Algorithms, State-Space Networks, and Computational Fluid Dynamics

by Lanqi Zhang, Yupan He, Yong Zhou, Guoying Jiang, Xiangnan Chu, Qi Ma, Fengyi Liu and Haotian Ye

Processes 2025, 13(3), 648; https://doi.org/10.3390/pr13030648 - 25 Feb 2025

Viewed by 373

Abstract

Aging diesel fuel in emergency storage tanks at nuclear power plants requires regular filtration to remove impurities. Due to the stringent safety requirements of nuclear power plants, high standards are set for operational timeliness and reliability. This study proposes an efficient optimization framework that integrates MATLAB-based genetic algorithm optimization, state-space networks (SSNs), and computational fluid dynamics (CFD). A FLUENT simulation model is used to simulate the internal flow field of the diesel storage tank, while the SSN comprehensively analyzes flow distribution and filtration path strategies. Global parameter optimization is achieved using a genetic algorithm (GA). The framework improves filtration efficiency by 7.9%, reducing filtration time from 464,000 s to 417,600 s. Water impurity levels decreased by 22.2% (from 0.00045 to 0.00035), and mechanical impurities decreased by 33.3% (from 0.000015 to 0.000010). The findings not only enhance the reliability of emergency diesel generator systems and support nuclear safety operations but also provide a solid foundation for further innovations in emergency fuel filtration technologies. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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24 pages, 557 KiB

Open AccessArticle

Vehicle Routing Perfection for Fresh Agricultural Products Distribution Under Carbon Emission Regulation and Customer Satisfaction

by Xiaoyong Zhu, Yu Liang, Chao Wu and Yongmao Xiao

Processes 2025, 13(3), 605; https://doi.org/10.3390/pr13030605 - 20 Feb 2025

Viewed by 457

Abstract

Under the background of “carbon peaking and carbon neutrality”, carbon reduction is not only a realistic need for the high-quality development of the national economy, but also a key way for the sustainable development of enterprises. Cold chain logistics has the characteristics of high energy consumption and high carbon emissions. Fresh distribution requirements of fresh agricultural products may increase carbon emissions in the cold chain distribution process. Based on customer satisfaction and aiming at minimizing carbon emission and comprehensive distribution cost, this paper establishes an optimization model of cold chain logistics distribution routes for fresh agricultural products. First of all, a two-objective optimization model is proposed considering customer satisfaction maximization and comprehensive cost minimization, including fixed cost, fuel cost, carbon emission cost, cargo damage cost, and time window penalty cost. And when constructing customer satisfaction function, we mainly pay attention to the time factor. Secondly, a hybrid ant colony algorithm, which combines improved ant colony algorithm and local search algorithm 3-opt, is designed to solve the model. Thirdly, a hybrid ant colony algorithm is applied to the simulation example of fresh agricultural products distribution through experimental simulation, and the results are compared with the traditional ant colony algorithm and improved ant colony algorithm. Finally, the results reveal that the average total cost of the hybrid ant colony algorithm is lower than that of the traditional ant colony algorithm and the improved ant colony algorithm, achieving nearly 8.159% and 4.622% cost savings, respectively, and in terms of customer satisfaction, the new algorithm is slightly better than the other algorithms. The results show that the method can effectively optimize the cold chain logistics distribution routes of fresh agricultural products, and reduce carbon emission and distribution cost. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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23 pages, 15693 KiB

Open AccessArticle

A Fault Judgment Method of Catalyst Loss in FCC Disengager Based on Fault Tree Analysis and CFD Simulation

by Yuhui Li, Yunpeng Zhao, Zeng Li, Nan Liu, Chunmeng Zhu, Shouzhuang Li, Xiaogang Shi, Chengxiu Wang and Xingying Lan

Processes 2025, 13(2), 464; https://doi.org/10.3390/pr13020464 - 8 Feb 2025

Viewed by 534

Abstract

Catalyst loss is a typical fault that impacts the long-term operation of the fluidized catalytic cracking (FCC) in the oil refining process. The FCC disengager is a critical place for separating the catalyst from oil gas. A fast and precise fault-cause judgment of catalyst loss is vital for avoiding catalyst loss failures. In this study, a novel fault judgment method of catalyst loss failures with quantitative criteria was established via the fault tree analysis (FTA) method, based on the relationship model between flow field signals and faults in the FCC disengager investigated by computational fluid dynamics (CFD). The FTA method defines three intermediate events: catalyst fragmentation, process fault and mechanical fault. In CFD results, it was found that the detailed fault reason can be inferred based on the changes in the characteristic parameters within the disengager. For example, when the catalyst loss rate of the FCC disengager may rapidly increase by a factor of around 200. Furthermore, the pressure drop of the cyclone separator decreases by around 35%, which indicates that the dipleg has fractured. The new fault judgment method has been applied in cases of catalyst loss in two industrial disengagers. The method accurately pinpointed the sudden reduction in inlet velocity and blockage fault at the cyclone separator as the main factors leading to catalyst loss faults, respectively. The judgment results are consistent with actual reasons, demonstrating the reliability of the method. This study could contribute to providing theoretical support and enhancing the accuracy for the diagnosis of catalyst loss faults, thereby ensuring the safe and stable operation of the FCC unit. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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23 pages, 10070 KiB

Open AccessArticle

An Innovative NOx Emissions Prediction Model Based on Random Forest Feature Selection and Evolutionary Reformer

by Xianyu Meng, Xi Li, Jialei Chen, Yongyan Fu, Chu Zhang, Muhammad Shahzad Nazir and Tian Peng

Processes 2025, 13(1), 107; https://doi.org/10.3390/pr13010107 - 3 Jan 2025

Viewed by 859

Abstract

Developing more precise NOx emission prediction models is pivotal for effectively controlling NOx emissions from gas turbines. In this paper, a Reformer is combined with random forest (RF) feature selection and the chaos game optimization (CGO) algorithm to predict NOx in gas turbines. Firstly, RF evaluates the importance of data features and reduces the dimensionality of multidimensional data to improve the predictive performance of the model. Secondly, the Reformer model extracts the inherent pattern of different data and explores the intrinsic connection between gas turbine variables to establish a more accurate NOx emission prediction model. Thirdly, the CGO algorithm is a parameter-free meta-heuristic optimization algorithm used to find the best parameters for the prediction model. The CGO algorithm was improved using Chebyshev Chaos Mapping to improve the initial population quality of the CGO algorithm. To evaluate the efficiency of the proposed model, a dataset of gas turbines in north-western Turkey is studied, and the results obtained are compared with seven benchmark models. The final results of this paper show that RF can select appropriate input variables, and the Reformer can extract the intrinsic links of the data and build a more accurate NOx prediction model. At the same time, ICGO can optimize the parameters of the Reformer effectively. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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20 pages, 3764 KiB

Open AccessArticle

Corrosion State Monitoring Based on Multi-Granularity Synergistic Learning of Acoustic Emission and Electrochemical Noise Signals

by Rui Wang, Guangbin Shan, Feng Qiu, Linqi Zhu, Kang Wang, Xianglong Meng, Ruiqin Li, Kai Song and Xu Chen

Processes 2024, 12(12), 2935; https://doi.org/10.3390/pr12122935 - 22 Dec 2024

Viewed by 617

Abstract

Corrosion monitoring is crucial for ensuring the structural integrity of equipment. Acoustic emission (AE) and electrochemical noise (EN) have been proven to be highly effective for the detection of corrosion. Due to the complementary nature of these two techniques, previous studies have demonstrated that combining both signals can facilitate research on corrosion monitoring. However, current machine learning models have not yet been able to effectively integrate these two different modal types of signals. Therefore, a new deep learning framework, CorroNet, is designed to synergistically integrate AE and EN signals at the algorithmic level for the first time. The CorroNet leverages multimodal learning, enhances accuracy, and automates the monitoring process. During training, paired AE-EN data and unpaired EN data are used, with AE signals serving as anchors to help the model better align EN signals with the same corrosion stage. A new feature alignment loss function and a probability distribution consistency loss function are designed to facilitate more effective feature learning to improve classification performance. Experimental results demonstrate that CorroNet achieves superior accuracy in corrosion stage classification compared to other state-of-the-art models, with an overall accuracy of 97.01%. Importantly, CorroNet requires only EN signals during the testing phase, making it suitable for stable and continuous monitoring applications. This framework offers a promising solution for real-time corrosion detection and structural health monitoring. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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19 pages, 7913 KiB

Open AccessArticle

Investigation of a Cogeneration System Combining a Solid Oxide Fuel Cell and the Organic Rankine Cycle: Parametric Analysis and Multi-Objective Optimization

by Sheng Yang, Anman Liang, Zhengpeng Jin and Nan Xie

Processes 2024, 12(12), 2873; https://doi.org/10.3390/pr12122873 - 16 Dec 2024

Cited by 2 | Viewed by 863

Abstract

A novel solid oxide fuel cell (SOFC)-based cogeneration system is proposed here, integrating an organic Rankine cycle for waste heat recovery. Technical–economic and parametric analyses are conducted, and a multi-objective optimization is carried out. The results reveal that the net electrical efficiency, investment cost, and payback time are 56.6%, USD 2,408,256, and 3.27 years, respectively. The parametric analysis indicates that the current density should be limited between 0.3 A/cm² and 0.9 A/cm², and the stack temperature should be controlled between 675 °C and 875 °C. After the operational optimization of η_ele-Cost_TCI, the investment cost and the net electrical efficiency are obtained as USD 2,164,742 and 62.1%. After the η_ele-PBT optimization, the payback period and the net electrical efficiency are 3.22 years and 58.9%. The heat transfer network optimization achieves the highest efficiency and reduces the cold utilities by 43 kW, but three additional heat exchangers should be added to the system. This research provides practical reference and pragmatic guidance for the integration, analysis, operation, and heat transfer network optimization of SOFC-based cogeneration systems. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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15 pages, 6235 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Integrated Hybrid Modelling and Surrogate Model-Based Operation Optimization of Fluid Catalytic Cracking Process

by Haoran Li, Qiming Zhao, Ruqiang Wang, Wenle Xu and Tong Qiu

Processes 2024, 12(11), 2474; https://doi.org/10.3390/pr12112474 - 7 Nov 2024

Viewed by 1386

Abstract

Fluid Catalytic Cracking (FCC) is one of the most important conversion processes in oil refineries, widely used to convert high-boiling, high-molecular-weight hydrocarbon components from crude oil into more valuable products like gasoline and diesel. Advanced simulation and optimization technologies are critical for improving the operational efficiency and economic performance of the FCC process. First-principles-based simulators rely on parameter estimation and are computationally intensive, making them unsuitable for online optimization. In recent years, with the development of deep learning, data-driven models have made significant progress in FCC modeling. However, due to their black-box nature and difficulty with extrapolation, they are rarely used for optimization. To bridge this gap, we propose an integrated framework that combines hybrid modeling and surrogate model-based optimization. This approach combines plant and simulation data to train a multi-task learning prediction model, which then serves as a surrogate for operational optimization. Validated on a large-scale FCC unit in southern China, the model predicts product yields with an error margin of under 4.84% for all products. Following optimization, yields of LNG, gasoline, and diesel rose by an average of 0.10 wt%, 1.58 wt%, and 1.05 wt%, respectively, resulting in a 3.67% increase in product revenues. This highlights the substantial potential of this framework for industrial applications. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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Review

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16 pages, 5515 KiB

Open AccessReview

The Molecular Modeling, Simulation, and Design of Base Oils and Additives in Lubricating Oils: A Review

by Kang Zhou, Xinhao Che, Chaoliang Wei, Zhongping Tang, Hai Yu, Dong Wang, Jianxin Wang and Lei Zhang

Processes 2024, 12(11), 2407; https://doi.org/10.3390/pr12112407 - 31 Oct 2024

Viewed by 1918

Abstract

Lubricating oils play a crucial role in modern industrial production, mechanical manufacturing, aerospace, and other fields. This paper provides a review and summary of the entire workflow for molecular simulations of lubricating oils, from molecular modeling, especially the molecular representation of base oils, to simulation calculation methods and result analysis. The application prospects and values of the relevant simulation techniques are discussed in detail. The simulation methods, force fields, and software involved in the modeling and simulation process are also introduced, aiming to provide guidance and insights for more rigorous, rational, and accurate lubricant molecular simulations, so as to accelerate the modification and development of new high-quality lubricants. Full article

(This article belongs to the Special Issue Advances in Process Systems Engineering: Selected Papers from China PSE Annual Meeting)

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