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Research on Process System Engineering
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Research on Process System 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 (30 September 2023) | Viewed by 25026

Special Issue Editors

Prof. Dr. Xiao Feng

E-Mail Website
Guest Editor
Faculty of Chemical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: process integration; energy system; water system integration; hydrogen network optimization; system analysis
Dr. Minbo Yang

E-Mail Website
Guest Editor
Faculty of Chemical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: energy integration; mass integration; process modeling, analysis, and optimization; mathematical programming; life cycle assessment

Special Issue Information

Dear Colleagues,

Process System Engineering (PSE) is a multidisciplinary field that focuses on the design, operation, control, and management of process systems. In recent decades, PSE has achieved major accomplishments in academia and also provided solutions for energy, economy, environment, and sustainability in the petrochemical, chemical, power, and other process industries. Undoubtedly, PSE will play an irreplaceable role in addressing the challenges in the industry of tomorrow including: digital and intelligent manufacturing. This Special Issue on “Research on Process System Engineering” invites high quality works on the recent and novel advances in the PSE theories, techniques, and applications. The topics can include, but are not limited to:

  • Product and process synthesis/design;
  • Process modeling, analysis, simulation, and optimization;
  • Process dynamics, control, and monitoring;
  • Enterprise-wide planning, scheduling, and optimization;
  • Intelligent process systems and manufacturing;
  • Digitalization and real-time optimization.

Prof. Dr. Xiao Feng
Dr. Minbo Yang
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 system
  • process integration
  • process control
  • modeling and optimization
  • machine learning
  • artificial intelligence
  • digitalization

Published Papers (13 papers)

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Editorial

Jump to: Research

4 pages, 147 KiB  
Editorial
Special Issue “Research on Process System Engineering”
by Minbo Yang and Xiao Feng
Processes 2024, 12(3), 607; https://doi.org/10.3390/pr12030607 - 19 Mar 2024
Viewed by 561
Abstract
Process system engineering (PSE) is a multidisciplinary research field that aims to address engineering problems related to the design, operation, control, and management of process systems [...] Full article
(This article belongs to the Special Issue Research on Process System Engineering)

Research

Jump to: Editorial

19 pages, 3167 KiB  
Article
Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network
by Prapatsorn Borisut and Aroonsri Nuchitprasittichai
Processes 2023, 11(11), 3232; https://doi.org/10.3390/pr11113232 - 16 Nov 2023
Cited by 1 | Viewed by 1299
Abstract
A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. [...] Read more.
A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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20 pages, 1424 KiB  
Article
An Adaptive-Noise-Bound-Based Set-Membership Method for Process Identification of Industrial Control Loops
by Zhu Wang, Qian Wang and Shaokang Zhang
Processes 2023, 11(10), 2835; https://doi.org/10.3390/pr11102835 - 26 Sep 2023
Viewed by 863
Abstract
Modeling of key variable data needs to consider the complex characteristics of systems in the catalytic cracking unit (CCU) of petroleum refining process, such as slow time-varying behavior, complex dynamic properties, distributed traits, and unknown stochastic noise. To fully capture the dynamics of [...] Read more.
Modeling of key variable data needs to consider the complex characteristics of systems in the catalytic cracking unit (CCU) of petroleum refining process, such as slow time-varying behavior, complex dynamic properties, distributed traits, and unknown stochastic noise. To fully capture the dynamics of a linear ordinary dynamic process without introducing incremental components, an adaptive-noise-bound-based set-membership method (RSMI) is proposed in this paper. Under the set-membership framework, the output set is typically represented as an ellipsoid based on the assumed conditions. Firstly, a CARMA model is considered; longer-duration historical data are selected to capture the intricate dynamic characteristics of industrial control loops. Secondly, RSMI introduces am approach to determine allowance factor, optimizing the noise bound for better suitability in real-world noise environments. The adaptive noise bound is achieved by designing an optimization algorithm that seeks the optimal parameters within the optimization framework. The stability of the RSMI algorithm is demonstrated through the application of the Lyapunov method. Next, the RSMI algorithm has been applied in engineering practice and designed for offline and online training stages of control processes. Finally, simulation experiments are performed to model and predict real-time data of flow, pressure, and liquid-level control loops within a catalytic cracking unit. Furthermore, the effectiveness of the RSMI algorithm is validated through two general examples, and frequency domain analysis is performed. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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15 pages, 1664 KiB  
Article
Process Simulation and Optimization of Fluid Catalytic Cracking Unit’s Rich Gas Compression System and Absorption Stabilization System
by Jin Sun, Haoshui Yu, Zengqi Yin, Liangliang Jiang, Li Wang, Shaolin Hu and Rujin Zhou
Processes 2023, 11(7), 2140; https://doi.org/10.3390/pr11072140 - 18 Jul 2023
Cited by 1 | Viewed by 1273
Abstract
In a fuel-based refinery, rich gas in the fluid catalytic cracking (FCC) unit is further processed to separate dry gas and refinery products (i.e., stabilized gasoline and liquified petroleum gas). The process is utility-intensive and costly and includes a two-stage compressor, pumps, an [...] Read more.
In a fuel-based refinery, rich gas in the fluid catalytic cracking (FCC) unit is further processed to separate dry gas and refinery products (i.e., stabilized gasoline and liquified petroleum gas). The process is utility-intensive and costly and includes a two-stage compressor, pumps, an absorber, a stripper, a stabilizer, and a re-absorber. The optimization was conducted with respect to the compressor outlet pressure from the gas compression system (GCS) and the flow rate of absorbent and supplementary absorbent from the Absorption-stabilization System (ASS) using the process simulation software Aspen Plus. Compared to the base case of a 725 kt/a rich gas FCC unit, a refinery can save 2.42% of utility costs under optimal operation. Through optimized operation, medium-pressure steam consumption has been reduced by 2.4% compared to the base case, resulting in a significant improvement in total operational cost. The optimization strategy can provide insightful guidance for the practical operation of GCS and ASS. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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20 pages, 4843 KiB  
Article
Chemical Looping Enhanced Oil Shale-to-Liquid Fuels Process: Modeling, Parameter Optimization, and Performance Analysis
by Qiang Wang, Yong Yang and Huairong Zhou
Processes 2023, 11(3), 929; https://doi.org/10.3390/pr11030929 - 18 Mar 2023
Viewed by 1314
Abstract
The solid heat carrier moving bed with internals is an advanced oil shale retorting technology. However, the retorting gas produced by pyrolysis is generally used as fuel gas. The content of CO, H2, and CH4 in the retorting gas is [...] Read more.
The solid heat carrier moving bed with internals is an advanced oil shale retorting technology. However, the retorting gas produced by pyrolysis is generally used as fuel gas. The content of CO, H2, and CH4 in the retorting gas is high, and direct combustion leads to resource waste and environmental pollution. In addition, heteroatomic sulfur and nitrogen, as well as unsaturated hydrocarbons, reduce the quality of shale oil. To solve these problems, this paper proposed a chemical looping enhanced oil shale-to-liquid fuels (CLeSTL) process. The chemical looping hydrogen production technology is applied to convert retorting gas to hydrogen, and the hydrogen produced is used for shale oil hydrogenation to improve the oil quality. In this paper, the new process is modeled and simulated; then technoeconomic analysis is carried out. Technical analysis shows that shale oil yield is increased from 65% to 95.7% and the yield of light fraction is increased from 20% to 64%–83%. Economic analysis shows that the CLeSTL process with ligh fraction hydrogenation has the highest investment profit rate and large profit space. In addition, when the oil price is lower than 50 USD/bbl, the investment profit is 5%, which shows strong anti-risk ability. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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14 pages, 3928 KiB  
Article
A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning
by Liangshan Shao and Kun Zhang
Processes 2023, 11(3), 883; https://doi.org/10.3390/pr11030883 - 15 Mar 2023
Viewed by 1122
Abstract
This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 [...] Read more.
This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By comparing the results of different sets, the final prediction sequence could be obtained, and a model that was composed of the optimal feature parameters was applied to the optimal machine learning algorithm. The results showed that the HKELM outperformed LSSVM in prediction accuracy, running speed, and stability. The root meant square error (RMSE) for the final prediction sequence was 0.22865, the determination coefficient (R2) was 0.99395, the mean absolute error (MAE) was 0.20306, and the mean absolute percentage error (MAPE) was 1.0595%. Every index of accuracy evaluation performed well and the constructed prediction model had high-prediction accuracy and a wide application. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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15 pages, 2076 KiB  
Article
Integrated Optimization for the Coupling Network of Refinery and Synthetic Plant of Chemicals
by Sen Yang, Qiao Zhang and Xiao Feng
Processes 2023, 11(3), 789; https://doi.org/10.3390/pr11030789 - 07 Mar 2023
Cited by 2 | Viewed by 986
Abstract
Synthetic plant of chemicals (SPC) consumes large amounts of hydrogen and carbon-oxides while refineries require high-purity hydrogen. Coal gasification (CG) and steam methane reforming (SMR) are common industrial hydrogen production technologies. Their gas products are essentially a mixture of H2, CO, [...] Read more.
Synthetic plant of chemicals (SPC) consumes large amounts of hydrogen and carbon-oxides while refineries require high-purity hydrogen. Coal gasification (CG) and steam methane reforming (SMR) are common industrial hydrogen production technologies. Their gas products are essentially a mixture of H2, CO, and CO2. Therefore, such gas products can provide both syngas for SPC and concentrated hydrogen for refinery through appropriate allocation. Based on the composition complementation of gas products from CG and SMR for their efficient utilization, this paper proposed an integration methodology for refinery and SPC coupling networks to conserve both fossil fuel resources and carbon emissions. A superstructure is established as a problem illustration and a nonlinear programming model (NLP) is formulated as a mathematical solution. A case study is performed, and the results show that the coupling network integration can save 19.1% and 20.2% of coal and natural gas consumption, as well as corresponding carbon emission and operation costs. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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18 pages, 1188 KiB  
Article
Agent-Based and Stochastic Optimization Incorporated with Machine Learning for Simulation of Postcombustion CO2 Capture Process
by Huilan Zheng, Gaurav Mirlekar and Lars O. Nord
Processes 2022, 10(12), 2727; https://doi.org/10.3390/pr10122727 - 16 Dec 2022
Cited by 1 | Viewed by 1725
Abstract
In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is [...] Read more.
In this paper, a novel method is proposed for the incorporation of data-driven machine learning techniques into process optimization. Such integration improves the computational time required for calculations during optimization and benefits the online application of advanced control algorithms. The proposed method is illustrated via the chemical absorption-based postcombustion CO2 capture process, which plays an important role in the reduction of CO2 emissions to address climate challenges. These processes simulated in a software environment are typically based on first-principle models and calculate physical properties from basic physical quantities such as mass and temperature. Employing first-principle models usually requires a long computation time, making process optimization and control challenging. To overcome this challenge, in this study, machine learning algorithms are used to simulate the postcombustion CO2 capture process. The extreme gradient boosting (XGBoost) and support vector regression (SVR) algorithms are employed to build models for prediction of carbon capture rate (CR) and specific reboiler duty (SRD). The R2 (a statistical measure that represents the fitness) of these models is, on average, greater than 90% for all the cases. XGBoost and SVR take 0.022 and 0.317 s, respectively, to predict CR and SRD of 1318 cases, whereas the first-principal process simulation model needs 3.15 s to calculate one case. The models built by XGBoost are employed in the optimization methods, such as an agent-based approach represented by the particle swarm optimization and stochastic technique indicated by the simulated annealing, to find specific optimal operating conditions. The most economical case, in which the CR is 72.2% and SRD is 4.3 MJ/kg, is obtained during optimization. The results show that computations with the data-driven models incorporated in the optimization technique are faster than first-principle modeling approaches. Thus, the application of machine learning techniques in the optimization of carbon capture technologies is demonstrated successfully. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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12 pages, 3614 KiB  
Article
Life Cycle Energy Consumption and GHG Emissions of the Copper Production in China and the Influence of Main Factors on the above Performance
by Lingchen Liu, Dong Xiang, Huiju Cao and Peng Li
Processes 2022, 10(12), 2715; https://doi.org/10.3390/pr10122715 - 15 Dec 2022
Cited by 5 | Viewed by 3278
Abstract
The copper demand and production in China are the largest in the world. In order to obtain the trends of the energy consumption and GHG emissions of copper production in China over a number of years, this paper uses a life cycle analysis [...] Read more.
The copper demand and production in China are the largest in the world. In order to obtain the trends of the energy consumption and GHG emissions of copper production in China over a number of years, this paper uses a life cycle analysis method to calculate the above two indexes, in the years between 2004 and 2017. The life cycle energy consumption ranged between 101.78 and 31.72 GJ/t copper and the GHG emissions varied between 9.96 and 3.09 t CO2 eq/t copper due to the improvements in mining and smelting technologies. This study also analyses the influence of electricity sources, auxiliary materials consumption, and copper ore grade on the life cycle performance. Using wind or nuclear electricity instead of mixed electricity can reduce energy consumption by 63.67–76.27% or 64.23–76.94%, and GHG emissions by 64.42–77.84% or 65.08–78.63%, respectively. The GHG emissions and energy consumption of underground mining are approximately 2.97–7.03 times that of strip mining, while the influence of auxiliary materials on the above two indexes is less than 3.88%. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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16 pages, 6983 KiB  
Article
Comparative Study on Snowflake Dendrite Solidification Modeling Using a Phase-Field Model and by Cellular Automaton
by Yi Dang, Jiali Ai, Jindong Dai, Chi Zhai and Wei Sun
Processes 2022, 10(11), 2337; https://doi.org/10.3390/pr10112337 - 09 Nov 2022
Cited by 2 | Viewed by 1624
Abstract
Dendrite is among the most frequently observed structures during the solidification process. Different dendrite morphologies caused by environmental conditions can affect the physical properties of materials. The formation of snowflakes can generate various morphologies under different conditions, and is used in this work [...] Read more.
Dendrite is among the most frequently observed structures during the solidification process. Different dendrite morphologies caused by environmental conditions can affect the physical properties of materials. The formation of snowflakes can generate various morphologies under different conditions, and is used in this work as an example. Simulation technologies provide insight into the correlation between a resulting morphology and its impact parameter, including the phase-field method (PF) and cellular automaton (CA). The PF method is derived from thermodynamic functions and kinetic equations, while the CA model is established by interaction rules between subsystems. It is difficult to solve the PF method due to the coupled differential equations, wherein the actual physical parameters are included. The CA model is conceptually simple and computationally efficient; however, the physical meaning of the parameters is absent. In this work, an example of snowflake formation is considered by PF with all the impact factors defined first, and then parameters in CA are searched by iterations to approximate the result, i.e., latent heat and the anisotropic coefficient in the PF method correspond to the initial distribution and the environmental effect in the CA model. In addition, the discrete time of each iteration in the CA model is identified according to the dendritic growth speed of these two models. A systematic identification process for the CA parameters’ physical meaning is demonstrated by the comparison with the PF method, and an approximate simulation of the PF method can be obtained simply by the CA model. The combination of the PF method and the CA model can be used to investigate the influence of environmental factors on dendritic morphology. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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17 pages, 3543 KiB  
Article
Dynamic Simulation Analysis and Optimization of Green Ammonia Production Process under Transition State
by Wu Deng, Chao Huang, Xiayang Li, Huan Zhang and Yiyang Dai
Processes 2022, 10(10), 2143; https://doi.org/10.3390/pr10102143 - 20 Oct 2022
Cited by 3 | Viewed by 5556
Abstract
Ammonia is an important chemical raw material and the main hydrogen energy carrier. In the context of “carbon neutrality”, green ammonia produced using renewable energy is cleaner and produces less carbon than traditional ammonia production. Raw hydrogen dynamically fluctuates during green ammonia production [...] Read more.
Ammonia is an important chemical raw material and the main hydrogen energy carrier. In the context of “carbon neutrality”, green ammonia produced using renewable energy is cleaner and produces less carbon than traditional ammonia production. Raw hydrogen dynamically fluctuates during green ammonia production because it is affected by the instability and intermittency of renewable energy; the green ammonia production process has frequent variable working conditions to take into account. Therefore, studying the transition state process of green ammonia is critical to the processing device’s stable operation. In this study, a natural gas ammonia production process was modified using green ammonia, and steady-state and dynamic models were established using UniSim. The model was calibrated using actual factory data to ensure the model’s reliability. Based on the steady-state model, hydrogen feed flow disturbance was added to the dynamic model to simulate the transition state process under variable working conditions. The change in system energy consumption in the transition state process was analyzed based on the data analysis method. The proportional-integral-derivative (PID) parameter optimization method was developed to optimize energy consumption under variable conditions of green ammonia’s production process. Based on this method, process control parameters were adjusted to shorten fluctuation time and reduce energy consumption. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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18 pages, 1434 KiB  
Article
Elements and Chemical Bonds Contribution Estimation of Activity Coefficients in Nonideal Liquid Mixtures
by Haodong Liu, Xinyu Li, Yuxin Wang, Xiaoyan Sun, Wenying Zhao, Li Xia and Shuguang Xiang
Processes 2022, 10(10), 2141; https://doi.org/10.3390/pr10102141 - 20 Oct 2022
Cited by 3 | Viewed by 1508
Abstract
Based on the contribution of elements and chemical bonds, the UNICAC (Universal Quasi-Chemical elements and chemical bonds Activity Coefficient) method was proposed to estimate the activity coefficients of nonelectrolyte liquid mixtures. The UNICAC method defined 10 elements and 33 chemical bonds as contribution [...] Read more.
Based on the contribution of elements and chemical bonds, the UNICAC (Universal Quasi-Chemical elements and chemical bonds Activity Coefficient) method was proposed to estimate the activity coefficients of nonelectrolyte liquid mixtures. The UNICAC method defined 10 elements and 33 chemical bonds as contribution groups. The calculation of activity coefficients was divided into the combination term and the residual term. The combination term represents molecular size differences, and the residual term describes the interaction between molecules. The interaction energy parameters of 43 groups were regressed simultaneously with the experimental data of the vapor–liquid equilibrium of 1085 binary systems. According to the molecular structural information of compounds, the UNICAC method can accurately predict the activity coefficients of nonelectrolyte liquid mixtures. The vapor–liquid equilibrium of 16 groups of the binary system, which were not included in the parameters regress, was predicted using UNICAC. The average relative error of vapor composition was 1.53%. Compared with UNIFAC (2003), UNIFAC (Lyngby), UNIFAC (Dortmund), and ASOG (2011), the UNICAC model employs fewer parameters, provides a broader scope of application, and receives more precise predicted results of the vapor–liquid equilibrium. The UNICAC method would play an important reference role in the design of the chemical separation process. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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18 pages, 4770 KiB  
Article
Comprehensive Analysis and Targeting of Distillation Integrated into Overall Process Considering Operating Pressure Change
by Wenting Duan, Minbo Yang and Xiao Feng
Processes 2022, 10(9), 1861; https://doi.org/10.3390/pr10091861 - 15 Sep 2022
Cited by 2 | Viewed by 2231
Abstract
Distillation is important to chemical processes but it is energy intensive, and its optimization is of great significance to energy savings and emissions reduction. Varying the operating pressures of distillation columns could assist heat integration of distillation columns into the overall process, thereby [...] Read more.
Distillation is important to chemical processes but it is energy intensive, and its optimization is of great significance to energy savings and emissions reduction. Varying the operating pressures of distillation columns could assist heat integration of distillation columns into the overall process, thereby reducing energy consumption. However, influences of varying column pressures on the energy profiles of the overall process have not been systematically analyzed in previous studies. This paper presents an insightful analysis of heat integration of distillation into the overall process considering the change of operating pressure. Firstly, effects of changing the operating pressure of a distillation column on its own utility requirements and the related process streams are studied. Next, such effects are graphically represented and incorporated into the grand composite curve (GCC). The change tendencies of the GCC, pinch temperature, and total utility consumption are analyzed and presented. On this basis, rules to identify the best operating pressure that minimize the overall energy consumption are proposed. A continuous reforming unit in a petrochemical enterprise is quantitatively analyzed to verify the obtained rules. The result indicates that the hot utility of the overall process can be reduced by 758 kW when the column pressure is lowered by 260 kPa. Full article
(This article belongs to the Special Issue Research on Process System Engineering)
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