Special Issue "Process Optimization and Control"

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

Deadline for manuscript submissions: closed (10 December 2019).

Special Issue Editors

Prof. Dr. Xi Chen
E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Zheda Road 38, Hangzhou 310027, China
Interests: process systems engineering; process modeling and optimization; process synthesis; parallel computation; scheduling and planning; industrial applications in polymerization, air separation, fine chemicals, and plastic processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenli Du
E-Mail Website
Guest Editor
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Interests: modeling; control and optimization for complex processes in refineries; ethylene
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Dr. Antonios Armaou
E-Mail Website
Guest Editor
Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
Interests: process dynamics and control, computational modeling and applied mathematics, process analysis and design
Assoc. Prof. Dr. Xiang Li
E-Mail Website
Guest Editor
Dept. of Chemical Engineering, Queen's University, 19 Division St., Kingston, ON K7L 3N6, Canada
Interests: process design and operation; planning and scheduling; supply chain optimization; energy and water networks; global optimization; optimization under uncertainty

Special Issue Information

Dear Colleagues,

The term “process industry” refers to elementary raw material industries such as industries for petroleum, chemicals, steel, nonferrous metals, and building materials, which are fundamental for the national economy. Lately, due to limited resources and increasingly rigorous safety and environment constraints, the methodoly of process system engineering (PSE) has played an increasingly important role, especially in China, for process industries to achieve sustainable growth.Major attention has been attracted to the field of process optimization and control. Besides the theory and method development, numerous industrial implementations have been reported, showing that demand from the process industry leads the trend. However, a gap still exists between theory and practice in terms of overall application effectiveness. Therefore, this Special Issue will provide a good opportunity for researchers from China and all over the world to talk and move forward.
This Special Issue solicites high-quality papers from three key academic conferences held in China ananualy in this area, including the Chinese Process System Engineering Conference, the Chinese Process Control Conference, and the Chinese Intelligent Control and Automatic Instrument Conference. This Special Issue on “Process Optimization and Control” aims to curate novel advances in the development and application of optimization and control to address longstanding challenges in industrial process.  Topics include but are not limited to the following:

  • Industrial process management and decision systems;
  • Integrated decision-making and control systems;
  • Advanced control optimization technology;
  • Artificial intelligence-driven optimization and control technology;
  • Industrial big data and cloud computing;
  • Safety monitoring and faulty detection systems;
  • Industrial modeling and simualtion systems;
  • Process instruments and smart devices;
  • Smart manufaturing systems.

Prof. Dr. Xi Chen
Prof. Dr. Wenli Du
Assoc. Prof. Dr. Antonios Armaou
Assoc. Prof. Dr. Xiang Li
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 papers will be 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 1200 CHF (Swiss Francs). Please note that for papers submitted after 31 December 2019 an APC of 1400 CHF applies. 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 optimization
  • advanced control
  • industrial modeling
  • decision making
  • artificial intelligence

Published Papers (21 papers)

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Research

Article
Model-Free Adaptive Direct Torque Control for the Speed Regulation of Asynchronous Motors
Processes 2020, 8(3), 333; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8030333 - 12 Mar 2020
Viewed by 802
Abstract
In this paper, a model-free adaptive direct torque control (MFADTC) method for the speed regulation of asynchronous motors is proposed to solve the problems of modeling difficulties and poor anti-disturbance ability of the asynchronous motor. The designed model-free adaptive direct torque control (MFADTC) [...] Read more.
In this paper, a model-free adaptive direct torque control (MFADTC) method for the speed regulation of asynchronous motors is proposed to solve the problems of modeling difficulties and poor anti-disturbance ability of the asynchronous motor. The designed model-free adaptive direct torque control (MFADTC) method depends merely on the input and the output data of the asynchronous motor. Numerical simulations are provided to show that this method has significantly improved the system’s anti-disturbance ability. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Optimal Speed Control for a Semi-Autogenous Mill Based on Discrete Element Method
Processes 2020, 8(2), 233; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020233 - 18 Feb 2020
Cited by 1 | Viewed by 1093
Abstract
The rotation speed of a mill is an important factor related to its operation and grinding efficiency. Analysis and regulation of the optimal speed under different working conditions can effectively reduce energy loss, improve productivity, and extend the service life of the equipment. [...] Read more.
The rotation speed of a mill is an important factor related to its operation and grinding efficiency. Analysis and regulation of the optimal speed under different working conditions can effectively reduce energy loss, improve productivity, and extend the service life of the equipment. However, the relationship between the optimal speed and different operating parameters has not received much attention. In this study, the relationship between the optimal speed and particle size and number was investigated using discrete element method (DEM). An improved exponential approaching law sliding mode control method is proposed to track the optimal speed of the mill. Firstly, a simulation was carried out to investigate the relationship between the optimal speed and different operating parameters under cross-over testing. The model of the relationships between the optimal rotation speed and the size and number of particles was established based on the response surface method. An improved sliding mode control using exponential approaching law is proposed to track the optimal speed, and simulation results show it can improve the stability and speed of sliding mode control near the sliding surface. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Comparing Composition Control Structures for Kaibel Distillation Columns
Processes 2020, 8(2), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020218 - 13 Feb 2020
Cited by 2 | Viewed by 986
Abstract
Although Kaibel distillation columns are superior to conventional distillation sequences owing to smaller equipment investment and operation cost, they display high nonlinearity and this greatly increases the difficulty of achieving their tight control. To overcome this problem, four decentralized composition control structures, i.e., [...] Read more.
Although Kaibel distillation columns are superior to conventional distillation sequences owing to smaller equipment investment and operation cost, they display high nonlinearity and this greatly increases the difficulty of achieving their tight control. To overcome this problem, four decentralized composition control structures, i.e., the CSR/QR, CSR/B, CSD/QR, and CSD/B structures, are proposed and compared based on the control of a Kaibel distillation column fractionating a methanol/ethanol/propanol/butanol quaternary mixture. These four composition control structures all include five composition control loops. While the four of them are employed to maintain the purity of the top, upper sidestream, lower sidestream, and bottom products, the remaining one is employed to minimize the energy consumption of the Kaibel distillation column by maintaining the composition of propanol at the first stage of the prefractionator. Dynamic simulation results show the CSR/QR and CSR/B structures can tightly maintain the purity of the controlled products with a small overshoot and short settling time after facing various disturbances in feed conditions, but the CSD/QR and CSD/B structures lead to oscillatory responses (the latter even shows divergent responses under individual disturbances). At the end of the article, some effective guides for developing composition control systems are given. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Coupling Layout Optimization of Key Plant and Industrial Area
Processes 2020, 8(2), 185; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020185 - 05 Feb 2020
Cited by 2 | Viewed by 851
Abstract
Layout problems are an engineering task that heavily relies on project experience. During the design of a plant, various factors need to be considered. Most previous efforts on industrial layout design have focused on the arrangement of facilities in a plant. However, the [...] Read more.
Layout problems are an engineering task that heavily relies on project experience. During the design of a plant, various factors need to be considered. Most previous efforts on industrial layout design have focused on the arrangement of facilities in a plant. However, the area-wide layout was not thoroughly studied and the relationship between plant layout and area-wide layout was rarely mentioned. In this work, the key plant that has the greatest impact on the industrial area is figured out first, and then the coupling relationships between the key plant and the industrial area are studied by changing the occupied area and length-width ratio of the key plant. Both of them are achieved by changing the floor number. A hybrid algorithm involving the genetic algorithm (GA) and surplus rectangle fill algorithm (SRFA) is applied. Various constraints are considered to make the layout more reasonable and practical. In the case study, a refinery with 20 plants is studied and the catalytic cracking plant is found to be the key plant. After the retrofit, the total cost of the refinery is 1,806,100 CNY/a less than that before, which illustrates the effectiveness of the method. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration
Processes 2020, 8(2), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020181 - 05 Feb 2020
Viewed by 800
Abstract
To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) [...] Read more.
To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
A Semi-Continuous PWA Model Based Optimal Control Method for Nonlinear Systems
Processes 2020, 8(2), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020170 - 04 Feb 2020
Cited by 1 | Viewed by 763
Abstract
To alleviate the mode mismatch of multiple model methods for nonlinear systems when completely discrete dynamical equations are adopted, a semi-continuous piecewise affine (SCPWA) model based optimal control method is proposed. Firstly, a SCPWA model is constructed where modes evolve in continuous time [...] Read more.
To alleviate the mode mismatch of multiple model methods for nonlinear systems when completely discrete dynamical equations are adopted, a semi-continuous piecewise affine (SCPWA) model based optimal control method is proposed. Firstly, a SCPWA model is constructed where modes evolve in continuous time and continuous states evolve in discrete time. Thanks to this model, a piecewise affine (PWA) system can switch at any time instant whereas mode switching only occurs at sample instants when a completely discrete PWA model is adopted, which improves the prediction accuracy of multi-models. Secondly, the switching condition is relaxed such that operating subspaces have overlaps and switching condition parameters are introduced. As a consequence, an optimal control problem with fixed mode switching sequence is established. Finally, a SCPWA model based model predictive control (MPC) policy is designed for nonlinear systems. The convergence of the MPC algorithm is proved. Compared with widely used mixed logical dynamic (MLD) model based methods, the proposed method not only alleviates mode mismatch, but also lightens the computing burden, hence improves the control performance and reduces the computation time. Some numerical examples are provided as well to show the efficiency of the method. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Quality Control for Medium Voltage Insulator via a Knowledge-Informed SPSA Based on Historical Gradient Approximations
Processes 2020, 8(2), 146; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020146 - 23 Jan 2020
Cited by 4 | Viewed by 1018
Abstract
Medium voltage insulators are essential and versatile components in electrical engineering. Quality control of the manufacturing process for the insulators has a significant role in their economic production and reliable operation. As the quality of medium voltage insulator is mainly affected by the [...] Read more.
Medium voltage insulators are essential and versatile components in electrical engineering. Quality control of the manufacturing process for the insulators has a significant role in their economic production and reliable operation. As the quality of medium voltage insulator is mainly affected by the process parameters of the automatic pressure gelation process (APG), the optimal process settings are required to achieve a satisfactory quality target. However, traditional process parameters’ optimization methods are often cumbersome and cost-consuming. Moreover, the operational cost of APG for insulator production is relatively high. Therefore, the determination of the optimal settings becomes a significant challenge for the quality control of insulators. To address the above issues, an idea of knowledge-informed optimization was proposed in this study. Based on the above idea, a knowledge-informed simultaneous perturbation stochastic approximation (SPSA) methodology was formulated to reduce the optimization costs, and thus improve the efficiency of quality control. Considering the characteristics of SPSA, the historical gradient approximations generated during the optimization process were utilized to improve the accuracy of gradient estimations and to tune the iteration step size adaptively. Therefore, an implementation of a quality control strategy of knowledge-informed SPSA based on historical gradient approximations (GK-SPSA) was thus constructed. In this paper, the GK-SPSA-based quality control method was applied to the weight control of a kind of post insulators. The experimental simulation results showed that the revised knowledge-informed SPSA was effective and efficient on quality control of medium voltage insulators. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Multimode Operating Performance Visualization and Nonoptimal Cause Identification
Processes 2020, 8(1), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010123 - 19 Jan 2020
Cited by 1 | Viewed by 872
Abstract
In the traditional performance assessment method, different modes of data are classified mainly by expert knowledge. Thus, human interference is highly probable. The traditional method is also incapable of distinguishing transition data from steady-state data, which reduces the accuracy of the monitor model. [...] Read more.
In the traditional performance assessment method, different modes of data are classified mainly by expert knowledge. Thus, human interference is highly probable. The traditional method is also incapable of distinguishing transition data from steady-state data, which reduces the accuracy of the monitor model. To solve these problems, this paper proposes a method of multimode operating performance visualization and nonoptimal cause identification. First, multimode data identification is realized by subtractive clustering algorithm (SCA), which can reduce human influence and eliminate transition data. Then, the multi-space principal component analysis (MsPCA) is used to characterize the independent characteristics of different datasets, which enhances the robustness of the model with respect to the performance of independent variables. Furthermore, a self-organizing map (SOM) is used to train these characteristics and map them into a two-dimensional plane, by which the visualization of the process monitor is realized. For the online assessment, the operating performance of the current process is evaluated according to the projection position of the data on the visual model. Then, the cause of the nonoptimal performance is identified. Finally, the Tennessee Eastman (TE) process is used to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Controllability Comparison of the Four-Product Petlyuk Dividing Wall Distillation Column Using Temperature Control Schemes
Processes 2020, 8(1), 116; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010116 - 16 Jan 2020
Cited by 3 | Viewed by 1091
Abstract
An effective process intensification strategy based on dividing walls shows promising energy-saving results for distillation processes. The three-product Petlyuk dividing wall distillation columns (DWDCs) are able to save approximately 30% energy in comparison with the traditional distillation columns. Furthermore, the four-product extended Petlyuk [...] Read more.
An effective process intensification strategy based on dividing walls shows promising energy-saving results for distillation processes. The three-product Petlyuk dividing wall distillation columns (DWDCs) are able to save approximately 30% energy in comparison with the traditional distillation columns. Furthermore, the four-product extended Petlyuk DWDC reduces about 50% of operation costs than conventional distillation sequences. Although researchers have extensively studied control schemes for the three-product Petlyuk DWDC, relatively little work has been done on the four-product extended Petlyuk DWDC. This paper studies feasible temperature control schemes containing temperature control scheme (TC), simplified temperature difference control scheme (STDC), and simplified double temperature difference control scheme (SDTDC) for the four-product extended Petlyuk DWDC. STDC and SDTDC are introduced so as to improve the dynamic performances with simple control schemes. All three control schemes are tested against a series of feed compositions and feed rate disturbances. Dynamic performances prove that the proposed STDC and SDTDC schemes are better at handling the inserted feed disturbances. These are very encouraging results for industrialization of the four-product extended Petlyuk DWDC in the future. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes
Processes 2020, 8(1), 105; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010105 - 13 Jan 2020
Cited by 4 | Viewed by 1228
Abstract
A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained [...] Read more.
A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained based on the obtained key process variables. Thirdly, the pseudo label confidence of identification model is dynamically optimized with an historical, current, and future pseudo label confidence mean. To increase the upper limit of the identification model that is self-learning with high entropy process data, active learning is used to identify process data and diagnosis fault causes by ontology. Finally, a PCA-dynamic active safe semi-supervised support vector machine (PCA-DAS4VM) for fault identification in labeled expensive chemical processes is built. The application in the Tennessee Eastman (TE) process shows that this hybrid technology is able to: (i) eliminate chemical process noise and redundant process variables simultaneously, (ii) combine historical pseudo label confidence with future pseudo label confidence to improve the identification accuracy of abnormal working conditions, (iii) efficiently select and diagnose high entropy unlabeled process data, and (iv) fully utilize unlabeled data to enhance the identification performance. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
A Hybrid Framework for Simultaneous Process and Solvent Optimization of Continuous Anti-Solvent Crystallization with Distillation for Solvent Recycling
Processes 2020, 8(1), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010063 - 02 Jan 2020
Viewed by 1205
Abstract
Anti-solvent crystallization is frequently applied in pharmaceutical processes for the separation and purification of intermediate compounds and active ingredients. The selection of optimal solvent types is important to improve the economic performance and sustainability of the process, but is challenged by the discrete [...] Read more.
Anti-solvent crystallization is frequently applied in pharmaceutical processes for the separation and purification of intermediate compounds and active ingredients. The selection of optimal solvent types is important to improve the economic performance and sustainability of the process, but is challenged by the discrete nature and large number of possible solvent combinations and the inherent relations between solvent selection and optimal process design. A computational framework is presented for the simultaneous solvent selection and optimization for a continuous process involving crystallization and distillation for recycling of the anti-solvent. The method is based on the perturbed-chain statistical associated fluid theory (PC-SAFT) equation of state to predict relevant thermodynamic properties of mixtures within the process. Alternative process configurations were represented by a superstructure. Due to the high nonlinearity of the thermodynamic models and rigorous models for distillation, the resulting mixed-integer nonlinear programming (MINLP) problem is difficult to solve by state-of-the-art solvers. Therefore, a continuous mapping method was adopted to relax the integer variables related to solvent selection, which makes the scale of the problem formulation independent of the number of solvents under consideration. Furthermore, a genetic algorithm was used to optimize the integer variables related to the superstructure. The hybrid stochastic and deterministic optimization framework converts the original MINLP problem into a nonlinear programming (NLP) problem, which is computationally more tractable. The successful application of the proposed method was demonstrated by a case study on the continuous anti-solvent crystallization of paracetamol. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Triple-Mode Model Predictive Control Using Future Target Information
Processes 2020, 8(1), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010054 - 02 Jan 2020
Viewed by 752
Abstract
In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) [...] Read more.
In this paper, we propose a triple-mode model predictive control (MPC) algorithm that uses future target information to improve tracking performance. To explicitly take into account the future target information in the MPC optimization, the proposed triple-mode control law encompasses three parts: (i) the future target information feedforward, (ii) the output feedback, and (iii) the extra degrees of freedom for constraint satisfaction. The first two parts of the control law are off-line designed through unconstrained MPC, and the optimal future trajectory horizon is obtained by golden section search based on the integral of squared error (ISE) criterion. The final part is calculated by the on-line MPC algorithm aiming to satisfy constraints. Furthermore, we analyze the feasibility and convergence properties of the proposed algorithm. The method is demonstrated by the simulation of the shell fundamental control problem and also tested on the coordinated control problem in the power plant. The test results show that the proposed algorithm can increase tracking performance dramatically due to the proper selection of future trajectory horizon. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Modeling and Simulation of Reaction and Fractionation Systems for the Industrial Residue Hydrotreating Process
Processes 2020, 8(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010032 - 27 Dec 2019
Cited by 3 | Viewed by 1392
Abstract
The residue hydrotreating process plays a significant role in the petroleum refining industry. In this process, modeling and simulation have critical importance for process development, control, and optimization. However, there is a lack of relevant reports of plant scale due to complexity in [...] Read more.
The residue hydrotreating process plays a significant role in the petroleum refining industry. In this process, modeling and simulation have critical importance for process development, control, and optimization. However, there is a lack of relevant reports of plant scale due to complexity in characterizing feedstock and determining reaction mechanisms. In this paper, reaction and fractionation models are constructed and simulated for a real-life industrial residue hydrotreating process based on Aspen HYSYS/Refining. Considering the heavier and inferior residue, analytical characterization is carried out for feedstock characterization based on laboratory analysis data. Moreover, two reactor models with parallel structures are proposed to implement the intricate reaction network, namely, a hydrocracker reactor and a plug flow reactor. The former simulates lighter petroleum hydrotreating based on the built-in reaction network. The latter emulates the conversion of a peculiar, heavier resin and asphaltene, using a six-lump model, which expands the scope of the feedstock and improves the accuracy of the model. To obtain a realistic simulation of fractionation, the database-based delumping method is adopted to model it with proper pseudo-components. The simulation results, including temperature rise, hydrogen consumption, temperature distribution, product yield, product properties, indicate that the model is capable of reflecting the realistic process accurately. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
Processes 2020, 8(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010023 - 23 Dec 2019
Viewed by 1408
Abstract
Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches [...] Read more.
Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Then the fingerprint image generation program was applied for structuring the complex instrumental data. Food olfactometry result was obtained by a machine learning method based on CNN using fingerprint image as the input. The case study on peanut oil samples demonstrated the model accuracy of around 93%. By structure optimization and further dataset expansion, the whole process has the potential to be utilized by sensory laboratories for aroma analysis instead of humans. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Process Modeling, Optimization, and Heat Integration of Ethanol Reforming Process for Syngas Production with High H2/CO Ratio
Processes 2019, 7(12), 960; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7120960 - 16 Dec 2019
Cited by 4 | Viewed by 1258
Abstract
The process modeling, parameter optimization, and heat integration of reforming ethanol to hydrogen is conducted in this paper. Modeling results show that the optimum reaction pressure for ethanol steam reforming is 1 bar. When the 7.4:1 is selected as a moderate water/ethanol ratio, [...] Read more.
The process modeling, parameter optimization, and heat integration of reforming ethanol to hydrogen is conducted in this paper. Modeling results show that the optimum reaction pressure for ethanol steam reforming is 1 bar. When the 7.4:1 is selected as a moderate water/ethanol ratio, the optimum reaction temperature is about 755 °C. As for heat integration, the composite curve and optimum heat-exchange network are given out by pinch technology, of which adding a heat exchanger can reduce 10,833 kW of heating duty and 10,833 kW of cooling duty and make the energy saving reach about 57.4%. Another two heat-integration plans are proposed for the ethanol steam-reforming process, to further decrease the high-level heat duty. Finally, similar heat integration was also carried out for the oxidative steam reforming, and the system is autothermal when the oxygen/ethanol is about 0.5:1 on the basis of above steam-reforming process, while the hydrogen molar purity is decreased from 69% to 66%. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
A Novel Hybrid Optimization Scheme on Connectivity Restoration Processes for Large Scale Industrial Wireless Sensor and Actuator Networks
Processes 2019, 7(12), 939; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7120939 - 10 Dec 2019
Cited by 5 | Viewed by 936
Abstract
In the wireless sensor and actuator networks (WSANs) of industrial field monitoring, maintaining network connectivity with coverage perception plays a decisive role in many industrial process scenarios. The mobile actuator node is responsible for collecting data from the sensing nodes and performing diverse [...] Read more.
In the wireless sensor and actuator networks (WSANs) of industrial field monitoring, maintaining network connectivity with coverage perception plays a decisive role in many industrial process scenarios. The mobile actuator node is responsible for collecting data from the sensing nodes and performing diverse specific collaborative operation tasks. However, the failure of the nodes usually causes coverage vulnerability and partition of the network. Urgent and time-sensitive applications expect a minimum coverage loss to complete an instant connectivity restoration. This paper presents a hybrid coverage perception-based connectivity restoration algorithm, which is designed to restore network connectivity with minimal coverage area loss. The algorithm uses a backup node, which is selected nearby the critical node, to ensure a timely restoration when the critical node encounters failure. In the process of backup node migration, the optimal destination will be reselected to maintain the best network coverage after network connectivity recovery. The effectiveness of the proposed algorithm was verified by some simulation experiments. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Comparison of Temperature Control and Temperature Difference Control for a Kaibel Dividing Wall Column
Processes 2019, 7(10), 773; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100773 - 21 Oct 2019
Cited by 5 | Viewed by 1144
Abstract
A dividing wall column (DWC) effectively intensifies the distillation process with a reduced energy consumption, capital investment, and space. The three-product DWC has been investigated intensively and extensively; however, the four-product Kaibel DWC has received scarce attention. This study aimed to propose feasible [...] Read more.
A dividing wall column (DWC) effectively intensifies the distillation process with a reduced energy consumption, capital investment, and space. The three-product DWC has been investigated intensively and extensively; however, the four-product Kaibel DWC has received scarce attention. This study aimed to propose feasible control structures for the Kaibel DWC using only temperature sensors in order to promote its industrialization. Two temperature control structures, two temperature difference control structures, and two double temperature difference control structures were studied. The feasibility of the six proposed control structures was verified with a wide variety of feed disturbances. In most cases, temperature difference control was better than temperature control to maintain product purities. The dynamic performances proved that the inserted feed disturbances were handled well. These results help to promote the industrialization of the Kaibel DWC. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Data-Driven Robust Optimization for Steam Systems in Ethylene Plants under Uncertainty
Processes 2019, 7(10), 744; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100744 - 15 Oct 2019
Cited by 5 | Viewed by 1050
Abstract
In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device [...] Read more.
In an ethylene plant, steam system provides shaft power to compressors and pumps and heats the process streams. Modeling and optimization of a steam system is a powerful tool to bring benefits and save energy for ethylene plants. However, the uncertainty of device efficiencies and the fluctuation of the process demands cause great difficulties to traditional mathematical programming methods, which could result in suboptimal or infeasible solution. The growing data-driven optimization approaches offer new techniques to eliminate uncertainty in the process system engineering community. A data-driven robust optimization (DDRO) methodology is proposed to deal with uncertainty in the optimization of steam system in an ethylene plant. A hybrid model of extraction–exhausting steam turbine is developed, and its coefficients are considered as uncertain parameters. A deterministic mixed integer linear programming model of the steam system is formulated based on the model of the components to minimize the operating cost of the ethylene plant. The uncertain parameter set of the proposed model is derived from the historical data, and the Dirichlet process mixture model is employed to capture the features for the construction of the uncertainty set. In combination with the derived uncertainty set, a data-driven conic quadratic mixed-integer programming model is reformulated for the optimization of the steam system under uncertainty. An actual case study is utilized to validate the performance of the proposed DDRO method. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Comparison of the Economy and Controllability of Pressure Swing Distillation with Two Energy-Saving Modes for Separating a Binary Azeotrope Containing Lower Alcohols
Processes 2019, 7(10), 730; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7100730 - 12 Oct 2019
Cited by 1 | Viewed by 784
Abstract
The pressure swing distillation (PSD) with two different energy-saving modes are put forward to separate a binary azeotrope containing lower alcohols: benzene/methanol. A comparison of the economy and controllability for the partial and fully heat integrated pressure swing distillation (HIPSD) is made by [...] Read more.
The pressure swing distillation (PSD) with two different energy-saving modes are put forward to separate a binary azeotrope containing lower alcohols: benzene/methanol. A comparison of the economy and controllability for the partial and fully heat integrated pressure swing distillation (HIPSD) is made by detailed simulation analysis. The optimal operating parameters of partial and fully HIPSD processes are obtained by minimizing total annual cost (TAC). These results show that the fully HIPSD mode saves 5.88% TAC compared with the partial HIPSD mode. Meanwhile, this paper proposes that the composition slope profile can help to select the temperature control stage (TCS), when the temperature profile in the column is rising rapidly near the bottom and the maximum of temperature slope value occurs in the bottom of the column. Several control structures are developed to check the rationality of the selection of the TCS and evaluate the industrial application. These results illustrate the composition/temperature cascade control structure for the PSD with two energy-saving modes can both get good control performances, and the purities of benzene and methanol can be brought close back to the initial value. However, the fully HIPSD mode can only handle much smaller composition disturbances (<10%) compared with the partial HIPSD mode. Therefore, the selection of energy-saving modes for the separation process should weigh economy against controllability. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Utility Paths Combination in HEN for Energy Saving and CO2 Emission Reduction
Processes 2019, 7(7), 425; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7070425 - 04 Jul 2019
Cited by 4 | Viewed by 1344
Abstract
Energy demand and flue gas emissions, namely carbon dioxide (CO2) associated with the industrial revolution have exhibited a continuous rise. Several approaches were introduced recently to mitigate energy consumption and CO2 emissions by either grass root design or retrofit of [...] Read more.
Energy demand and flue gas emissions, namely carbon dioxide (CO2) associated with the industrial revolution have exhibited a continuous rise. Several approaches were introduced recently to mitigate energy consumption and CO2 emissions by either grass root design or retrofit of existing heat exchanger networks (HEN) in chemical process plants. In this work, a combinatorial approach of path combination is used to generate several options for heat recovery enhancement in HEN. The options are applied to successively shift heat load from HEN utilities using combined utility paths at different heat recovery approach temperature (HRAT) considering exchangers pressure drop. Industrial case study for HEN of the preheat train in crude oil distillation unit from the literature is used to demonstrate the approach. The obtained results have been studied economically using the cost targeting of Pinch Technology. As a result, both external energy usage and CO2 emissions have been reduced from a heater device in HEN by 20% and 17%, respectively, with a payback of less than one year. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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Article
Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving
Processes 2019, 7(6), 334; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7060334 - 01 Jun 2019
Viewed by 1184
Abstract
The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a [...] Read more.
The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform. Full article
(This article belongs to the Special Issue Process Optimization and Control)
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