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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: closed (15 December 2021) | Viewed by 25071

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Special Issue Editors

Prof. Dr. Xi Chen

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College of Control Science and Engineering, Zhejiang University, 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

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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

Dr. Zuhua Xu

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College of Control Science and Engineering, Zhejiang University, Zheda Road 38, Hangzhou 310027, China
Interests: advanced process control; system identification; model predictive control; iterative learning control

Dr. Anjan K. Tula

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Guest Editor

College of Control Science and Engineering, Zhejiang University, Zheda Road 38, Hangzhou 310027, China
Interests: process systems engineering; systems tools and integration; integrated process–product design

Special Issue Information

Dear Colleagues,

The process industry has a significant impact on the sustainable development of economy. Process optimization and advanced control strategies have been supporting the development of the process industry for decades. Traditionally, optimization and control theory has been derived from model-based design. However, big data analytics and IoT are fast becoming the biggest technological revolutions the world has ever seen, and the process industry is no exception. Here, the high volume of data collected by thousands of IoT devices should be handled and analyzed to take process-related decisions. This explosion of data requires an intesection of disciplines such as machine learning, optimization, and control theory. There is a wonderful opportunity to improve and expand both the control theory and application in a more data-driven fashion. As process industry is the cornerstone of the manufacturing industry, the broader impact to the industry from this hybrid approach of model and data based is expected to be incredible and profound. Therefore, this Special Issue will provide a good opportunity for researchers all over the world to discuss and find a way toward a sustainable future.

This Special Issue on “Learning for Process Optimization and Control” aims to curate novel advances in the development and application of intelligent optimization and control to address longstanding challenges in industrial process. The authors of certain papers from the Chinese Process System Engineering Conference (PSE2020) held in Chongqing on 13-15 November will be recommended to extend and submit them to this Special Issue. This Special Issue solicits high-quality papers internationally. Topics include but are not limited to:

Artificial-intelligence-driven modeling and optimization technology;
Integrated artificial intelligence and advanced control technology;
Cloud computing and process decision-making systems;
Big data and process modeling and simulation;
Industrial process safety monitoring and faulty detection systems;
Industrial model predictive control system and application;
Process instruments and smart devices;
Process smart manufacturing systems.

Prof. Dr. Xi Chen
Prof. Dr. Wenli Du
Dr. Zuhua Xu
Dr. Anjan K. Tula
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 optimization
advanced control
machine learning
big data
artificial intelligence

Published Papers (10 papers)

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Research

15 pages, 646 KiB

Open AccessArticle

A Parallel Processing Approach to Dynamic Simulation of Ethylbenzene Process

by Junkai Zhang, Zhongqi Liu, Zengzhi Du and Jianhong Wang

Processes 2021, 9(8), 1386; https://doi.org/10.3390/pr9081386 - 10 Aug 2021

Viewed by 1881

Abstract

Parallel computing has been developed for many years in chemical process simulation. However, existing research on parallel computing in dynamic simulation cannot take full advantage of computer performance. More and more applications of data-driven methods and increasing complexity in chemical processes need faster dynamic simulators. In this research, we discuss the upper limit of speed-up for dynamic simulation of the chemical process. Then we design a parallel program considering the process model solving sequence and rewrite the General dynamic simulation & optimization system (DSO) with two levels of parallelism, multithreading parallelism and vectorized parallelism. The dependency between subtasks and the characteristic of the hottest subroutines are analyzed. Finally, the accelerating effect of the parallel simulator is tested based on a 500

kt \cdot a^{- 1}

ethylbenzene process simulation. A 5-hour process simulation shows that the highest speed-up ratio to the original program is 261%, and the simulation finished in 70.98 s wall clock time. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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19 pages, 2917 KiB

Open AccessArticle

A Lagrange Relaxation Based Decomposition Algorithm for Large-Scale Offshore Oil Production Planning Optimization

by Xiaoyong Gao, Yue Zhao, Yuhong Wang, Xin Zuo and Tao Chen

Processes 2021, 9(7), 1257; https://doi.org/10.3390/pr9071257 - 20 Jul 2021

Cited by 2 | Viewed by 2707

Abstract

In this paper, a new Lagrange relaxation based decomposition algorithm for the integrated offshore oil production planning optimization is presented. In our previous study (Gao et al. Computers and Chemical Engineering, 2020, 133, 106674), a multiperiod mixed-integer nonlinear programming (MINLP) model considering both well operation and flow assurance simultaneously had been proposed. However, due to the large-scale nature of the problem, i.e., too many oil wells and long planning time cycle, the optimization problem makes it difficult to get a satisfactory solution in a reasonable time. As an effective method, Lagrange relaxation based decomposition algorithms can provide more compact bounds and thus result in a smaller duality gap. Specifically, Lagrange multiplier is introduced to relax coupling constraints of multi-batch units and thus some moderate scale sub-problems result. Moreover, dual problem is constructed for iteration. As a result, the original integrated large-scale model is decomposed into several single-batch subproblems and solved simultaneously by commercial solvers. Computational results show that the proposed method can reduce the solving time up to 43% or even more. Meanwhile, the planning results are close to those obtained by the original model. Moreover, the larger the problem size, the better the proposed LR algorithm is than the original model. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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12 pages, 2865 KiB

Open AccessArticle

New Design Method of Solid Propellant Grain Using Machine Learning

by Seok-Hwan Oh, Hyoung Jin Lee and Tae-Seong Roh

Processes 2021, 9(6), 910; https://doi.org/10.3390/pr9060910 - 21 May 2021

Cited by 3 | Viewed by 3610

Abstract

The correlation between solid propellant grain configuration and burning surface area profile is a complicated nonlinear problem. Nonlinear optimization has been adopted to design grain configurations that satisfied the objective area profiles. However, as conventional design methods are impractical, with limited performance, it is necessary to investigate alternatives. Useful information for grain design can be obtained by analyzing the aforementioned correlation. However, this aspect has not been studied owing to the requirement of large amounts of data and analysis techniques. In this study, machine learning was used to develop a new design method. The objective of machine learning was to train a model to classify classes of data. The database stores various sets of configuration variables and their classes. The proposed Gaussian kernel-based support vector machine model predicts the class of newly designed grains. The results verified that the model accurately predicted the class of the set of configuration variables and can be used to modify the set of configuration variables to satisfy the requirement. Thus, it was confirmed that machine learning is an appropriate approach to grain design; however, further research is needed to analyze its practicality. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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24 pages, 5546 KiB

Open AccessArticle

A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators

by Xiangsong Kong and Dongbin Zheng

Processes 2021, 9(5), 770; https://doi.org/10.3390/pr9050770 - 28 Apr 2021

Cited by 6 | Viewed by 1528

Abstract

Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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25 pages, 6873 KiB

Open AccessArticle

Dynamic Modelling and Simulation of a Multistage Flash Desalination System

by Qiu-Yun Huang, Ai-Peng Jiang, Han-Yu Zhang, Jian Wang, Yu-Dong Xia and Lu He

Processes 2021, 9(3), 522; https://doi.org/10.3390/pr9030522 - 13 Mar 2021

Cited by 8 | Viewed by 2698

Abstract

As the leading thermal desalination method, multistage flash (MSF) desalination plays an important role in obtaining freshwater. Its dynamic modeling and dynamic performance prediction are quite important for the optimal control, real-time optimal operation, maintenance, and fault diagnosis of MSF plants. In this study, a detailed mathematical model of the MSF system, based on the first principle and its treatment strategy, was established to obtain transient performance change quickly. Firstly, the whole MSF system was divided into four parts, which are brine heat exchanger, flashing stage room, mixed and split modulate, and physical parameter modulate. Secondly, based on mass, energy, and momentum conservation laws, the dynamic correlation equations were formulated and then put together for a simultaneous solution. Next, with the established model, the performance of a brine-recirculation (BR)-MSF plant with 16-stage flash chambers was simulated and compared for validation. Finally, with the validated model and the simultaneous solution method, dynamic simulation and analysis were carried out to respond to the dynamic change of feed seawater temperature, feed seawater concentration, recycle stream mass flow rate, and steam temperature. The dynamic response curves of TBT (top brine temperature), BBT (bottom brine temperature), the temperature of flashing brine at previous stages, and distillate mass flow rate at previous stages were obtained, which specifically reflect the dynamic characteristics of the system. The presented dynamic model and its treatment can provide better analysis for the real-time optimal operation and control of the MSF system to achieve lower operational cost and more stable freshwater quality. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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19 pages, 2120 KiB

Open AccessFeature PaperArticle

Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

by Fumitake Fujii, Akinori Kaneishi, Takafumi Nii, Ryu’ichiro Maenishi and Soma Tanaka

Processes 2021, 9(3), 487; https://doi.org/10.3390/pr9030487 - 08 Mar 2021

Cited by 4 | Viewed by 1899

Abstract

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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20 pages, 8757 KiB

Open AccessArticle

Early Warning of Internal Leakage in Heat Exchanger Network Based on Dynamic Mechanism Model and Long Short-Term Memory Method

by Wende Tian, Nan Liu, Dongwu Sui, Zhe Cui, Zijian Liu, Ji Wang, Hao Zou and Ya Zhao

Processes 2021, 9(2), 378; https://doi.org/10.3390/pr9020378 - 19 Feb 2021

Cited by 5 | Viewed by 2681

Abstract

In the process of butadiene rubber production, internal leakage occurs in heat exchangers due to excessive pressure difference. It leads to the considerable flow of organic matters into the circulating water system. Since these organic matters are volatile and prone to explode in the cold water tower, internal leakage is potentially dangerous for the enterprise. To prevent this phenomenon, a novel intelligent early warning and risk assessment method (DYN-EW-QRA) is proposed in this paper by combining dynamic simulations (DYN), long short-term memory (LSTM), and quantitative risk assessment (QRA). First, an original internal leakage mechanism model of a heat exchanger network is designed and simulated by DYN to obtain datasets. Second, the potential relationships between variables that have a direct impact on the hazards of the accident are deeply learned by LSTM to predict the internal leakage trends. Finally, the QRA method is used to analyze the range and destructive power of potential hazards. The results show that DYN-EW-QRA method has excellent performance. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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14 pages, 11473 KiB

Open AccessArticle

A Modified Expectation Maximization Approach for Process Data Rectification

by Weiwei Jiang, Rongqiang Li, Deshun Cao, Chuankun Li and Shaohui Tao

Processes 2021, 9(2), 270; https://doi.org/10.3390/pr9020270 - 30 Jan 2021

Cited by 1 | Viewed by 1920

Abstract

Process measurements are contaminated by random and/or gross measuring errors, which degenerates performances of data-based strategies for enhancing process performances, such as online optimization and advanced control. Many approaches have been proposed to reduce the influence of measuring errors, among which expectation maximization (EM) is a novel and parameter-free one proposed recently. In this study, we studied the EM approach in detail and argued that the original EM approach is not feasible to rectify measurements contaminated by persistent biases, which is a pitfall of the original EM approach. So, we propose a modified EM approach here to circumvent this pitfall by fixing the standard deviation of random error mode. The modified EM approach was evaluated by several benchmark cases of process data rectification from literatures. The results show advantages of the proposed approach to the original EM in solving efficiency and performance of data rectification. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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15 pages, 3167 KiB

Open AccessFeature PaperArticle

Fault Detection of Diesel Engine Air and after-Treatment Systems with High-Dimensional Data: A Novel Fault-Relevant Feature Selection Method

by Qilan Ran, Yedong Song, Wenli Du, Wei Du and Xin Peng

Processes 2021, 9(2), 259; https://doi.org/10.3390/pr9020259 - 29 Jan 2021

Cited by 5 | Viewed by 2885

Abstract

In order to reduce pollutants of the emission from diesel vehicles, complex after-treatment technologies have been proposed, which make the fault detection of diesel engines become increasingly difficult. Thus, this paper proposes a canonical correlation analysis detection method based on fault-relevant variables selected by an elitist genetic algorithm to realize high-dimensional data-driven faults detection of diesel engines. The method proposed establishes a fault detection model by the actual operation data to overcome the limitations of the traditional methods, merely based on benchmark. Moreover, the canonical correlation analysis is used to extract the strong correlation between variables, which constructs the residual vector to realize the fault detection of the diesel engine air and after-treatment system. In particular, the elitist genetic algorithm is used to optimize the fault-relevant variables to reduce detection redundancy, eliminate additional noise interference, and improve the detection rate of the specific fault. The experiments are carried out by implementing the practical state data of a diesel engine, which show the feasibility and efficiency of the proposed approach. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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17 pages, 2927 KiB

Open AccessArticle

A Fault Identification Method in Distillation Process Based on Dynamic Mechanism Analysis and Signed Directed Graph

by Wende Tian, Shifa Zhang, Zhe Cui, Zijian Liu, Shaochen Wang, Ya Zhao and Hao Zou

Processes 2021, 9(2), 229; https://doi.org/10.3390/pr9020229 - 26 Jan 2021

Cited by 8 | Viewed by 2002

Abstract

Due to the complexity of materials and energy cycles, the distillation system has numerous working conditions difficult to troubleshoot in time. To address the problem, a novel DMA-SDG fault identification method that combines dynamic mechanism analysis based on process simulation and signed directed graph is proposed for the distillation process. Firstly, dynamic simulation is employed to build a mechanism model to provide the potential relationships between variables. Secondly, sensitivity analysis and dynamic mechanism analysis in process simulation are introduced to the SDG model to improve the completeness of this model based on expert knowledge. Finally, a quantitative analysis based on complex network theory is used to select the most important nodes in SDG model for identifying the severe malfunctions. The application of DMA-SDG method in a benzene-toluene-xylene (BTX) hydrogenation prefractionation system shows sound fault identification performance. Full article

(This article belongs to the Special Issue Learning for Process Optimization and Control)

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