Processes

6 pages, 196 KiB

Open AccessEditorial

Special Issue on “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”

by Zhiwei Gao, Michael Z. Q. Chen and Dapeng Zhang

Processes 2021, 9(4), 664; https://doi.org/10.3390/pr9040664 - 09 Apr 2021

Cited by 15 | Viewed by 1529

Complex industrial automation systems and processes, such as chemical processes, manufacturing systems, wireless network systems, power and energy systems, smart grids and so forth, have greatly contributed to our daily life [...] Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

19 pages, 2038 KiB

Open AccessFeature PaperReview

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

by Zhiwei Gao and Xiaoxu Liu

Processes 2021, 9(2), 300; https://doi.org/10.3390/pr9020300 - 05 Feb 2021

Cited by 169 | Viewed by 13637

Abstract

Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system [...] Read more.

Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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24 pages, 5037 KiB

Open AccessArticle

Controller Design and Control Structure Analysis for a Novel Oil–Water Multi-Pipe Separator

by Sveinung Johan Ohrem, Håvard Slettahjell Skjefstad, Milan Stanko and Christian Holden

Processes 2019, 7(4), 190; https://doi.org/10.3390/pr7040190 - 02 Apr 2019

Cited by 4 | Viewed by 3251

Abstract

To enable more efficient production of hydrocarbons on the seabed in waters where traditional separator equipment is infeasible, the offshore oil and gas industry is leaning towards more compact separation equipment. A novel multi-pipe separator concept, designed to meet the challenges of subsea [...] Read more.

To enable more efficient production of hydrocarbons on the seabed in waters where traditional separator equipment is infeasible, the offshore oil and gas industry is leaning towards more compact separation equipment. A novel multi-pipe separator concept, designed to meet the challenges of subsea separation, has been developed at the Department of Geoscience and Petroleum at the Norwegian University of Science and Technology. In this initial study, a control structure analysis for the novel separator concept, based on step-response experiments, is presented. Proportional-integral controllers and model reference adaptive controllers are designed for the different control loops. The proportional-integral controllers are tuned based on the well-established simple internal model control tuning rules. Both control methods are implemented and tested on a prototype of the separator concept. Different measurements are controlled, and results show that the performance of the separator under varying inlet conditions can be improved with proper selection of control inputs and measurements. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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17 pages, 944 KiB

Open AccessArticle

Monitoring the Process Based on Belief Statistic for Neutrosophic Gamma Distributed Product

by Muhammad Aslam, Rashad A. R. Bantan and Nasrullah Khan

Processes 2019, 7(4), 209; https://doi.org/10.3390/pr7040209 - 12 Apr 2019

Cited by 18 | Viewed by 2858

Abstract

In this paper, we developed a control chart methodology for the monitoring the mean time between two events using the belief estimator under the neutrosophic gamma distribution. The proposed control chart coefficients and the neutrosophic average run length (NARL) have been determined using [...] Read more.

In this paper, we developed a control chart methodology for the monitoring the mean time between two events using the belief estimator under the neutrosophic gamma distribution. The proposed control chart coefficients and the neutrosophic average run length (NARL) have been determined using different process settings. The performance of the proposed chart is compared with the control chart under classical statistics in terms of NARL using the simulation data and real example. From comparisons, it is concluded that the proposed chart is efficient, effective and adequate to be used under uncertainty environment than the chart under classical statistics. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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16 pages, 3210 KiB

Open AccessArticle

Daytime/Nighttime Event-Based PI Control for the pH of a Microalgae Raceway Reactor

by Enrique Rodríguez-Miranda, Manuel Beschi, José Luis Guzmán, Manuel Berenguel and Antonio Visioli

Processes 2019, 7(5), 247; https://doi.org/10.3390/pr7050247 - 28 Apr 2019

Cited by 16 | Viewed by 4167

Abstract

In this paper, a new solution to improve the traditional control operation of raceway microalgae reactors is presented. The control strategy is based on an event-based method that can be easily coupled to a classical time-driven proportional-integral controller, simplifying the design process approach. [...] Read more.

In this paper, a new solution to improve the traditional control operation of raceway microalgae reactors is presented. The control strategy is based on an event-based method that can be easily coupled to a classical time-driven proportional-integral controller, simplifying the design process approach. The results of a standard Proportional-Integral (PI) controller, as well as of two event-based architectures, are presented in simulation and compared with each other and with traditional On/Off control. It is demonstrated that the event-based PI controller—operating during the whole day instead of only during daytime—achieves a better performance by reducing the actuator effort and saving costs related to gas consumption. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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24 pages, 2422 KiB

Open AccessArticle

An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

by Zhonghua Han, Quan Zhang, Haibo Shi and Jingyuan Zhang

Processes 2019, 7(5), 302; https://doi.org/10.3390/pr7050302 - 21 May 2019

Cited by 20 | Viewed by 3201

Abstract

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop [...] Read more.

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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17 pages, 5836 KiB

Open AccessArticle

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

by Huazhen Cao, Tao Yu, Xiaoshun Zhang, Bo Yang and Yaxiong Wu

Processes 2019, 7(6), 321; https://doi.org/10.3390/pr7060321 - 31 May 2019

Cited by 9 | Viewed by 2668

Abstract

A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and [...] Read more.

A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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20 pages, 7729 KiB

Open AccessArticle

Determination of Holmquist–Johnson–Cook Constitutive Parameters of Coal: Laboratory Study and Numerical Simulation

by Beijing Xie, Zheng Yan, Yujing Du, Zeming Zhao and Xiaoqian Zhang

Processes 2019, 7(6), 386; https://doi.org/10.3390/pr7060386 - 21 Jun 2019

Cited by 22 | Viewed by 4706

Abstract

The main sensitivity parameters of the Holmquist–Johnson–Cook constitutive model for coal were obtained from a variety of tests such as uniaxial compression, uniaxial cyclic loading, splitting and triaxial compression tests, as well as the indirect derivation equation of a briquette. The mechanical properties [...] Read more.

The main sensitivity parameters of the Holmquist–Johnson–Cook constitutive model for coal were obtained from a variety of tests such as uniaxial compression, uniaxial cyclic loading, splitting and triaxial compression tests, as well as the indirect derivation equation of a briquette. The mechanical properties of briquettes under dynamic impact were investigated using a split Hopkinson pressure bar experiment. Based on the experimental measurement of the Holmquist–Johnson–Cook constitutive model, the numerical simulation of briquette was performed using ANSYS/LS-DYNA software. A comparison between experimental and simulation results verified the correctness of simulation parameters. This research concluded that the failure of briquette at different impact velocities started from an axial crack in the middle of the coal body, and the sample was swollen to some extent. By the increase of impact velocity, the severity of damage in the coal body was increased, while the size of the coal block was decreased. Moreover, there was good compliance between experimental and simulated stress wave curves in terms of coal sample failure and fracture morphology at different speeds. Finally, the parameters of the validated Holmquist–Johnson–Cook constitutive model were applied to the numerical simulation model of the impact damage of heading face and the process of coal seam damage in the roadway was visually displayed. The obtained results showed that the Holmquist–Johnson–Cook constitutive model parameters suitable for the prominent coal body were of great significance for the improvement and exploration of the occurrence mechanism of coal and rock dynamic disasters. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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13 pages, 1624 KiB

Open AccessArticle

Process Simulation of the Separation of Aqueous Acetonitrile Solution by Pressure Swing Distillation

by Jing Li, Keliang Wang, Minglei Lian, Zhi Li and Tingzhao Du

Processes 2019, 7(7), 409; https://doi.org/10.3390/pr7070409 - 01 Jul 2019

Cited by 13 | Viewed by 8463

Abstract

The separation of aqueous acetonitrile solution by pressure swing distillation (PSD) was simulated and optimized through Aspen Plus software. The distillation sequence of the low pressure column (LPC) and high pressure column (HPC) was determined with a phase diagram. The pressures of the [...] Read more.

The separation of aqueous acetonitrile solution by pressure swing distillation (PSD) was simulated and optimized through Aspen Plus software. The distillation sequence of the low pressure column (LPC) and high pressure column (HPC) was determined with a phase diagram. The pressures of the two columns were set to 1 and 4 atm, respectively. Total annual cost (TAC) was considered as the objective function, and design variables, such as the tray number, the reflux ratio, and the feeding position, were optimized. The optimum process parameters were obtained. For the reduction of energy consumption, the PSD with full-heat integration was designed. The TAC of this method is lower by 32.39% of that of the PSD without heat integration. Therefore, it is more economical to separate acetonitrile and water mixture by PSD with full-heat integration, which provides technical support for the separation design of such azeotropes. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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21 pages, 4028 KiB

Open AccessArticle

Distributed Model Predictive Control of Steam/Water Loop in Large Scale Ships

by Shiquan Zhao, Anca Maxim, Sheng Liu, Robin De Keyser and Clara M. Ionescu

Processes 2019, 7(7), 442; https://doi.org/10.3390/pr7070442 - 11 Jul 2019

Cited by 7 | Viewed by 3364

Abstract

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which [...] Read more.

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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21 pages, 5691 KiB

Open AccessArticle

Optimal Nonlinear Adaptive Control for Voltage Source Converters via Memetic Salp Swarm Algorithm: Design and Hardware Implementation

by Yueping Jiang, Xue Jin, Hui Wang, Yihao Fu, Weiliang Ge, Bo Yang and Tao Yu

Processes 2019, 7(8), 490; https://doi.org/10.3390/pr7080490 - 01 Aug 2019

Cited by 9 | Viewed by 3007

Abstract

Voltage source converter (VSC) has been extensively applied in renewable energy systems which can rapidly regulate the active and reactive power. This paper aims at developing a novel optimal nonlinear adaptive control (ONAC) scheme to control VSC in both rectifier mode and inverter [...] Read more.

Voltage source converter (VSC) has been extensively applied in renewable energy systems which can rapidly regulate the active and reactive power. This paper aims at developing a novel optimal nonlinear adaptive control (ONAC) scheme to control VSC in both rectifier mode and inverter mode. Firstly, the nonlinearities, parameter uncertainties, time-varying external disturbances, and unmodelled dynamics can be aggregated into a perturbation, which is then estimated by an extended state observer (ESO) called high-gain perturbation observer (HGPO) online. Moreover, the estimated perturbation will be fully compensated through state feedback. Besides, the observer gains and controller gains are optimally tuned by a recent emerging biology-based memetic salp swarm algorithm (MSSA), the utilization of such method can ensure a desirably satisfactory control performance. The advantage of ONAC is that even though the operation conditions are constantly changing, the control performance can still be maintained to be globally consistent. In addition, it is noteworthy that in rectifier mode only the reactive power and DC voltage are required to be measured, while in inverter mode merely the reactive power and active power have to be measured. At last, in order to verify the feasibility of ONAC in practical application, a hardware experiment is implemented. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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12 pages, 799 KiB

Open AccessArticle

Pole-Zero Cancellation Method for Multi Input Multi Output (MIMO) Temperature Control in Heating Process System

by Song Xu, Seiji Hashimoto and Wei Jiang

Processes 2019, 7(8), 497; https://doi.org/10.3390/pr7080497 - 01 Aug 2019

Cited by 10 | Viewed by 3903

Abstract

With the rapid development of industrial technology, the multi-point (multi-input multi-output) heating processing systems with integrated temperature control have been increasingly needed to achieve high-quality and high-performance processing. In this paper, in response to the demand for proper transient response and to provide [...] Read more.

With the rapid development of industrial technology, the multi-point (multi-input multi-output) heating processing systems with integrated temperature control have been increasingly needed to achieve high-quality and high-performance processing. In this paper, in response to the demand for proper transient response and to provide more accurate temperature controls, a novel pole-zero cancelation method is proposed for multi-input multi-output (MIMO) temperature control in heating process systems. In the proposed method, the temperature differences and transient characteristics of all points can be improved by compensating dead time difference and coupling effect together by matrix compensation and pole-zero cancelation with the feedforward reference model. Both simulations and experiments were carried out. The results were compared to the well-tuned conventional PI control system and PI plus decoupling compensation system to evaluate the control efficiency of the proposed method. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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12 pages, 921 KiB

Open AccessArticle

Slow Mode-Based Control Method for Multi-Point Temperature Control System

by Song Xu, Seiji Hashimoto, Wei Jiang, Yuqi Jiang, Katsutoshi Izaki, Takeshi Kihara and Ryota Ikeda

Processes 2019, 7(8), 533; https://doi.org/10.3390/pr7080533 - 14 Aug 2019

Cited by 6 | Viewed by 3859

Abstract

In recent years, thermal processing systems with integrated temperature control have been increasingly needed to achieve high quality and high performance. In this paper, responding to the growing demands for proper transient response and to provide more accurate temperature controls, a novel slow-mode-based [...] Read more.

In recent years, thermal processing systems with integrated temperature control have been increasingly needed to achieve high quality and high performance. In this paper, responding to the growing demands for proper transient response and to provide more accurate temperature controls, a novel slow-mode-based control (SMBC) method is proposed for multi-point temperature control systems. In the proposed method, the temperature differences and the transient response of all points can be controlled and improved by making the output of the fast modes follow that of the slow mode. Both simulations and experiments were carried out, and the results were compared to conventional control methods in order to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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30 pages, 8362 KiB

Open AccessArticle

Study on Three-Dimensional Stress Field of Gob-Side Entry Retaining by Roof Cutting without Pillar under Near-Group Coal Seam Mining

by Xiaoming Sun, Yangyang Liu, Junwei Wang, Jiangbing Li, Shijie Sun and Xuebin Cui

Processes 2019, 7(9), 552; https://doi.org/10.3390/pr7090552 - 22 Aug 2019

Cited by 25 | Viewed by 3138

Abstract

In order to explore the distribution law of stress field under the mining mode of gob-side entry retaining by roof cutting without pillar (GERRCP) under goaf, based on the engineering background of 8102 and 9101 working faces in Xiashanmao coal mine, the stress [...] Read more.

In order to explore the distribution law of stress field under the mining mode of gob-side entry retaining by roof cutting without pillar (GERRCP) under goaf, based on the engineering background of 8102 and 9101 working faces in Xiashanmao coal mine, the stress field distribution of GERRCP and traditional remaining pillar was studied by means of theoretical analysis and numerical simulation. The simulation results showed that: (1) in the front of the working face, the vertical peak stress of non-pillar mining was smaller than that of the remaining pillar mining, and it could effectively control stress concentration in surrounding rock of the mining roadway; the trend of horizontal stress distribution of the two was the same, and the area, span and peak stress of stress the rise zone were the largest in large pillar mining and the minimum in non-pillar mining. (2) On the left side of the working face, the vertical stress presented increasing-decreasing characteristics under non-pillar mining mode and saddle-shaped distribution characteristics under the remaining pillar mining mode respectively. Among them, the peak stress was the smallest under non-pillar mining, and compared with the mining of the large pillar and small pillar, non-pillar mining decreased by 12–21% and 3–10% respectively. The position of peak stress of the former was closer to the mining roadway, indicating that the width of the plastic zone of the surrounding rock of the non-pillar mining was smaller and bearing capacity was higher. In the mining of the large and small pillar, the horizontal stress formed a high stress concentration in the pillar and 9102 working face respectively. In non-pillar mining, the horizontal stress concentration appeared in solid coal, but the concentration area was small. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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24 pages, 3659 KiB

Open AccessArticle

Grouping Method of Semiconductor Bonding Equipment Based on Clustering by Fast Search and Find of Density Peaks for Dynamic Matching According to Processing Tasks

by Zhijun Gao, Wen Si, Zhonghua Han, Jiayu Peng and Feng Qiao

Processes 2019, 7(9), 566; https://doi.org/10.3390/pr7090566 - 25 Aug 2019

Cited by 1 | Viewed by 2636

Abstract

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method [...] Read more.

Given that the equipment for the semiconductor packaging line adopts the fixed grouping production method, thus failing to dynamically match the processing task demand capacity, in the present study, we proposed a semiconductor bonding equipment-grouping method based on processing task matching. This method sets the device group closed position constraint and the matching constraint between the device type and the processing type and uses the graph theory method to establish the device grouping model. The dynamic grouping of equipment under the capacity demand of different processing tasks was achieved by changing the relationship matrix between devices. The drawback of this grouping method is rather large grouping deviation, which we tried to solve with the clustering by fast search and find of density peaks (CFSFDP) that was added to cluster the sets of attribute information of the devices so as to obtain the maximum number of grouping groups obtained to reduce the grouping deviation. Simulation comparison experiments were carried out under different circumstances considering the size of the formation, the distribution of demand capacity, and the coefficient of difference in demand capacity. Compared with the standard device grouping method, the grouping method based on semiconductor bonding equipment and CFSFDP algorithm for dynamic matching according to processing tasks had better performance in solving the dynamic grouping problem. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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17 pages, 9306 KiB

Open AccessArticle

Intelligent Control Strategy for Transient Response of a Variable Geometry Turbocharger System Based on Deep Reinforcement Learning

by Bo Hu, Jie Yang, Jiaxi Li, Shuang Li and Haitao Bai

Processes 2019, 7(9), 601; https://doi.org/10.3390/pr7090601 - 06 Sep 2019

Cited by 26 | Viewed by 5625

Abstract

Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of [...] Read more.

Deep reinforcement learning (DRL) is an area of machine learning that combines a deep learning approach and reinforcement learning (RL). However, there seem to be few studies that analyze the latest DRL algorithms on real-world powertrain control problems. Meanwhile, the boost control of a variable geometry turbocharger (VGT)-equipped diesel engine is difficult mainly due to its strong coupling with an exhaust gas recirculation (EGR) system and large lag, resulting from time delay and hysteresis between the input and output dynamics of the engine’s gas exchange system. In this context, one of the latest model-free DRL algorithms, the deep deterministic policy gradient (DDPG) algorithm, was built in this paper to develop and finally form a strategy to track the target boost pressure under transient driving cycles. Using a fine-tuned proportion integration differentiation (PID) controller as a benchmark, the results show that the control performance based on the proposed DDPG algorithm can achieve a good transient control performance from scratch by autonomously learning the interaction with the environment, without relying on model supervision or complete environment models. In addition, the proposed strategy is able to adapt to the changing environment and hardware aging over time by adaptively tuning the algorithm in a self-learning manner on-line, making it attractive to real plant control problems whose system consistency may not be strictly guaranteed and whose environment may change over time. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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23 pages, 2024 KiB

Open AccessArticle

Flexible Flow Shop Scheduling Method with Public Buffer

by Zhonghua Han, Chao Han, Shuo Lin, Xiaoting Dong and Haibo Shi

Processes 2019, 7(10), 681; https://doi.org/10.3390/pr7100681 - 01 Oct 2019

Cited by 10 | Viewed by 2129

Abstract

Actual manufacturing enterprises usually solve the production blockage problem by increasing the public buffer. However, the increase of the public buffer makes the flexible flow shop scheduling rather challenging. In order to solve the flexible flow shop scheduling problem with public buffer (FFSP–PB), [...] Read more.

Actual manufacturing enterprises usually solve the production blockage problem by increasing the public buffer. However, the increase of the public buffer makes the flexible flow shop scheduling rather challenging. In order to solve the flexible flow shop scheduling problem with public buffer (FFSP–PB), this study proposes a novel method combining the simulated annealing algorithm-based Hopfield neural network algorithm (SAA–HNN) and local scheduling rules. The SAA–HNN algorithm is used as the global optimization method, and constructs the energy function of FFSP–PB to apply its asymptotically stable characteristic. Due to the limitations, such as small search range and high probability of falling into local extremum, this algorithm introduces the simulated annealing algorithm idea such that the algorithm is able to accept poor fitness solution and further expand its search scope during asymptotic convergence. In the process of local scheduling, considering the transferring time of workpieces moving into and out of public buffer and the manufacturing state of workpieces in the production process, this study designed serval local scheduling rules to control the moving process of the workpieces between the public buffer and the limited buffer between the stages. These local scheduling rules can also be used to reduce the production blockage and improve the efficiency of the workpiece transfer. Evaluated by the groups of simulation schemes with the actual production data of one bus manufacturing enterprise, the proposed method outperforms other methods in terms of searching efficiency and optimization target. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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20 pages, 3651 KiB

Open AccessArticle

Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification

by Gaipin Cai, Xin Liu, Congcong Dai and Xiaoyan Luo

Processes 2019, 7(10), 725; https://doi.org/10.3390/pr7100725 - 11 Oct 2019

Cited by 6 | Viewed by 1925

Abstract

To overcome the difficulty of accurately determining the load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic [...] Read more.

To overcome the difficulty of accurately determining the load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic neural network (AEPSO_PNN) classification is proposed. First, the concept of a sliding frequency window is introduced based on EWT, and the adaptive frequency window EWT algorithm, which is used to decompose the vibration signals recorded under different load states to obtain the intrinsic mode components, is proposed. Second, a correlation coefficient threshold is used to select the sensitive mode components that characterize the state of the original signal for signal reconstruction. Finally, the MFE of the reconstructed signal is used as the characteristic vector to characterize the load state of the mill, and the partial mean value of MFE is calculated. The results show that the mean value of MFE under different load states varies. To further identify the load state, a characteristic mill load vector is constructed from the MFE values of the reconstructed signal under different load conditions and is used as the input of the AEPSO_PNN model, which then outputs the predicted ball mill load state. Thus, a load state identification model is established. The feasibility of the method is verified based on grinding experiments. The results show that the overall recognition rate of the proposed method is as high as 97.3%. Compared with the back propagation (BP) neural network, Bayes discriminant method, and PNN classification, AEPSO_PNN classification increases the overall recognition rate by 8%, 5.3%, and 3.3%, respectively, which indicates that this method can be used to accurately identify the different load states of a ball mill. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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15 pages, 3427 KiB

Open AccessArticle

Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems

by Mariam Ibrahim and Ahmad Alsheikh

Processes 2019, 7(11), 787; https://doi.org/10.3390/pr7110787 - 01 Nov 2019

Cited by 9 | Viewed by 2650

Abstract

Complex Engineering Systems are subject to cyber-attacks due to inherited vulnerabilities in the underlying entities constituting them. System Resiliency is determined by its ability to return to a normal state under attacks. In order to analyze the resiliency under various attacks compromising the [...] Read more.

Complex Engineering Systems are subject to cyber-attacks due to inherited vulnerabilities in the underlying entities constituting them. System Resiliency is determined by its ability to return to a normal state under attacks. In order to analyze the resiliency under various attacks compromising the system, a new concept of Hybrid Attack Graph (HAG) is introduced. A HAG is a graph that captures the evolution of both logical and real values of system parameters under attack and recovery actions. The HAG is generated automatically and visualized using Java based tools. The results are illustrated through a communication network example. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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22 pages, 2321 KiB

Open AccessArticle

Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

by Wei Liu, Fengming Luo, Yuanhong Liu and Wei Ding

Processes 2019, 7(12), 955; https://doi.org/10.3390/pr7120955 - 13 Dec 2019

Cited by 24 | Viewed by 2898

Abstract

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective [...] Read more.

With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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19 pages, 4117 KiB

Open AccessFeature PaperArticle

Improvement of Refrigeration Efficiency by Combining Reinforcement Learning with a Coarse Model

by Dapeng Zhang and Zhiwei Gao

Processes 2019, 7(12), 967; https://doi.org/10.3390/pr7120967 - 17 Dec 2019

Cited by 15 | Viewed by 2805

Abstract

It is paramount to improve operational conversion efficiency in air-conditioning refrigeration. It is noticed that control efficiency for model-based methods highly relies on the accuracy of the mechanism model, and data-driven methods would face challenges using the limited collected data to identify the [...] Read more.

It is paramount to improve operational conversion efficiency in air-conditioning refrigeration. It is noticed that control efficiency for model-based methods highly relies on the accuracy of the mechanism model, and data-driven methods would face challenges using the limited collected data to identify the information beyond. In this study, a hybrid novel approach is presented, which is to integrate a data-driven method with a coarse model. Specifically, reinforcement learning is used to exploit/explore the conversion efficiency of the refrigeration, and a coarse model is utilized to evaluate the reward, by which the requirement of the model accuracy is reduced and the model information is better used. The proposed approach is implemented based on a hierarchical control strategy which is divided into a process level and a loop level. The simulation of a test bed shows the proposed approach can achieve better conversion efficiency of refrigeration than the conventional methods. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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18 pages, 3501 KiB

Open AccessArticle

Supporting Design Optimization of Tunnel Boring Machines-Excavated Coal Mine Roadways: A Case Study in Zhangji, China

by Bin Tang, Hua Cheng, Yongzhi Tang, Tenglong Zheng, Zhishu Yao, Chuanbing Wang and Chuanxin Rong

Processes 2020, 8(1), 46; https://doi.org/10.3390/pr8010046 - 01 Jan 2020

Cited by 17 | Viewed by 3478

Abstract

Tunnel Boring Machines (TBMs) are a cutting-edge excavating equipment, but are barely applied in underground coal mines. For TBM excavation projects involving the Zhangji coal mine, the surrounding rock properties, stress field, cross section geometry, as well as the excavation-induced stress path of [...] Read more.

Tunnel Boring Machines (TBMs) are a cutting-edge excavating equipment, but are barely applied in underground coal mines. For TBM excavation projects involving the Zhangji coal mine, the surrounding rock properties, stress field, cross section geometry, as well as the excavation-induced stress path of TBM-excavated coal mine roadways are different from those of traditional tunnels or roadways. Consequently, traditional roadway supporting technologies and experiences cannot be relied on for this project. In order to research an appropriate supporting pattern for a TBM-excavated coal mine roadway, first of all, the constitutive model of roadway surrounding rocks was derived, and a rock failure criterion was proposed based on rock mechanical tests. Secondly, a three-dimension finite element model was established and computer simulations under three different supporting patterns were conducted. Stress redistribution, roadway convergence, and excavation damage zone ranges of surrounding rocks under three different support patterns were analyzed and an optimal support design of the TBM-excavated roadway was made based on simulation results. During roadway excavation, convergence gauge and rock bolt dynamometers were installed for monitoring roadway convergence and the axial forces of rock bolts. The in-situ monitoring results verified the validity of roadway supporting designs. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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16 pages, 2933 KiB

Open AccessArticle

Community-Based Link-Addition Strategies for Mitigating Cascading Failures in Modern Power Systems

by Po Hu and Lily Lee

Processes 2020, 8(2), 126; https://doi.org/10.3390/pr8020126 - 21 Jan 2020

Cited by 5 | Viewed by 2245

Abstract

The propagation of cascading failures of modern power systems is mainly constrained by the network topology and system parameter. In order to alleviate the cascading failure impacts, it is necessary to adjust the original network topology considering the geographical factors, construction costs and [...] Read more.

The propagation of cascading failures of modern power systems is mainly constrained by the network topology and system parameter. In order to alleviate the cascading failure impacts, it is necessary to adjust the original network topology considering the geographical factors, construction costs and requirements of engineering practice. Based on the complex network theory, the power system is modeled as a directed graph. The graph is divided into communities based on the Fast–Newman algorithm, where each community contains at least one generator node. Combined with the islanding characteristics and the node vulnerability, three low-degree-node-based link-addition strategies are proposed to optimize the original topology. A new evaluation index combining with the attack difficulty and the island ratio is proposed to measure the impacts on the network under sequential attacks. From the analysis of the experimental results of three attack scenarios, this study adopts the proposed strategies to enhance the network connectivity and improve the robustness to some extent. It is therefore helpful to guide the power system cascading failure mitigation strategies and network optimization planning. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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14 pages, 5410 KiB

Open AccessArticle

Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process

by Zhaohui Tang, Liyong Tang, Guoyong Zhang, Yongfang Xie and Jinping Liu

Processes 2020, 8(5), 536; https://doi.org/10.3390/pr8050536 - 03 May 2020

Cited by 9 | Viewed by 2490

Abstract

It is well known that the change of the reagent dosage during the flotation process will cause the froth image to change continuously with time. Therefore, an intelligent setting method based on the time series froth image in the zinc flotation process is [...] Read more.

It is well known that the change of the reagent dosage during the flotation process will cause the froth image to change continuously with time. Therefore, an intelligent setting method based on the time series froth image in the zinc flotation process is proposed. Firstly, the sigmoid kernel function is used to estimate the cumulative distribution function of bubble size, and the cumulative distribution function shape is characterized by sigmoid kernel function parameters. Since the reagent will affect the froth image over a period of time, the time series of bubble size cumulative distribution function is processed by the ELMo model and the dynamic feature vectors are output. Finally, XGBoost is used to establish the nonlinear relationship modeling between reagent dosage and dynamic feature vectors. Industrial experiments have proved the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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32 pages, 6973 KiB

Open AccessFeature PaperArticle

Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

by Yichuan Fu, Zhiwei Gao, Yuanhong Liu, Aihua Zhang and Xiuxia Yin

Processes 2020, 8(9), 1066; https://doi.org/10.3390/pr8091066 - 01 Sep 2020

Cited by 53 | Viewed by 5111

Abstract

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are [...] Read more.

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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18 pages, 5080 KiB

Open AccessFeature PaperArticle

Position Deviation Control of Drilling Machine Using a Nonlinear Adaptive Backstepping Controller Based on a Disturbance Observer

by Huifu Ji and Songyong Liu

Processes 2021, 9(2), 237; https://doi.org/10.3390/pr9020237 - 27 Jan 2021

Cited by 4 | Viewed by 2911

Abstract

Thin coal seam mining is a development direction to solve the problem of energy supply at this stage, which cannot be realized by small working space, low automation, and drilling deviation. In this paper, a nonlinear adaptive backstepping controller based on a disturbance [...] Read more.

Thin coal seam mining is a development direction to solve the problem of energy supply at this stage, which cannot be realized by small working space, low automation, and drilling deviation. In this paper, a nonlinear adaptive backstepping controller based on a disturbance observer is proposed and used on a coal auger for position tracking control to achieve directional drilling. Firstly, a nonlinear dynamic model for the deflection control mechanism is built with the consideration of parameter uncertainties and external disturbances. Then, the parameter uncertainty and external disturbance are regarded as a system compound disturbance. Furthermore, a disturbance observer is designed to estimate the system compound disturbance and a nonlinear adaptive backstepping controller was proposed to compensate the system compound disturbance. The upper bound of the compound disturbance, which can effectively reduce the chattering in the directional control process, cannot be obtained easily. A stability analysis of the DCM (deviation control mechanism) with the proposed controller is proved based on the Lyapunov theory. Finally, an electro-hydraulic servo displacement control experimental system with matlab xPC target rapid prototyping technology and a prototype experiment system is established to verify the effectiveness of the proposed control strategy. The experimental results indicate that the proposed controller can yield more satisfactory position tracking performance, such as parameter uncertainties and external disturbances, than the conventional proportion integral derivative (PID) controller and an adaptive backstepping controller. Using the control strategy, technical breakthrough on horizontal directional drilling can be realized for thin coal seam mining. Full article

(This article belongs to the Special Issue Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes)

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