In this paper, a simplified and robust control strategy of an interleaved high gain DC/DC boost converter (IHGBC) is proposed in order to enhance DC bus voltage regulation in proton exchange membrane fuel cell (PEMFC) applications. The fluctuation of the energy source voltage and external load, and the change in system parameters lead to the instability of output voltage. Based on the creation of an average state space model of the DC/DC boost converter, the proposed controller is designed based on a linear active disturbance rejection control (LADRC), which has an external voltage loop and an internal current loop to meet the output voltage requirements under parameters uncertainties and disturbances. The effectiveness of the proposed approach strategy and its superiority were examined under different operating conditions and scenarios. Simulation and experiment results showed the efficiency and robustness of the suggested approach and the great effectiveness in the reference tracking... [more]

This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for the receiver-side buck converter system in the wireless power transfer system subjecting to the lumped uncertainty. The proposed control strategy utilizes a singularity-free fixed-time sliding mode (FTSM) feedback control, which ensures a fixed-time convergence for both the sliding variable and voltage tracking error. An ELM-based uncertainty bound estimator is further designed to learn the uncertainty bound information in real-time, which opportunely loosens the constraint of bound information requirement for sliding mode control design. The global stability of the closed-loop system is rigidly analyzed, and the good performance of the proposed control strategy is validated by comparison experiments which exhibit ideal overshoot elimination, 45.70−51.72% reduction of settling time, and 13.65−36.96% reduction of the root mean square value for voltage tracking error with respect to dif... [more]

Copper catalysts have been extensively studied for CO oxidation at low temperatures. Previous findings on the stability of such catalysts, on the other hand, revealed that they deactivated badly under extreme circumstances. Therefore, in this work, a series of KCC−1-supported copper oxide catalysts were successfully prepared by impregnation method, of which 5% CuO/KCC−1 exhibited the best activity: CO could be completely converted at 120 °C. The 5% CuO/KCC−1 catalyst exhibited better thermal stability, which is mainly attributed to the large specific surface area of KCC−1 that facilitates the high dispersion of CuO species, and because the dendritic layered walls can lengthen the movement distances from particle-to-particle, thus helping to slow down the tendency of active components to sinter. In addition, the 5% CuO/KCC−1 has abundant mesoporous and surface active oxygen species, which are beneficial to the mass transfer and promote the adsorption of CO and the decomposition of Cu+−C... [more]

In this paper, the electronic expansion valve (EXV) on the single-tube heat exchange experimental platform was used as a research object. Firstly, the EXVs were selected according to the experimental requirements, and the functional parameters were set. Subsequently, the effective opening ranges of the EXVs were determined by manual control, and the control effects of the EXVs installed at the front and back ends of the test section were compared. Finally, by self-tuning and optimizing the best response curves, the proportional and integral coefficients suitable for the experimental platform were obtained; thus, the automatic intelligent control of EXV based on the proportional integral (PI) control algorithm was realized. From setting EXV functional parameters to realizing PI control, an appropriate experimental system-debugging solution for the whole process could be obtained. Based on the solution, the system stability could be improved, and the transition process time could be shor... [more]

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) p... [more]

Novel profile control agents are constantly emerging in the field of enhanced oil recovery, contributing to the extension of a stable production period. However, evaluation performed through conventional core flow experiments is usually inadequate to reveal the in-depth mechanism of profile control agents. Besides, due to different operation and production modes, there is an urgent need for a specific experimental method applicable to horizontal wells in bottom water reservoirs. In this context, this paper describes two models tailored to bottom water reservoirs and investigates the flow characteristics and mechanisms of three water-shutoff agent types. At the pore scale, further study was carried out on the water-shutoff synergism between a gel and an emulsifier. The results show that the gel is present at the edge of the pore body, while the emulsion is blocked in the center of the pore body. Hence, gel that enters a water channel (main flow and accumulation area of emulsion) can coo... [more]

A fault detection and isolation (FDI) approach based on nonlinear sliding mode observers for a wind turbine model is presented. Problems surrounding pitch and drive train system FDI are addressed. This topic has generated great interest because the early detection of faults in these components allows avoiding irreparable damage in wind turbines. A fault diagnosis strategy using nonlinear sliding mode observer banks is proposed due to its ability to handle model uncertainties and external disturbances. Unlike the reported solutions, the solution approach does not need a priori knowledge of the faults and considers system uncertainty. The robustness to disturbances, uncertainties, and measurement noise is shown in the dynamic of the generated residuals, which is sensible to only one kind of fault. To show the effectiveness of the proposed FDI approach, numerical examples based on a wind turbine benchmark model, considering closed loop applications, are presented.

In view of the complicated hydraulic system, the many driving parts and the great load variation in the combine harvester, and on-line monitoring methods of hydraulic actuating parts such as cutting tables, conveyors and threshing drums were studied. By analyzing the working principle of the hydraulic system of the combine harvester, a mathematical model of the hydraulic system of the combine harvester was established; a simulation model for the fault diagnosis of the hydraulic system of the combine harvester was established based on AMESim. The load signal was introduced to simulate the feeding amount, and the simulation test was carried out. According to the simulation analysis results, the best position of each monitoring point was determined. The on-line monitoring system of the hydraulic actuators of the combine harvester was designed by using LabView, which can collect and display the working parameters of the main working parts of a combine harvester in real time, and alarm the... [more]

In a pilot-scale internal loop airlift reactor with a height of 5.5 m and a main column diameter of 0.484 m, the influence of three gas sparger structures (ladder distributor, tri-nozzle sparger and perforated plate) on the volumetric mass transfer coefficient kLa was investigated. It was found that the perforated plate produces the highest gas holdup difference and circulating liquid velocity between the riser and the downcomer. The perforated plate provides the most efficient mass transfer due to the more uniform gas distribution and higher circulating liquid velocity, followed by the ladder distributor and tri-nozzle spargers. Compared with the tri-nozzle sparger, the perforated plate increases the value of kLa by up to 16% at a superficial velocity of 0.15 m/s. Interestingly, the analysis of the liquid-phase mass transfer coefficient kL and specific area a with respect to gas velocity shows that the mass transfer rate is primarily controlled by a. By comparing the predictions of di... [more]

To solve the issue that the monocular vision vehicle navigation system is limited by the field of vision acquired by the charge-coupled device camera and cannot acquire navigation turning path information throughout the turning process, decreasing the vehicle turning control accuracy, this paper proposed a turning control algorithm based on monocular vision vehicle turning path prediction. Firstly, the camera’s distortion was adjusted. Secondly, the camera imaging model was built, and the turning path’s position information was determined using the imaging position relationship. The vehicle motion model was built in accordance with the vehicle steering mode. Lastly, the cornering trajectory of a vehicle was estimated using the vehicle’s front axle length and front-wheel adjustment data, determining the vehicle turning point and turn operations on the basis of the projected relationship between the vehicle turning track and the turning path position. The experimental results showed that... [more]

Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time pre... [more]

Hydropower generation is one of the most prominent renewable sources of power. Run-of-river hydropower is like traditional hydropower but has significantly less environmental impact. Faults in industrial processes are a cause for large amounts of losses in monetary value and off times in industrial processes and consumer utilities. It is more efficient for the system to identify the occurring faults and, if possible, to have the processes running without interruption with the occurrence of a fault. This work uses a model previously proposed—the three-pond hydraulic run-of-river system and integrates it with a turbine and regulated power generation. After integration of the hydraulic system with the turbine and power generation, we then design a diagnostic system for commonly occurring faults within the system. Mathematical models of the faults are formulated and residues are calculated. Fault detection and identification is achieved by analyzing the residues and then a fault-tolerant c... [more]

In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative f... [more]

In order to solve the problems of low precision of variable-rate fertilization and uneven fertilization flow of field liquid fertilizer applicator, a control system of variable-rate fertilization of liquid fertilizer based on beetle antennae search algorithm was proposed. First of all, this study established a mathematical model for the variable-rate fertilization control system of liquid fertilizer. Then, according to the control requirements, the search algorithm is used to optimize the three parameters of Proportion Integration Differentiation (PID). Finally, the response time and overshoot of the system are analyzed by software simulation, and the PID control based on beetle antennae search algorithm is compared and analyzed with fuzzy PID control and traditional PID control. The control effect of the control system is verified by a bench test. The results show that the actual response time of the variable-rate fertilization control system based on the beetle antennae search algori... [more]

A tuning procedure for a model predictive controller (MPC) is presented for multi-input multi-output systems. The approach consists of two steps based on a hybrid method: the goal attainment method and a variable neighborhood search. In the first step, the weights of the MPC objective function are obtained, minimizing the square error between the closed-loop response of the internal controller model and a predefined desired reference trajectory. In the second step, the integer variables of the problem (prediction and control horizons) are obtained, minimizing the square error between the closed-loop response and an optimal trajectory, aiming a controller with low computational cost and good performance. The proposed method was tested in two benchmark processes using different MPC formulations, showing satisfactory results.

China’s “dual carbon” goals, energy conservation and emission reduction in the energy system, have become increasingly important. The sensor fault of an energy system will cause unstable operation and increase energy consumption. Therefore, this study proposes a new sensor fault detection strategy based on the data driven method for energy saving and emission reduction. However, for data-driven models, data quality has a greater impact on model performance. This study innovatively uses five machine learning methods to optimize the energy system operating data. Five machine learning methods include the moving average (MA), Lowess, Loess, Rlowess and Rloess methods. Fault detection performances of different data driven models optimized by different approaches are compared and analyzed. Besides, data outliers and parameter selection of data optimization methods are discussed. The results indicate that the MA method has the best optimization performance when the smoothness degree is level... [more]

Soil degradation is a crucial problem, particularly in tropical and subtropical areas. Prevention or reduction of soil erosion requires strategies based on thorough rapid vegetation cover (VC) and favorable soil quality in subtropical and tropical areas. This study applied wood biochar (WB) and rice husk biochar (RHB) in a mudstone soil, which is widely distributed in Southern Taiwan, to investigate the effects of biochar application on soil erosion and vegetation restoration. The standard erosion unit plots (22.13 m in length and 9% in slope gradient) were set up to determine the relationship among soil losses, VC, and natural rainfall characteristics with and without biochar application. The results indicated that biochar application increased the growth rate (identified by cover ratio) of Bahia grass (Paspalum notatum Flüggé) by 2−2.6 times within 40 days compared with control (without biochar application) and increased VC by 20% after 120 days of treatment. The biochar application... [more]

In submerged arc welding (SAW) of chromium containing steels, the chromium in the weld metal is usually sourced from weld wire. Manufacturing of precise weld wire compositions for alloying of the weld metal is expensive. In addition, alloying of weld metal with high levels of copper via weld wire is hindered by work hardening of the weld wire. In the SAW process, a large quantity of oxygen is added to the weld pool. Because chromium has a high affinity for oxygen, the oxygen partial pressure at the weld pool-molten flux interface must be controlled to ensure high recovery of chromium to the weld metal. This study illustrates the application of copper as stabilizer, in conjunction with aluminum, to enhance chromium transfer to the weld pool. The stabilizer effect occurs because the Cr-Al-Cu alloy liquidus temperatures are much lower than the pure Cr liquidus temperature. The result is an increase in the total quantity of Cr, Al, and Cu powder melted into the weld pool. The application o... [more]

This paper is concerned with the real-time configuration of fault detection systems by exploiting an gradient optimization scheme. It is known that industrial processes may often encounter some uncertainties or changes of operating points and environment, which would lead to an unsatisfactory fault detection result. To handle this problem, a real-time (or online) configuration strategy is introduced, which plays an important role in ensuring the efficiency of the fault detection method without a high industrial cost. In this paper, a gradient-based iterative optimization scheme is taken into account for the real-time configuration implementation. By utilizing the gradient-based iterative algorithm to minimize the K-gap between the residual generator and the current system, the parameters of the residual generator can be configured from the online input/output data. Based on this, real-time configuration of the residual generator parameters is achieved and, correspondingly, the fault de... [more]

Within this article, the nonlinear vibration control of the rotor active magnetic bearings system is tackled utilizing the integral resonant controller for the first time. Two integral resonant controllers are proposed to mitigate the system lateral oscillations in the horizontal and vertical directions. Based on the suggested control technique, the whole system dynamical model is derived as a two-degree-of-freedom nonlinear system (i.e., rotor system) coupled linearly to two first-order filters (i.e., the integral resonant controllers). The nonlinear autonomous system that governs the oscillation amplitudes of the controlled system as a function of the control parameters is extracted by applying perturbation analysis. The obtained autonomous system showed that the linear damping coefficients of the rotor system are functions of the control gains, feedback gains, and internal loop feedback gains of the coupled controller. Accordingly, the sensitivity of the rotor oscillation amplitudes... [more]

With the continuous improvement of industrial production requirements, bearings work significantly under strong noise interference, which makes it difficult to extract fault features. Deep Learning-based approaches are promising for bearing diagnosis. They can extract fault information efficiently and conduct accurate diagnosis. However, the structure of deep learning is often determined by trial and error, which is time-consuming and lacks theoretical support. To address the above problems, an adaptive (Adaptive Depthwise Separable Dilated Convolution and multi-grained cascade forest) ADSD-gcForest fault diagnosis model is proposed in this paper. Multiscale convolution combined with convolutional attention mechanism (CBAM) concentrates on effectively extracting fault information under strong noise, and the Meta-Activate or Not (Meta-ACON) activation function is integrated to adaptively optimize the model structure according to the characteristics of input samples, then gcForest output... [more]

Nowadays, Circular Economy (CE) is shifting from a nice-to-have marketing operation to a business development strategy that is central to the restoring and preservation of our natural ecosystem. However, despite motivated promotion efforts, the concrete application of this model at the micro-level is still in its infancy due to several challenges that companies face. One of these challenges is the uncertainty that businesses have in the estimation of the actual impact that adopting CE strategies can have on their activities from an environmental and economic perspective. This work aims to define a conceptual framework to be applied to the Life Cycle Extension Strategies (LCES) of products, which is meant to understand whether the application of a strategy is beneficial from an environmental and economic point of view. In order to address the identified need, a list of indicators to be exploited in the LCES performance analysis has been designed and divided into the two categories of en... [more]

The growing biopharmaceutical industry has reached a level of maturity that allows for the monitoring of numerous key variables for both process characterization and outcome predictions. Sensors were historically used in order to maintain an optimal environment within the reactor to optimize process performance. However, technological innovation has pushed towards on-line in situ continuous monitoring of quality attributes that could previously only be estimated off-line. These new sensing technologies when coupled with software models have shown promise for unique fingerprinting, smart process control, outcome improvement, and prediction. All this can be done without requiring invasive sampling or intervention on the system. In this paper, the state-of-the-art sensing technologies and their applications in the context of cell culture monitoring are reviewed with emphasis on the coming push towards industry 4.0 and smart manufacturing within the biopharmaceutical sector. Additionally,... [more]

The team’s cognitive behavior plays a crucial role in dealing with accidents at nuclear power plants. Herein, the main behaviors of reactor operators and coordinators in performing accident management were analyzed in executing a state-oriented procedure. According to these cognitive behavioral characteristics, we established cognitive behavioral models of accident management procedures. After that, a cognitive behavioral model was established for the team in the main control room of the nuclear power plant based on the two models, which is expected to provide support to the optimization of a corresponding Human Reliability Analysis model.

The complementary metal-oxide-semiconductor (CMOS) process is the main stream to fabricate integrated circuits (ICs) in the semiconductor industry. Microelectromechanical systems (MEMS), when combined with CMOS electronics to form the CMOS MEMS process, have the merits of small features, low power consumption, on-chip circuitry, and high sensitivity to develop microsensors and micro actuators. Firstly, the authors review the educational CMOS MEMS foundry service provided by the Taiwan Semiconductor Research Institute (TSRI) allied with the United Microelectronics Corporation (UMC) and the Taiwan Semiconductor Manufacturing Company (TSMC). Taiwan’s foundry service of ICs is leading in the world. Secondly, the authors show the new flow sensor integrated with an instrumentation amplifier (IA) fabricated by the latest UMC 0.18 µm CMOS MEMS process as the case study. The new flow sensor adopted the self-heating resistive-thermal-detector (RTD) to sense the flow speed. This self-heating RTD... [more]