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Qingsong Hua
College of Nuclear Science and Technology, Beijing Normal University, Beijing 100875, China

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Journal article
Published: 25 March 2021 in Sustainability
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Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.

ACS Style

Ying Wang; Bo Feng; Qing-Song Hua; Li Sun. Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method. Sustainability 2021, 13, 3665 .

AMA Style

Ying Wang, Bo Feng, Qing-Song Hua, Li Sun. Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method. Sustainability. 2021; 13 (7):3665.

Chicago/Turabian Style

Ying Wang; Bo Feng; Qing-Song Hua; Li Sun. 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method." Sustainability 13, no. 7: 3665.

Original paper
Published: 09 November 2019 in Ionics
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For high-energy density lithium-sulfur (Li-S) batteries, the effective active material loading, cyclic stability, and modification hardly any effect on energy density are crucial factors, but these three indicators seem contradictory in most case. In this paper, cells with a high sulfur loading, 4.3 mg cm−2, demonstrate excellent performances with the assistance of reduced graphene oxide (rGO) modified on the separator, and the density of modified layer is only 0.1 mg cm−2, which is hardly any effect on energy density. Moreover, in order to understand the improvement mechanism of the modified layer, graphene oxide (GO) modified layer is also to be applied for comparison, which is also helpful to establish cognition to select other modification layers. Most important of all, the application of high sulfur loading is generally required for practical Li-S batteries and the extremely light-weight modified layer is beneficial to the exertion of the whole energy density.

ACS Style

Xiang-Yun Qiu; Qing-Song Hua; Zuo-Qiang Dai; Zong-Min Zheng; Fa-Jie Wang; Hong-Xin Zhang. High sulfur loading application with the assistance of an extremely light-weight multifunctional layer on the separator for lithium-sulfur batteries. Ionics 2019, 26, 1139 -1147.

AMA Style

Xiang-Yun Qiu, Qing-Song Hua, Zuo-Qiang Dai, Zong-Min Zheng, Fa-Jie Wang, Hong-Xin Zhang. High sulfur loading application with the assistance of an extremely light-weight multifunctional layer on the separator for lithium-sulfur batteries. Ionics. 2019; 26 (3):1139-1147.

Chicago/Turabian Style

Xiang-Yun Qiu; Qing-Song Hua; Zuo-Qiang Dai; Zong-Min Zheng; Fa-Jie Wang; Hong-Xin Zhang. 2019. "High sulfur loading application with the assistance of an extremely light-weight multifunctional layer on the separator for lithium-sulfur batteries." Ionics 26, no. 3: 1139-1147.

Journal article
Published: 17 September 2019 in Renewable Energy
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The cooling of the open-cathode proton exchange membrane fuel cell (PEMFC) is critical for the operational safety and overall efficiency. However, its control is challenging because of the model uncertainties and frequent disturbances caused by the power adjustment. To this end, this paper proposes a hybrid cooling control strategy by combining the merits of the model-based and data-driven methods. Firstly, a simplified nonlinear mechanistic model is used to exhibit the dynamic perturbations in terms of the different fan speeds and power conditions. Secondly, a modified active disturbance rejection control (ADRC) is developed by incorporating an identified nominal linear model into extended state observer. The external disturbances and the internal uncertainties beyond the nominal model are lumped as a total term, which will be estimated and mitigated in a real-time data-driven manner. The simulation show that the proposed hybrid method is able to give a faster response with more robustness and less sensitivity noise against the uncertainties than the conventional PI and ADRC methods. The experimental test on a 500W open-cathode PEMFC verifies the simulation merits in both set-point tracking and disturbance rejection, depicting a promising prospect of the proposed hybrid method in the open-cathode PEMFC cooling control practice.

ACS Style

Li Sun; Guanru Li; Q.S. Hua; Yuhui Jin. A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control. Renewable Energy 2019, 147, 1642 -1652.

AMA Style

Li Sun, Guanru Li, Q.S. Hua, Yuhui Jin. A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control. Renewable Energy. 2019; 147 ():1642-1652.

Chicago/Turabian Style

Li Sun; Guanru Li; Q.S. Hua; Yuhui Jin. 2019. "A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control." Renewable Energy 147, no. : 1642-1652.

Journal article
Published: 14 June 2019 in Sustainability
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Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

ACS Style

Yuxiao Qin; Guodong Zhao; Qingsong Hua; Li Sun; Soumyadeep Nag. Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization. Sustainability 2019, 11, 3290 .

AMA Style

Yuxiao Qin, Guodong Zhao, Qingsong Hua, Li Sun, Soumyadeep Nag. Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization. Sustainability. 2019; 11 (12):3290.

Chicago/Turabian Style

Yuxiao Qin; Guodong Zhao; Qingsong Hua; Li Sun; Soumyadeep Nag. 2019. "Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization." Sustainability 11, no. 12: 3290.

Journal article
Published: 22 May 2019 in Water
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Water pump control, prevalent in various industrial plants, such as wastewater treatment and steam generator facilities, plays a significant role in maintaining economic efficiency and stable plant operation. Due to its slow dynamics, strong nonlinearity, and various disturbances, it is also widely studied as a typical benchmark problem in process control. The current control strategies can be categorized into two aspects: one branch resorts to model-based design and the other to data-driven design. To merge the merits and overcome the deficiencies of each paradigm, this paper proposes a hybrid data-driven and model-assisted control strategy, namely modified active disturbance rejection control (MADRC). The model information regarding water dynamics is incorporated into an extended state observer (ESO), which is used to estimate and mitigate the limitations of slow dynamics, strong nonlinearity, and various disturbances by analyzing the real-time data. The tuning formula is given in terms of the desired closed-loop performance. It is shown that MADRC is able to produce a satisfactory control performance while maintaining a low sensitivity to the measurement noise under general parametric setting conditions. The simulation results verify the clear superiority of MADRC over the proportional-integral (PI) controller and the conventional ADRC, and the results also evidence its noise reduction effects. The experimental results agree well with the simulation results based on a water tank setup. The proposed MADRC approach is able to improve the control performance while reducing the actuator fluctuation. The results presented in this paper offer a promising methodology for the water control loops widely used in the water industry.

ACS Style

Guanru Li; Lei Pan; Qingsong Hua; Li Sun; Kwang Y. Lee. Water Pump Control: A Hybrid Data-Driven and Model-Assisted Active Disturbance Rejection Approach. Water 2019, 11, 1066 .

AMA Style

Guanru Li, Lei Pan, Qingsong Hua, Li Sun, Kwang Y. Lee. Water Pump Control: A Hybrid Data-Driven and Model-Assisted Active Disturbance Rejection Approach. Water. 2019; 11 (5):1066.

Chicago/Turabian Style

Guanru Li; Lei Pan; Qingsong Hua; Li Sun; Kwang Y. Lee. 2019. "Water Pump Control: A Hybrid Data-Driven and Model-Assisted Active Disturbance Rejection Approach." Water 11, no. 5: 1066.

Journal article
Published: 16 March 2019 in Engineering Analysis with Boundary Elements
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In this article, a novel meshless boundary function method (BFM) is proposed for solving the boundary identification problem of steady-state nonlinear heat conduction in arbitrary plane domain. Firstly, the original governing equation is transformed to a new one with homogeneous Cauchy boundary conditions by using a homogenization technique. Secondly, the domain type meshless collocation method is employed to solve the new partial different equation in a reduced domain, in which the numerical solution is expanded by a sequence of boundary functions, automatically satisfying the homogeneous boundary conditions on the known boundary. After that, a nonlinear equation corresponding to each angle is formed and then is solved by the Newton iterative method in order to determine the missing boundary shape. Finally, the accuracy and robustness of the proposed BFM are examined through three numerical examples.

ACS Style

Lin Qiu; Wen Chen; Fajie Wang; Chein-Shan Liu; Qingsong Hua. Boundary function method for boundary identification in two-dimensional steady-state nonlinear heat conduction problems. Engineering Analysis with Boundary Elements 2019, 103, 101 -108.

AMA Style

Lin Qiu, Wen Chen, Fajie Wang, Chein-Shan Liu, Qingsong Hua. Boundary function method for boundary identification in two-dimensional steady-state nonlinear heat conduction problems. Engineering Analysis with Boundary Elements. 2019; 103 ():101-108.

Chicago/Turabian Style

Lin Qiu; Wen Chen; Fajie Wang; Chein-Shan Liu; Qingsong Hua. 2019. "Boundary function method for boundary identification in two-dimensional steady-state nonlinear heat conduction problems." Engineering Analysis with Boundary Elements 103, no. : 101-108.

Journal article
Published: 23 February 2019 in Applied Mathematical Modelling
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In this paper we investigate the application of the generalized finite difference method (GFDM) to three-dimensional (3D) transient heat conduction in anisotropic composite (layered) materials. In our computations, the Krylov deferred correction (KDC) method, a pseudo-spectral type time-marching technique, is introduced to perform temporal discretization in time-domain. The KDC method allows discretizing the temporal direction using relatively large time-steps, making the method very promising for dynamic simulations, particularly when high precision is desired. A multi-domain GFDM scheme is also employed where the composite material considered is decomposed into several sub-domains and, in each sub-domain, the solution is approximated by using the GFDM expansion. On the sub-domain interface, compatibility of temperatures and normal heat fluxes is imposed. The method is tested on several benchmark numerical examples and its relative merits and disadvantages are discussed.

ACS Style

Yan Gu; Qingsong Hua; Chuanzeng Zhang; Xiaoqiao He. The generalized finite difference method for long-time transient heat conduction in 3D anisotropic composite materials. Applied Mathematical Modelling 2019, 71, 316 -330.

AMA Style

Yan Gu, Qingsong Hua, Chuanzeng Zhang, Xiaoqiao He. The generalized finite difference method for long-time transient heat conduction in 3D anisotropic composite materials. Applied Mathematical Modelling. 2019; 71 ():316-330.

Chicago/Turabian Style

Yan Gu; Qingsong Hua; Chuanzeng Zhang; Xiaoqiao He. 2019. "The generalized finite difference method for long-time transient heat conduction in 3D anisotropic composite materials." Applied Mathematical Modelling 71, no. : 316-330.

Journal article
Published: 22 January 2019 in Journal of Alloys and Compounds
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Transition metal oxides/hydroxides materials attract much attention in the field of energy storage materials because of their high theoretical specific capacity and low cost. However, it is normally difficult to improve the capacitance of the electrode by simply increasing the mass loading or thickness of the electrode. To raise the utilizing efficiency of the active material, hierarchical integrated electrodes assembled by NiCo hydroxide nanowires arrays and reduced graphene oxide interlayers are well designed and synthesized. The surface morphology and inner structure of the hierarchical electrode are characterized by SEM. The electrochemical performances of the electrodes are evaluated by three-electrode and two-electrode system in 2 M KOH respectively. Assisted with two reduced graphene oxide interlayers, NiCo hydroxide nanowires arrays exhibit a specific capacity of 2.41 C cm−2 at 1 mA cm−2. Even at a high current density of 50 mA cm−2, nearly 47% of the capacity could still remain. The results show that, the multi-level nanowires arrays structure provides multi-dimensional transmission path for ions and electrons, and achieved higher specific capacities and rate properties.

ACS Style

Jian Zhang; Zongmin Zheng; Guanglei Wu; Qingsong Hua. Hierarchical electrodes assembled by alternate NiCo hydroxide nanowires arrays and conductive interlayers with enhanced properties for electrochemical supercapacitors. Journal of Alloys and Compounds 2019, 785, 725 -731.

AMA Style

Jian Zhang, Zongmin Zheng, Guanglei Wu, Qingsong Hua. Hierarchical electrodes assembled by alternate NiCo hydroxide nanowires arrays and conductive interlayers with enhanced properties for electrochemical supercapacitors. Journal of Alloys and Compounds. 2019; 785 ():725-731.

Chicago/Turabian Style

Jian Zhang; Zongmin Zheng; Guanglei Wu; Qingsong Hua. 2019. "Hierarchical electrodes assembled by alternate NiCo hydroxide nanowires arrays and conductive interlayers with enhanced properties for electrochemical supercapacitors." Journal of Alloys and Compounds 785, no. : 725-731.

Journal article
Published: 21 January 2019 in Applied Mathematics Letters
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A new space–time meshless method is proposed for solving the nonhomogeneous convection–diffusion equations with variable coefficients. From the perspective of the space–time distance, the space–time radial basis function in a space and time scale framework is presented and then is employed to discretize the time-dependent parabolic partial differential equations. The present meshless scheme is a truly meshless method which requires neither domain or boundary discretization, and can be easily used to 1D, 2D, and 3D problems. Furthermore, the proposed new methodology does not require the discretization of the temporal derivatives, and therefore is very simple mathematically, relatively fast computationally, and easy to program. Numerical examples conforms the effectiveness and accuracy of the proposed method.

ACS Style

Xingxing Yue; Fajie Wang; Qingsong Hua; Xiang-Yun Qiu. A novel space–time meshless method for nonhomogeneous convection–diffusion equations with variable coefficients. Applied Mathematics Letters 2019, 92, 144 -150.

AMA Style

Xingxing Yue, Fajie Wang, Qingsong Hua, Xiang-Yun Qiu. A novel space–time meshless method for nonhomogeneous convection–diffusion equations with variable coefficients. Applied Mathematics Letters. 2019; 92 ():144-150.

Chicago/Turabian Style

Xingxing Yue; Fajie Wang; Qingsong Hua; Xiang-Yun Qiu. 2019. "A novel space–time meshless method for nonhomogeneous convection–diffusion equations with variable coefficients." Applied Mathematics Letters 92, no. : 144-150.

Journal article
Published: 14 December 2018 in International Journal of Environmental Research and Public Health
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The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.

ACS Style

Yuxiao Qin; Li Sun; Qingsong Hua. Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm. International Journal of Environmental Research and Public Health 2018, 15, 2865 .

AMA Style

Yuxiao Qin, Li Sun, Qingsong Hua. Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm. International Journal of Environmental Research and Public Health. 2018; 15 (12):2865.

Chicago/Turabian Style

Yuxiao Qin; Li Sun; Qingsong Hua. 2018. "Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm." International Journal of Environmental Research and Public Health 15, no. 12: 2865.

Journal article
Published: 25 September 2018 in Applied Energy
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Efficient oxygen excess ratio (OER) control is of great importance for proton exchange membrane fuel cell because it is closely associated with the economic efficiency and safety. As widely investigated, OER control is challenging due to the difficulties of system nonlinearity, parametric uncertainty and load disturbances. In this paper, an underlying difficulty for OER control is addressed by pointing out the overshoot response. To this end, this paper employs active disturbance rejection control which is able to handle the various difficulties in a data-driven manner. It treats the nonlinearity, uncertainty and disturbances as a lumped term, which is then estimated online via analyzing the real-time data. The estimated lumped term is canceled timely such that the remaining dynamics behaves like an integrator without overshoot term therein. The data-driven and conventional proportional-integral controllers are tuned and compared based on the linearized transfer function model, showing the potential superiority of the proposed method in terms of the uncertainty and disturbance rejection, anti-windup and overshoot reduction. The nonlinear simulation based on the nonlinear mechanism model further demonstrates it good flexibility under different operating conditions. Moreover, it requires less compressor movement efforts, leading to a dynamic energy-saving effect and thus prolonging the durability and lifetime of the compressor.

ACS Style

Li Sun; Jiong Shen; Qingsong Hua; Kwang Y. Lee. Data-driven oxygen excess ratio control for proton exchange membrane fuel cell. Applied Energy 2018, 231, 866 -875.

AMA Style

Li Sun, Jiong Shen, Qingsong Hua, Kwang Y. Lee. Data-driven oxygen excess ratio control for proton exchange membrane fuel cell. Applied Energy. 2018; 231 ():866-875.

Chicago/Turabian Style

Li Sun; Jiong Shen; Qingsong Hua; Kwang Y. Lee. 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell." Applied Energy 231, no. : 866-875.

Journal article
Published: 24 September 2018 in Neural Computing and Applications
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In recent years, the development of emerging technologies has brought about a new era of industrial reform. The current industrial revolution will deeply integrate the new generation of information technology with modern manufacturing industry and production servicing businesses to promote transformation and upgrading. As it is the foundation of the manufacturing industry, intelligent equipment plays an important role in the reform. In this paper, we propose an innovative design method to help design intelligent equipment. Firstly, referring to the architecture of the Cognitive Internet of Things (CIoT) and industrial big data, we proposed the architecture of the method and defined the different layers to process the data. Then, for the acquired external data, we put forward an algorithm which was combined with the technology of CIoT and industrial big data, to help designers analyze and make decisions. Finally, we verified the validity and feasibility of this method through a case study. The results showed that this method could effectively mine the deep information of intelligent equipment and provide more valuable information about design-assisting designers in designing better intelligent equipment.

ACS Style

Jiafu Wan; Jiapeng Li; Qingsong Hua; Antonio Celesti; Zhongren Wang. Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data. Neural Computing and Applications 2018, 32, 4463 -4472.

AMA Style

Jiafu Wan, Jiapeng Li, Qingsong Hua, Antonio Celesti, Zhongren Wang. Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data. Neural Computing and Applications. 2018; 32 (9):4463-4472.

Chicago/Turabian Style

Jiafu Wan; Jiapeng Li; Qingsong Hua; Antonio Celesti; Zhongren Wang. 2018. "Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data." Neural Computing and Applications 32, no. 9: 4463-4472.

Journal article
Published: 01 September 2018 in Computers & Mathematics with Applications
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This paper presents a simple empirical formula of origin intensity factor in singular boundary method (SBM) solution of Hausdorff derivative Laplace equations. The SBM with the empirical formula is mathematically more simple and computationally more efficient than using the other techniques for origin intensity factor. Numerical experiments simulate the steady heat conduction through fractal media governed by the Hausdorff Laplace equation, and show the efficiency and reliability benefits of the present SBM empirical formula.

ACS Style

Fajie Wang; Wen Chen; Qingsong Hua. A simple empirical formula of origin intensity factor in singular boundary method for two-dimensional Hausdorff derivative Laplace equations with Dirichlet boundary. Computers & Mathematics with Applications 2018, 76, 1075 -1084.

AMA Style

Fajie Wang, Wen Chen, Qingsong Hua. A simple empirical formula of origin intensity factor in singular boundary method for two-dimensional Hausdorff derivative Laplace equations with Dirichlet boundary. Computers & Mathematics with Applications. 2018; 76 (5):1075-1084.

Chicago/Turabian Style

Fajie Wang; Wen Chen; Qingsong Hua. 2018. "A simple empirical formula of origin intensity factor in singular boundary method for two-dimensional Hausdorff derivative Laplace equations with Dirichlet boundary." Computers & Mathematics with Applications 76, no. 5: 1075-1084.

Journal article
Published: 30 July 2018 in Sustainability
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A heat exchanger is widely used for energy management or heat recovery in sustainable energy systems. In many application cases, the outlet temperature should be strictly controlled as desired. However, it is challenging to obtain an accurate dynamic model due to the high-order dynamics, thus reducing the control performance. To this end, this paper proposes a novel identification method by considering the heating process as an approximate second-order plus time delay (SOPDT) model. A normalized analysis indicates that the time-scaled step responses of the general second-order models almost intersect at the same point, which leads to an equation describing the sum of the time constants. Critical stability analysis based on the Nyquist criterion gives another two equations in the frequency domain. Hence the time constants and time delay can be obtained by solving the equations. Illustrative examples show the identification efficiency of the proposed method in the parameter estimation, model reduction, and anti-noise performance. With an effective identification, the high-fidelity SOPDT model makes the PID controller tuning less challengeable. The simulation results based on a benchmark heat exchanger model demonstrate the feasibility of the identification and control. Finally, a real heat exchanger control facility is built and the experimental performance agrees well with the simulation expectation, depicting a promising application prospect in future sustainable applications.

ACS Style

Yuhui Jin; Li Sun; Qingsong Hua; Shunjia Chen. Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification. Sustainability 2018, 10, 2667 .

AMA Style

Yuhui Jin, Li Sun, Qingsong Hua, Shunjia Chen. Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification. Sustainability. 2018; 10 (8):2667.

Chicago/Turabian Style

Yuhui Jin; Li Sun; Qingsong Hua; Shunjia Chen. 2018. "Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification." Sustainability 10, no. 8: 2667.

Journal article
Published: 12 July 2018 in Sustainability
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Solid oxide fuel cells (SOFCs) are promising electrochemical devices which translate chemical energy directly into electric energy with high efficiency and low pollution. However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative (PID) controllers are commonly utilized in industrial control systems for their high reliability and simplicity. However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. This paper aims to improve the PID controller performance based on fuzzy logic by (1) identifying a linear model based on the least squares method; (2) optimizing the PID parameters based on the generated linear model; and (3) designing a fuzzy adaptive PID controller based on the optimized parameters. The simulation results of the conventional PID controller and the fuzzy adaptive PID controller are compared, demonstrating that the proposed controller can achieve satisfactory control performance for SOFCs in terms of anti-windup, overshoot reduction, and tracking acceleration. The main contribution of this paper can be summarized as: (1) this paper identifies the SOFC model and uses the identified model as a control object to optimize conventional PID controllers; (2) this paper combines a fuzzy logic control scheme and PID control scheme to design our proposed fuzzy adaptive PID controller; and (3) this paper develops an anti-windup structure based on a back-calculation method to reduce saturation time and overshoot.

ACS Style

Yuxiao Qin; Li Sun; Qingsong Hua; Ping Liu. A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant. Sustainability 2018, 10, 2438 .

AMA Style

Yuxiao Qin, Li Sun, Qingsong Hua, Ping Liu. A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant. Sustainability. 2018; 10 (7):2438.

Chicago/Turabian Style

Yuxiao Qin; Li Sun; Qingsong Hua; Ping Liu. 2018. "A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant." Sustainability 10, no. 7: 2438.

Short communication
Published: 29 May 2018 in Corrosion Science
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Nb doping substantially changes the oxidation mechanism and significantly enhances its oxidation resistance of Ti3SiC2 at 800 °C. After Nb doping, the oxidation of Ti3SiC2 is only controlled by the inward diffusion of O, while the outward diffusion of Ti is restrained totally. The oxide layer structure changes from a duplex-layer of TiO2 outer layer and TiO2+SiO2 mixture inner layer to a single TiO2+SiO2 mixture layer. It is proposed that Nb doping decreases the concentrations of oxygen vacancies and Ti interstitials in the formed TiO2, leading to the completely restrained outward diffusion of Ti and the decreased oxidation rate.

ACS Style

Lili Zheng; Qingsong Hua; Xichao Li; Meishuan Li; Yuhai Qian; Jingjun Xu; Jianmin Zhang; Zongmin Zheng; Zuoqiang Dai; Hongxin Zhang; Tiezhu Zhang. Investigation on the effect of Nb doping on the oxidation mechanism of Ti3SiC2. Corrosion Science 2018, 140, 374 -378.

AMA Style

Lili Zheng, Qingsong Hua, Xichao Li, Meishuan Li, Yuhai Qian, Jingjun Xu, Jianmin Zhang, Zongmin Zheng, Zuoqiang Dai, Hongxin Zhang, Tiezhu Zhang. Investigation on the effect of Nb doping on the oxidation mechanism of Ti3SiC2. Corrosion Science. 2018; 140 ():374-378.

Chicago/Turabian Style

Lili Zheng; Qingsong Hua; Xichao Li; Meishuan Li; Yuhai Qian; Jingjun Xu; Jianmin Zhang; Zongmin Zheng; Zuoqiang Dai; Hongxin Zhang; Tiezhu Zhang. 2018. "Investigation on the effect of Nb doping on the oxidation mechanism of Ti3SiC2." Corrosion Science 140, no. : 374-378.

Journal article
Published: 09 March 2018 in IEEE/ASME Transactions on Mechatronics
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The introduction of Industry 4.0 and rapid development of Manufacturing Cyber-Physical Systems (MCPS), as well as the increasing demand for multi-variety, small batch and personalized customization, pose a huge challenge to the traditional manufacturing systems. In order to meet the production requirements for fast iteration and realize agile and efficient manufacturing resource allocation, this paper proposes an ontology-based resource reconfiguration method from the perspective of resource utilization. First, an intelligent device ontology that describes the intelligent manufacturing resource is established using the Web Ontology Language (OWL). On this basis, the relational database is associated with the ontology of manufacturing system, which makes the manufacturing resources be mapped to the model instances. Finally, we analyze the equipment reconfiguration of intelligent manipulator as an application case, which explains the proposed method for resource reconfiguration based on ontology, and verifies its feasibility in manufacturing. Lastly, this study provides a new method for reconfigurable research of manufacturing resources.

ACS Style

Jiafu Wan; Boxing Yin; Di Li; Antonio Celesti; Fei Tao; Qingsong Hua. An Ontology-Based Resource Reconfiguration Method for Manufacturing Cyber-Physical Systems. IEEE/ASME Transactions on Mechatronics 2018, 23, 2537 -2546.

AMA Style

Jiafu Wan, Boxing Yin, Di Li, Antonio Celesti, Fei Tao, Qingsong Hua. An Ontology-Based Resource Reconfiguration Method for Manufacturing Cyber-Physical Systems. IEEE/ASME Transactions on Mechatronics. 2018; 23 (6):2537-2546.

Chicago/Turabian Style

Jiafu Wan; Boxing Yin; Di Li; Antonio Celesti; Fei Tao; Qingsong Hua. 2018. "An Ontology-Based Resource Reconfiguration Method for Manufacturing Cyber-Physical Systems." IEEE/ASME Transactions on Mechatronics 23, no. 6: 2537-2546.

Journal article
Published: 08 February 2018 in Sustainability
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Solid oxide fuel cell (SOFC) is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.

ACS Style

Long Wu; Li Sun; Jiong Shen; Qingsong Hua. Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System. Sustainability 2018, 10, 437 .

AMA Style

Long Wu, Li Sun, Jiong Shen, Qingsong Hua. Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System. Sustainability. 2018; 10 (2):437.

Chicago/Turabian Style

Long Wu; Li Sun; Jiong Shen; Qingsong Hua. 2018. "Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System." Sustainability 10, no. 2: 437.

Journal article
Published: 01 December 2017 in Applied Energy
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ACS Style

Li Sun; Qingsong Hua; Jiong Shen; Yali Xue; Donghai Li; Kwang Y. Lee. Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant. Applied Energy 2017, 208, 592 -606.

AMA Style

Li Sun, Qingsong Hua, Jiong Shen, Yali Xue, Donghai Li, Kwang Y. Lee. Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant. Applied Energy. 2017; 208 ():592-606.

Chicago/Turabian Style

Li Sun; Qingsong Hua; Jiong Shen; Yali Xue; Donghai Li; Kwang Y. Lee. 2017. "Multi-objective optimization for advanced superheater steam temperature control in a 300 MW power plant." Applied Energy 208, no. : 592-606.

Journal article
Published: 28 August 2017 in Sustainability
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Control of output voltage is critical for the power quality of solid oxide fuel cells (SOFCs), which is, however, challenging due to electrochemical nonlinearity, load disturbances, modelling uncertainties, and actuator constraints. Moreover, the fuel utilization rate should be limited within a safety range during the voltage regulation transient. The current research is usually appealing to model predictive control (MPC) by formulating the difficulties into a constrained optimization problem, but its huge computational complexity makes it formidable for real-time implementation in practice. To this end, this paper aims to develop a combined control structure, with basic function blocks, to fulfill the objectives with minor computation. Firstly, the disturbance, nonlinearity and uncertainties are lumped as a total disturbance, which is estimated and mitigated by active disturbance rejection controller (ADRC). Secondly, a feed-forward controller is introduced to improve the load disturbance rejection response. Finally, the constraints are satisfied by designing a cautious switching strategy. The simulation results show that the nominal performance of the proposed strategy is comparable to MPC. In the presence of parameter perturbation, the proposed strategy shows a better performance than MPC.

ACS Style

Li Sun; Qingsong Hua; Jiong Shen; Yali Xue; Donghai Li; Kwang Y. Lee. A Combined Voltage Control Strategy for Fuel Cell. Sustainability 2017, 9, 1517 .

AMA Style

Li Sun, Qingsong Hua, Jiong Shen, Yali Xue, Donghai Li, Kwang Y. Lee. A Combined Voltage Control Strategy for Fuel Cell. Sustainability. 2017; 9 (9):1517.

Chicago/Turabian Style

Li Sun; Qingsong Hua; Jiong Shen; Yali Xue; Donghai Li; Kwang Y. Lee. 2017. "A Combined Voltage Control Strategy for Fuel Cell." Sustainability 9, no. 9: 1517.