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In this paper, the basic technology of spin forming process was carried out on the GH4169 alloy sheet by heating and strong spinning. The spheroidal spinability test method was used to study the spinability of the alloy. The effects of the strong spinning process parameters on the ultimate thinning rate, diameter expansion and fracture behavior of GH4169 alloy were analyzed. The research results showed that when the temperature was higher than 800 °C, the ultimate thinning rate of GH4169 alloy was above 80%. At the same time, with the increase of feed rate, the ultimate thinning rate of GH4169 alloy decreased. When the feed rate of the rotary wheel was lower than 200 mm•min-1, the ultimate thinning rate of GH4169 alloy was maintained above 80%. The radius of rotary wheel was strongly influence GH4169 alloy ductility which was confirmed by the picture of fracture. This basic research provides the experimental basis and process guidance for the practical use of GH4169 and other high-temperature alloy sheets to produce high-precision and high-performance spinning parts, which has significant application value.

The data-based parameter identification is an efficient way for the modeling of Wind Turbine Generation System (WTGS) which only reveals the external characteristic. However the inner characteristic is unknown. In practice the parameters of the mechanism model are changing with time. Thus in the paper the grey-box modeling approach which utilizes the operation data and operating mechanism together are proposed to realize the online parameter identification of WTGS under closed-loop. Considering the essential difference between aerodynamic system and the other systems the driven-train system and the electrical system are identified together which also simplify the identification process and the switching mechanism is proposed for the parameters update of mechanism model which is much efficient and applicable in practice. Besides the identification of driven-train system and electrical system in combination could ensure the global approximation ability to the dynamics of WTGS while the local approximation ability to the subsystems could also be validated when the identification parameters are obtained. In the paper the combined state space model including the driven-train model and Doubly-Fed Induction Generator (DFIG) model is deduced. The GH Bladed Software would be used to generated the operation data for identification and the genetic algorithm is adopted to identify the model parameters. As a result the nonlinear state space model could be obtained and its approximation ability to the dynamic characteristic of WTGS is validated through simulation. Then the practical characteristic of wind turbine could be approximated more accurately. Note that the paper provides meaningful research basis for the control design and model-based fault diagnosis of large-scale WTGS.

In order to overcome the difficulty of extracting gold from gold-bearing sulfide ore by cyanide process, flotation was adopted based on mineralogical analysis. Mineralogy shows that gold particles are of superfine structure and mainly enclosed by sulfide ores. Primary gold-bearing sulfide ore is fine-grained pyrite and arsenopyrite. The paper describes the effects of ratios and dosage of activators and collectors on the recovery and grade of gold concentrate. A proper flotation flowsheet was then proposed based on experimental condition and closed-circuit test. The gold concentrate with the gold grade of 25.14 g·ton−1 and the recovery of 86.94 % is obtained after one rougher, three cleaners, and four scavengers from fine grinding flotation process. Furthermore, the mechanisms of combined activators and combined collectors were studied by thermodynamic calculation, and structure–activity relationship of flotation reagent was also explained.

To improve the security and reliability of equipment and reduce their failure rate, a data-driven state detection algorithm was proposed. The concepts of multi-scale system, multi-scale entropy and multi-scale exergy were defined. The algorithm is used for multi-scale systems whose state parameters change over time and have the characteristic of increasing monotonically on a dominant scale. An abrasion index for the middle speed roller ring mill was constructed, which was used to monitor the states of the instruments. Noise that affected the accuracy of the results was analyzed. The results of simulation experiments demonstrate the effectiveness of the algorithm, which can provide a technical basis for condition maintenance.

According to the structure and information flow of the active control system of large-scale wind farm, a load allocation scheme is proposed considering the wind speed fluctuations within the cluster, i.e. deciding the de-load depth of each turbine according to wind speed fluctuation when the farm is running under AGC mode, using wind turbine generator (WTG) de-load ability to reduce the impacts of wind speed fluctuations on the entire wind farm output. Use particle swarm optimization, prediction of WTG generation, wind speed fluctuation condition, wind scheduling instruction information to calculate the WTG de-load depth risk factors, and get the de-load power order distribution scheme. A simulation of 10 2MW WTGs shows that, load allocation scheme considering the wind speed fluctuations can reflect the relationship effectively between wind speed condition and output reliability, and promote the stability of wind farm output.

The heat storage capacity of the drum boiler plays an important role in the Coordination Control System (CCS) of units. The theoretical calculating method of the heat storage capacity is achieved through the mechanism analysis of the risers, drum and over-heater, which solves the problem of the traditional method effectively. The calculating results based on the units design data and the real running data shows that the heat storage capacity of saturation water and metal increases with the pressure decreasing, and the heat storage capacity of steam is same with pressure changes. The heat storage of every medium has different proportion under different load, the heat storage of drum boiler shows different characteristics when the working conditions changes.

Automatic control system of circulating fluidized bed boiler (CFBB) is considered that has long combustion lag characteristic, instable parameters and difficult to formulate a mathematical model. A T-S fuzzy controller according to the study of fuzzy control based on proportional factors is put forward. The value of each proportional factor is verified by experiment and an online adjusting rules are made. The controller can distinguish the forecast output of least square support vector machine (LS-SVM) and adjusts the proportional factors by the rules. To verify the control effect, the model is simulated with matlab. The results reveal that the control effect is superior to normal control system.

The model of circulating fluidized bed (CFB) electric power generation process based on dynamic method which needs a lot of parameters, but these parameters are usually not easy to get. A model method based on least square support vector machine (LS-SVM) which directly uses the experimental data is presented to overcome the weakness of mechanism model, and the system structure and algorithm are given. Compared with the experimental data, the simulation results show that this method has good performance and can provide control and optimization reference to the CFB electric power generation.

This paper designs the pitch controller synthesis to ensure the stability and the controllability in the whole regions as the power grid scheduling instructions. Because of the wind speed measurement and accurate system model are hard to obtain, the range that the actual working point deviating from the balance point is very large, so that the system dynamic performance cannot be described with linear time variable (LTI). This paper utilizes the linear matrix inequalities' techniques (LMI) to design LPV (linear parameter varying) gain-scheduled controllers based on criteria, which provide guarantees of stability performances along the whole reference trajectory. The LPV pitch controller replaces the traditional speed controller to dynamically compensate the pitch angle. According to the errors between the power grid scheduling instructions and measuring the output power, power controller adjusts generator torque to achieve the adjustable power in the whole regions. In view of unmodeled dynamics and wind speed random disturbance, the feed forward linearization. robust pitch regulation using extended state observer (ESO) is proposed to restrain comprehensive disturbances. The proposed controller is seen to be more efficient.

A three-dimensional elastic-plastic FE(finite element) model of power spinning of thin-walled Ti2AlNb alloy shell was established based on the dynamic, explicit module of FE software. The key technologies, including contact boundary, mesh, were dealt with reasonably. The reliability of model was validated by theoretical evaluation. To find a optimal combination of processing parameters, an L9(3 4 ) orthogonal test was adopted. The results showed that the feed velocity of roller had the most significant influence on the fracture trend, and the mandrel velocity on the wrinkle trend. The optimal processing parameters combination was v2n1r3t3. Based on this model, the distribution of strain of both sides of workpiece was obtained in order to research the reason of the fracture and wrinkle of workpiece. The strain of outer side was larger than that of inner side. The results conformed to the practical power spinning process.

The sintered shale brick and light-weight sintered shale brick are both of brittle wall materials. The mechanical and other properties will be affected greatly by tensile force producing the lime burst. Through analysis of physical and chemical properties of raw materials, the reason of lime burst, and the comparative experiments of lime burst, studied on similarities and differences between the two wall materials, providing important technical parameters for the follow-up research, production and construction in the feature.

Energy-saving and improving energy utilization efficiency are common concerned topics of the whole world. Control quality of reheat steam temperature is an important factor for thermo economy, which is mostly controlled by mankind and leads to uneconomical operation. One of the reasons is unsuitable controller parameters, causing by inaccurate models. In this paper, reheater model was established based on single-phase heat-exchanger thermal system dynamics. And parameters of the model were determined by design manual. Simulation on a 1000WM unit showed that, the model had good dynamic and steady performance.

In this paper, the technology of pressurized hot water extraction of hemi-cellulose and the effects of the pre-extraction on APMP pulping process of the Pink Sheets bamboo are investigated. The results show that the yields of pentosan and bamboo chip are the highest under the conditions of the maximum temperature 170 °C, liquid ratio 1:5, holding time 2h. And the yield of bamboo chip is lowered generally, the color of bamboo is deepened, the pulp strength is degraded, but the refining energy is reduced largely after pre-extraction.

A solution to real-time optimal load dispatching problem of power plant is introduced in this paper. Based on analysis of amounts of operating data, it is proved that the characteristics curve of unit coal consumption rate (CCR) against load are constructed by discrete points and it can't be deduced by linear regression. At the default condition of accurate thermodynamic calculation, the factors impacting unit CCR for power supply are studied at different operating conditions. Data mining method is employed to predict CCR for power supply at different operating points and environmental condition based on history and real-time data. Thus, discrete mathematical model between load and CCR is build up for optimal load dispatching (OLD) among the units. Global optimal solution can be approached by dynamic programming method for the reason of the low dimension (number of units in one plant) of this issue now. The method proposed in this paper is applied in a power plant of Datang group and it is proved to be practical and effective.

A rule extraction method for thermal power unit operation optimization which based on data mining technology is proposed in this paper. Data mining technology is employed to analyze history data stored in real-time/history database of the unit. Stability index, economic index, environmental index and synthetic index are constructed to evaluate the performance of current operating condition. Through comparison analysis on similar operating condition, the best operating parameters and operating mode can be found out at the current condition. Accompanying with data accumulation proceeding in database, nonlinear static model between the unit operation performance index and controllable parameters is drawn over all operating conditions. Effectiveness of this method is illustrated by a practical operation optimization project, in which the realization scheme of operation guidance is also given out.

The determination of operation optimization value is very important for economic analysis and operation optimization in power plants. The operation optimization value determined by traditional methods usually cannot reflect the actual running states correctly in power plants with the increase in running time. Based on a large amount of history data stored in power plants, a way of operation optimization by applying data mining technique is proposed. The structure of operation optimi-zation based on data mining is established and the fuzzy association rule mining algorithm is introduced to find the operation optimization target value to guide the operation in power plants. Based on the actual local data in a 300 MW unit, the operation optimization value in typical load ranges is found out by data mining to provide better adjustment guidance in industry process. Experimental results show that the determination of operation optimization value based on data mining can improve the efficiency and decrease the emission of pollutants.

A motorcycle helmet is the best protective headgear for the prevention of head injuries caused by direct cranial impact. A finite element model based on realistic geometric features of a motorcycle helmet is established, and explicit finite element LS‐DYNA code is employed to simulate dynamic responses at different impact velocities. Peak acceleration and Head Injury Criterion values derived from the headform are used to assess the protective performance of the helmet. We have concluded that the dynamic responses of the helmet dramatically vary with impact velocity, as well as the mechanical properties of the outer shell and energy‐absorbing liner. At low velocities, e.g., less than 8.3 m/s, the shell stiffness and liner density should be relatively low to diminish head‐contact force. At high velocity, e.g., 11.1 m/s, a stiffer shell and denser liner offer superior protection against head injuries.

In this paper, a robust model predictive control algorithm based on infinite norm optimization is applied to a nonlinear boiler-turbine unit. Simulation results show that the designed controller has good tracking, disturbance rejection and robustness against the variations of the operation points due to the plant nonlinearity. Compared with conventional model predictive controller, this control system functions better when the operating point varies.

Against the serious coupling among system variables, long time delay of outputs response and time changing of model of the low speed pulverizer of power plant, a kind of self-tuning fuzzy control algorithm is presented. In the algorithm, the coupling among system variables has been eliminated, the proportion of the deviation and its differential can be adjusted automatically according to the control process, and accurate object model is not needed when setting the controller parameters. The fuzzy control algorithm in this paper has been used into power plant and satisfactory control result acquired.

An information field command values emulation system was designed to meet the requirements of testing and emending the information field testing devices. According to form principle of information field command values, the FPGA (Field-Programmable Gate Array) was used to design the emulation system of information field command values, drive the laser to produce simulate information field command values. By comparing the simulate values and the theoretic values, it can be educed that the precision of the simulate values is high enough to verified the feasibility of the emulation system, therefore it can be concluded that the emulation system can meet the corresponding requirements.

A data driven state detection algorithm was proposed to improve the security and reliability of equipment. The algorithm is used for one class of objects whose state parameters change slowly and cumulatively in the long term. The concepts of multi-scale system, multi-scale entropy and multi-scale exergy were used to describe these processes. With the help of the algorithm, an ash deposition index for the radiant heating surface of a 600 MW unit was constructed to monitor the states of the instruments. Noise was analyzed. The results of simulation experiments demonstrate the effectiveness of the algorithm, which can provide a technical basis for condition maintenance.

The boiler steam pressure is an important parameter reflecting the state of boiler operation. Considering the disturbance and uncertainty of the fuel-steam pressure system, only using pressure signal, an extended state observer is designed, and sliding mode controller is designed based on disturbance and uncertainty compensation. From Lyapunov stability analysis, it is shown that the closed system stability can be guarantee. Simulation results are presented to validate the good system robustness and good tracking performance of the control system.

There are some load areas of thermal power units, around which some coal mills need frequent starting and stopping. In this paper, those areas are considered as Prohibited Operating Zones (POZs), and a new strategy is proposed to solve this issue. PPLD with POZs can be treated as a special non-convex space optimization problem and new method is needed. An improved GA-TS algorithm is proposed intend to search optimal solution. There are three major improvements on the algorithm, including application of Elite Solution List (ESL), design of solution repair strategy, and stop criterion of Tabu Search. In final part of this paper, simulation on a thermal plant with 8 units show effectiveness of the proposed strategy and improved GA-TS algorithm.

Aiming at multi-disturbance characteristic of drum water level system, control system uses three impulse cascade control strategy. This paper designs three impulse cascade control combined of incomplete differential PID and immune P control, fully using the advantages of artificial immune feedback control and incomplete differential PID. Boiler drum level simulation result shows the cascade control based on immune feedback mechanism, can improve the system dynamic performance. The simulation results also prove the validity of the control strategy.

Aimed at the current main steam temperature control problem caused by the burning disturbances which is followed by the coal-fired units involved in peak regulation and frequency modulation, analyzed the shortcomings of the existing solutions and proposed path analysis modeling which can effectively combine the mechanism analysis results for the superheater absorption state reconstruction. Center height signals of the flame was structured, meanwhile introduced the coal factor, excess air, heat signal as the middle layer of variables, and the superheater enthalpy expectations path analysis model has been established and tested. The results show that the model has a great accuracy of prediction, better to reveal the influence of state parameters to the superheater heat-absorbing during combustion process, and be of a great reference value for the boiler combustion system optimization.

For the thermal process is of special features, and it could be disturbed by uncertain factors easily, a novel modeling method based on least mean square (LMS) adaptive filter is presented. Based on the internal model control (IMC) principle, the sub-loop system and the inert area of the superheated steam system are considered as a generalized control object, an internal model-P cascade control system is designed. The models are built by the adaptive LMS filter, the corresponding internal controller is designed, and the self-adaptive control of the full operating condition is realized. The simulation result under typical operating condition in the superheated steam process of a supercritical once-through 600MW boiler show that the method has a good regulating performance. The method is based on the online identification; it is adapted to the complex industrial process whose dynamic characters varied with different operating conditions.

Several different control algorithms for high-order inertial system were researched recently in China. Since different algorithms have similar effects, the consistency of these algorithms was first proposed in this paper in the view of control principle. The superheated steam temperature system of a 600 MW unit was taken as an object. The characteristics of time domain and frequency domain, spectrum of local disturbance signals, robustness under model perturbation, and the real actuator output were analyzed and discussed. The conclusion is that the different algorithms applied in practice do have consistency; these algorithms have intrinsic deficiency, which is derived from the structure of the algorithms themselves. Finally it has carried on prospect for future research and some problems meriting our attention.

In this paper a novel self-tuning controller based on the concept of process supervision and gain adjustment are documented. The features of the design include leading a time varing factor λ (k) to controller output and adopting estimater switch technique. The new design known as ISTC was extended to MIMO system under a particular process model assumption. Simulation study was done in a 2x2 load/pressure system of a turbine - boiler unit. Experiment results shown the efficiency of new design and possibility of industry application in practice.

A state observer based on classical dynamics and a closed loop control system consisted of the state observer are constructed. A general controller based on classical dynamics is constructed, which is explained in classical dynamics: the controlled plant output locus is controlled and led to the desired output locus using feedback control of position, speed and acceleration of the general controller. The cases study show the general controller can improve performances and qualities of control system. At the same time, the paper analyses that PID controller, inner model controller and robust controller are identical in classical dynamics, and gives some improved methods for designing these controllers.

Combustion instability is a phenomenon that happens in a gas turbine when the lean premixed combustion mode is used to reduce the NOx emissions. The process is unstable and thus difficult to control. This paper applies the internal model control (IMC) method in the control of the combustion instability, with the aim of finding effective control structures and methods for this kind of system, and preparing for the study of wide-range robust control. Design results show that the IMC controller has good disturbance rejection capability, but the method should be improved to achieve better performance for the active control of combustion instability.

Multi-model control of a boiler-turbine unit is discussed in this paper. Based on the nonlinear model of the unit, this paper applies the gap metric concept to analyze the problem whether a multi-model control is necessary for the unit, and select the appropriate operating points accordingly. The multi-model control is then designed based on local coordinated PID controllers. Simulation and the experimental results at Daqi 4# unit show that the multi-model controller can guarantee the desired performance under wide range of load variations.

Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. This is often difficult to achieve using conventional PI controllers, due to the nonlinearities and uncertainties. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better perform-ance than the traditional cascade PI controller or the linear GPC is obtained.

In unit steam-boiler generation, a coordinated control strategy is required to ensure a higher rate of load change without violating thermal constraints. The process is characterized by nonlinearity and uncertainty. While neural networks can model highly complex nonlinear dynamical systems, they produce black box models. This has led to significant interest in neuro-fuzzy networks (NFNs) to represent a nonlinear dynamical process. Two alternative methods of exploiting the NFNs within a generalised predictive control (GPC) framework are described. Coordinated control of steam-boiler generation using the two nonlinear GPC methods show excellent tracking and disturbance rejection results and improved performance compared with conventional linear GPC

On the basis of analysis to the boiler models that have exist, the modeling method and simulation environment for large-scale coal-fired power unit are discussed. The power unit is divided into many parts; a set of dynamic model library of thermal equipment and subsystem is setup on with modularization modeling method. The simulation environment is selected as MATLAB/simulink and S-function is used. The integrated dynamic model of 300 MW coal-fired power unit is set up in simulink.

The main steam temperature control is a typical time-variable dead time and time-variable parameter process. The relation between the dead time of the main steam temperature controlled process and steam flow is presented in this paper. A kind of adaptive control arithmetic based on single neuron is designed to make parameters of the internal model to approach the object. Parameters of the controller can be tuned based on parameters of the internal model through a parameter modifying arithmetic. Internal control strategy is adopted in the controller, and the control system designed is applied through a distributed control system. Operation result shows that the control system designed in this paper has good performance to overcome disturbance and deviations of model parameters.

Coal-fired boiler combustion system is a complex multiinput and multioutput plant with strong nonlinear and large time-delay. What's more, it is difficult to establish accurate mathematical model. Because the oxygen content signal has large time-delay and air leak exists, traditional combustion control method that uses oxygen meter to decide air flow is inefficient. Aiming at common problems in the boiler combustion process and based on heat balance principle, this paper adopts the variable oxygen content correction project which sets optimal excess air ratio according to current combustion thermal efficiency and dynamic self-optimizing control which can self-search optimal combustion state. The air/fuel ratio can be adjusted on-line to maintain the optimal combustion condition. Local experiment proves that adopting dynamic self-optimizing control to search optimal air/fuel ratio in combustion process can improve boiler's thermal efficiency and achieve the goal of optimal combustion.

This paper proposes a cerebellar model articulation control (CMAC) used in reheated-steam system. CMAC is thought as a kind of association memory neural network It can be used to approach any nonlinear junction, dynamical modeling, etc. In this paper neural network was used online to identify the controlled plant and the weights of the adaptive PID controller are adjusted timely based-on the identification of the plant. So the controller has the adaptability and the system can be controlled effectively. The self-adaptive algorithm of the whole control system is proposed This approach is also applied to the reheated temperature plant with long time-delay, large inertia and time-variation in power plant. The simulation experiment demonstrates that the controller performs very well when disturb happens or plant parameter varies. It is of strong robustness and adaptive abilities and can be easily accomplished online.

A boiler dynamic model is a relatively complicated model. In order to construct a more practical control system, we must analyse the more detailed dynamic process. This paper introduces a new analysis method named load/pressure increment prediction model. This increment prediction model can fit the practical signal well. Simulation illustrates the introduced method.

This paper introduces the structure of the integrated information system of the power plant. This system can be classified by two components, one is the integration of the control and diagnostic system, the other is integration of the management information and real-time information. Multi-goal optimization and coordination can be used to realize the integration of the control systems, and failure trend-based forecasting methods can be used to realize the integration between the control and diagnostic systems. Through the inter-link between the distributed control system (DCS) networks, the information can be shared by real-time system and management system.