基于支持向量机的电弧炉逆内模控制器

针对三相交流电弧炉电极调节系统的耦合问题,提出了基于支持向量机的逆内模解耦控制策略.根据广义电弧炉对象的Taylor近似模型直接推导逆控制律,消除三相之间的耦合,避免了在线辨识逆模型计算量过大的问题.另外,在内模结构中引入非线性补偿,当系统参数变化和受到外部干扰时,保证了系统的鲁棒性.系统的稳定性和鲁棒性通过Lyapunov方法进行了分析,最后的仿真与实验结果验证了控制器的有效性.     

An economic model predictive control framework for mechanical pulping processes

We develop a multi-objective economic model predictive control (m-econ MPC) framework to control and optimize a nonlinear mechanical pulping (MP) process. M-econ MPC interprets economic MPC as a multi-objective optimization problem that trades off economic and set-point tracking performance. This interpretation allows us to construct a stabilizing constraint that guarantees closed-loop stability. The framework infers unmeasured states of the MP process (associated with product consistency) by using a moving horizon estimator (MHE). The MP process dynamics are described by using a nonlinear Wiener model. Examples from a two-stage high-consistency MP process are employed to demonstrate that significant improvements in economic performance are achievable.     

基于近似模型的电弧炉解耦控制器

针对电弧炉电极调节系统,提出了基于近似模型的解耦控制策略.首先,选取弧长为状态变量,推导电极调节系统的状态方程,由Taylor展开技术,得到系统的近似模型.由近似模型直接推导近似逆控制律,实现三相之间的解耦,并避免了在线辨识逆模型计算量过大的问题.由于状态不能直接测量,利用扩展卡尔曼状态估计方法得到系统状态.另外,在内模结构中引入非线性补偿,保证了系统的鲁棒性.系统的稳定性通过Lyapunov方法进行了分析,最后的仿真结果验证了控制器的有效性.     

Thermodynamical computations for removal of alkali halides and lead compounds from electric arc furnace dust

Thermodynamical aspects of a separation process, proposed by the authors for the removal of volatile species from EAF dust, have been studied. Computations have been performed using FACT (Facility for Analysis of Chemical Thermodynamics) software. Different conditions have been considered through calculations of equilibrium state. Effects of different additives, temperature and atmosphere at different total pressure were investigated. It has been shown that removal of alkali halides and lead compounds is possible at temperature as low as 900 ^°C. Oxidizing condition helps removal of lead in the form of lead oxide, while reducing condition increases zinc loss due to reduction and evaporation. Higher temperature results in higher vapor pressure of volatile species, but not necessarily in higher amount of removal for volatiles at equilibrium condition. Reduced pressure does not change the thermodynamical conditions of the separation process.     

基于grey Markov--支持向量机的电弧炉终点参数预报

考虑电弧炉终点参数既受定量因素的影响,又受非定量因素的影响.将灰色马尔可夫 (grey Markov)与支持向量机 (SVM)相结合.建立了电弧炉终点参数grey Markov-SVM预报模型,其中grey Markov模型反映非定量因素对电弧炉终点参数预测值的影响,SVM模型反映电弧炉各种定量输入对终点参数预测值的影响.建立grey Markov-SVM模型的方法是:首先建立反映非定量因素的GM (1,1)模型,然后用Markov链修正其预测值:由于grey Markov模型对定量输入的影响无法准确反映,因此grey Mar-kov模型必然存在预测偏差,此预测偏差通过建立反映定量输入与终点参数预测偏差之间关系的SVM模型方法加以补偿,并采用粒子群算法 (PSO)对SVM的参数进行寻优,最终得到电弧炉终点参数的预报值,同时实现滚动预测.仿真实验表明grey Markov-SVM模型与grey-SVM模型、Markov-SVM相比较,具有很高的终点预报精度.     

A smart modelling for the casting temperature prediction in an electric arc furnace

The efficient and reliable control of an electric arc furnace (EAF) is a challenging problem, due to the strong intercorrelation among process variables, the large dimension of the input and output space, the strong interaction among process variables, a large time delay, and a highly nonlinear behaviour. This paper presents a model that allows us to optimize the control and, therefore, the electric power consumption in an EAF. The data used for this study were collected from Bizkaia Steel Mill (Arcelor Company). Neural network and fuzzy logic techniques have been applied on these data in order to get an improved model of the casting temperature inside the furnace's hearth. First, we developed some neural network models with different topologies and input variables. Then we used the best model obtained in the previous step to combine it with a fuzzy logic technique to get the final model. Comparison with experimental data and other models is carried out to validate the proposed model. Finally, the conclusions and future studies are exposed.     

An efficient power control scheme for electric arc furnaces using online estimation of flexible cable inductance

This paper proposes a method for efficient power control of Electric Arc Furnaces (EAFs) based on the estimation of the instantaneous value of the flexible cable inductance. In this method, the displacements of the graphite electrodes regulating arc voltages are controlled using instant values of inductances at the secondary side of the EAF in three phases. For on-line estimation of inductance values, an artificial neural network is trained, using a random set of data obtained from an appropriate inductance simulator. The performance of the proposed method is examined by comparing the thermal energy efficiency of a typical EAF for two control schemes, namely, constant and varying flexible cable inductances. It is shown that the proposed method significantly improves the efficiency with negligible changes in harmonic generation and unbalance operation of the EAF.     

A stochastic method for the energy management in hybrid electric vehicles

There are many approaches addressing the problem of optimal energy management in hybrid electric vehicles; however, most of them optimise the control strategy for particular driving cycles. This paper takes into account that the driving cycle is not a priori known to obtain a near-optimal solution. The proposed method is based on analysing the power demands in a given receding horizon to estimate future driving conditions and minimise the fuel consumption while cancelling the expected battery energy consumption after a defined time horizon. Simulations show that the proposed method allows charge sustainability providing near-optimal results.     

Real-time nonlinear MPC and MHE for a large-scale mechatronic application

Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables.     

Least costly energy management for series hybrid electric vehicles

Energy management of plug-in hybrid electric vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging. Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user – the combined costs of fuel, grid energy and battery degradation – is here proposed. A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters. The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign.     

基于随机模型预测控制的插电式混合动力汽车多目标能量管理策略

为了改善插电式混合动力汽车的燃油消耗和排放, 开展多目标随机模型预测控制策略的研究. 首先, 建立适用于模型预测的多元线性回归的发动机和电池模型, 建立融合燃油消耗和排放的多目标价值函数的模型预测控制, 随后, 基于随机驾驶员模型未来时刻的车速, 结合交通信息并利用动态规划(DP)算法进行参考电荷状态(SOC)优化, 进而建立多目标随机模型预测控制策略. 最后, 通过与DP, MPC等策略进行对比验证, 及给出两组不同权值进行多目标控制效果分析. 结果表明, 该策略的燃油消耗和排放最接近DP的控制效果, 且设置不同权重值可获得相应的控制目标, 说明该策略对提升燃油消耗和排放的多目标性能的有效性.     

Model predictive control of a rotary cement kiln

A first principles model of a cement kiln is used to control and optimize the burning of clinker in the cement production process. The model considers heat transfer between a gas and a feed state via convection and radiation. Furthermore, it contains effects such as chemical reactions, feed transport, energy losses and energy input. A model predictive controller is used to stabilize a temperature profile along the rotary kiln, guarantee good combustion conditions and maximize production. Moving horizon estimation was used for online estimation of selected model parameters and unmeasured states. Results from the pilot site are presented.     

Transient model-based operation guidance on blast furnace

To reduce the heat variance and realize the stable operation of a blast furnace, an operation guidance system was developed. This system involves nonlinear model predictive control based on a new transient model and provides appropriate guidance to operators. Accurate future prediction is a key to success because the huge heat capacity of the blast furnace leads to slow, complex dynamics with long dead time. The online validation results in the actual furnace showed that the standard deviation of the hot metal temperature was reduced by 3.7 °C when the operators followed the guidance provided by the developed system.     

Comparison of three control strategies for optimization of spray dryer operation

Spray drying is the preferred process to reduce the water content of many chemicals, pharmaceuticals, and foodstuffs. A significant amount of energy is used in spray drying to remove water and produce a free flowing powder product. In this paper, we present and compare the performance of three controllers for operation of a four-stage spray dryer. The three controllers are a proportional-integral (PI) controller that is used in industrial practice for spray dryer operation, a linear model predictive controller with real-time optimization (MPC with RTO, MPC-RTO), and an economically optimizing nonlinear model predictive controller (E-NMPC). The MPC with RTO is based on the same linear state space model in the MPC and the RTO layer. The E-NMPC consists of a single optimization layer that uses a nonlinear system of ordinary differential equations for its predictions. The PI control strategy has a fixed target that is independent of the disturbances, while the MPC-RTO and the E-NMPC adapt the operating point to the disturbances. The goal of spray dryer operation is to optimize the profit of operation in the presence of feed composition and ambient air humidity variations; i.e. to maximize the production rate, while minimizing the energy consumption, keeping the residual moisture content of the powder below a maximum limit, and avoiding that the powder sticks to the chamber walls. We use an industrially recorded disturbance scenario in order to produce realistic simulations and conclusions. The key performance indicators such as the profit of operation, the product flow rate, the specific energy consumption, the energy efficiency, and the residual moisture content of the produced powder are computed and compared for the three controllers. In this simulation study, we find that the economic performance of the MPC with RTO as well as the E-NMPC is considerably improved compared to the PI control strategy used in industrial practice. The MPC with RTO improves the profit of operation by 8.61%, and the E-NMPC improves the profit of operation by 9.66%. The energy efficiency is improved by 6.21% and 5.51%, respectively.     

Cooperative energy management of automated vehicles

This paper presents a cooperative adaptive cruise controller that controls vehicles along a planned route in a possibly hilly terrain, while keeping safe distances among the vehicles. The controller consists of two predictive layers that may operate with different update frequencies, horizon lengths and model abstractions. The top layer plans kinetic energy in a centralized manner by solving a quadratic program, whereas the bottom layer optimizes gear in a decentralized manner by solving a dynamic program. The efficiency of the proposed controller is shown through several case studies with different horizon lengths and number of vehicles in the platoon.     

A control benchmark on the energy management of a plug-in hybrid electric vehicle

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting–charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.     

Ontology-based facility data model for energy management

Context: Definition of a comprehensive facility data model is a prerequisite for providing more advanced energy management systems capable of tackling the underlying heterogeneity of complex infrastructures, thus providing more flexible data interpretation and event management, advanced communication and control system capabilities. Objective: This paper proposes one of the possible implementations of a facility data model utilizing the concept of ontology as part of the contemporary Semantic Web paradigm. Method: The proposed facility ontology model was defined and developed to model all the static knowledge (such as technical vendor data, proprietary data types, and communication protocols) related to the significant energy consumers of the target infrastructure. Furthermore, this paper describes the overall methodology and how the common semantics offered by the ontology were utilized to improve the interoperability and energy management of complex infrastructures. Initially, a core facility ontology, which represents the generic facility model providing the general concepts behind the modelling, was defined. Results: In order to develop a full-blown model of the specific facility infrastructure, Malpensa and Fiumicino airports in Italy were taken as a test-bed platform in order to develop the airport ontology owing to the variety of the technical systems installed at the site. For the development of the airport ontology, the core facility ontology was first extended and then populated to reflect the actual state of the target airport facility. Conclusion: The developed ontology was tested in the environment of the two pilots, and the proposed solution proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers.     

An optimal regulation strategy with disturbance rejection for energy management of hybrid electric vehicles

The energy management problem of finding the optimal split between the different sources of energy in a charge-sustaining parallel HEV, ensuring stability and optimality with respect to a performance objective (fuel consumption minimization over a driving cycle), is addressed in this paper. The paper develops a generic stability and optimality framework within which the energy management problem is cast in the form of a nonlinear optimal regulation (with disturbance rejection) problem and a control Lyapunov function is used to design the control law. Two theorems ensuring optimality and asymptotic stability of the energy management strategy are proposed and proved. The sufficient conditions for optimality and stability are used to derive an analytical expression for the control law as a function of the battery state of charge/state of energy and system parameters. The control law is implemented in a simplified backward vehicle simulator and its performance is evaluated against the global optimal solution obtained from dynamic programming. The strategy performs within 4% of the benchmark solution while guaranteeing optimality and stability for any driving cycle.     

An adaptive system for modelling and simulation of electrical arc furnaces

Modelling and simulator development for electric arc furnaces (EAFs) are of significant importance in designing control systems and in performance optimisation of EAFs. This paper presents a method based on adaptive neuro-fuzzy inference systems (ANFIS) for modelling and simulating EAFs with the focus on the regulator loop that is used for positioning the electrodes. The effectiveness of the simulator is shown through experiments by comparing the simulator outputs with actual plant data, using the EAF of Gerdau Ameristeel Whitby (GAW) in Ontario, Canada.