Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

The supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.     

基于预前控制异步电机直接转矩控制方案的研究

在传统异步电动机直接转矩控制方案中，由于负载的变化规律不可预测，因此其常会带来较大的开关频率的变化，为此提出了一种基于预前控制的异步电动机直接转矩控制的方法。该方法依据前一个周期的磁链和转矩误差，对下一个开关周期所应施加到异步电动机的定子电压矢量进行预测，然后借助空间矢量PWM的方法，合成此开关电压矢量。样机实验结果表明，该方案不但能维持逆变器的开关频率基本恒定，而且还具有比传统直接转矩控制更为优良的动静态特性。     

An evaluation system of the setting up of predictive maintenance programmes

Predictive Maintenance can provide an increase in safety, quality and availability in industrial plants. However, the setting up of a Predictive Maintenance Programme is a strategic decision that until now has lacked analysis of questions related to its setting up, management and control. In this paper, an evaluation system is proposed that carries out the decision making in relation to the feasibility of the setting up. The evaluation system uses a combination of tools belonging to operational research such as: Analytic Hierarchy Process, decision rules and Bayesian tools. This system is a help tool available to the managers of Predictive Maintenance Programmes which can both increase the number of Predictive Maintenance Programmes set up and avoid the failure of these programmes. The Evaluation System has been tested in a petrochemical plant and in a food industry.     

Case Study of an Aerosol Explosion and a Method to Determine Explosion Temperatures

A failure analysis case study is presented for a two-piece aerosol containing tetrafluoroethane, commonly referred to as Refrigerant 134a. A gentleman was preparing to recharge the air conditioning system of an automobile when the bottom exploded off the aerosol container, propelling the body of the aerosol container like a rocket, which hit the man in the eye and blinded him in that eye. The aerosol was never connected to the air conditioner, therefore backpressure from the air conditioner (AC) compressor was ruled out as a cause for the explosion. The objective of the study was to determine why the aerosol exploded. Several recently developed test methods were used, including two types of heat-to-burst tests and a puncture chamber to measure the pressure-versus-temperature behavior of aerosols. More common test methods were also used, such as water bath pressure tests, hydro pressure burst tests, pneumatic pressure burst tests, hardness measurements, weight measurements, metallography, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and an accident scenario recreation. A semi-empirical correlation between the hardness and weights of the container bottoms was used to determine the explosion temperature and/or pressure. This semi-empirical correlation agrees in principle with an analysis of the explosion pressures using finite-element analysis (FEA). The root cause for the explosion was determined to be a lack of strength of the bottom of the two-piece aerosol coupled with heating the aerosol to temperatures significantly above room temperature.     

Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.     

Min-Max MPC based on a computationally efficient upper bound of the worst case cost

Min-Max MPC (MMMPC) controllers [P.J. Campo, M. Morari, Robust model predictive control, in: Proc. American Control Conference, June 10–12, 1987, pp. 1021–1026] suffer from a great computational burden which limits their applicability in the industry. Sometimes upper bounds of the worst possible case of a performance index have been used to reduce the computational burden. This paper proposes a computationally efficient MMMPC control strategy in which the worst case cost is approximated by an upper bound based on a diagonalization scheme. The upper bound can be computed with O(n³) operations and using only simple matrix operations. This implies that the algorithm can be coded easily even in non-mathematical oriented programming languages such as those found in industrial embedded control hardware. A simulation example is given in the paper.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.     

注气井井筒结垢预测研究

在油田开发过程中,油、气、水和泥浆在地层或管道中流动,当达到一定条件时,油垢、水垢或泥垢便会产生。在实际生产过程中,油垢和泥垢通常伴随着水垢而生成,所以对水垢结垢趋势的预测研究显得至关重要。介绍了Oddo-Tomos饱和指数法的理论预测模型,以及在生产过程中利用Oddo-Tomos饱和指数法预测油田结垢趋势的方法,阐述并分析了油田结垢对油田开采的影响。研究结果可为下一步的生产作业提供理论依据。     

多变量预测控制软件在加氢分馏装置控制中的应用

针对加氢分馏装置分馏塔工艺特点,重点分析加氢分馏塔所面临的控制问题,采用多变量预测控制软件APC—Adcon,通过实验测试、模型辨识、控制器和切换逻辑设计等工作实现该工业分馏塔系的多变量预测控制,显著提高两塔运行参数的平稳性,有效降低操作人员的操作难度。实际应用的结果表明该多变量预测控制系统可有效地克服干扰、确保工艺参数的平稳运行。通过该系统的应用使在相同的负荷下,燃料气减少5％,显著提高分馏塔的分离能力,组分切割更为合理,航空煤油收率提高0.4％,取得了显著的经济效益。     

电磁骚扰自动测试系统中的寻优算法设计

针对电磁骚扰测试转台的特殊要求,根据PID调节器和最小拍控制超调量小,响应速度快,跟随性能强的特点,设计了一种转台的最优控制算法。仿真结果表明,应用此控制方法可以实现电磁骚扰测试转台系统响应速度快、超调量小、控制效果好及抗干扰能力强的优点。该方法具有一定的实用意义和经济价值。