基于MI-LSSVM的水泥生料细度软测量建模

针对水泥生料细度软测量模型难以建立的问题,考虑到输入变量选择易受时延的影响,提出一种基于互信息和最小二乘支持向量机(MI-LSSVM)的软测量建模方法。该方法采用互信息表征变量间的相关性,进而解决水泥生料细度软测量建模中的时延问题,并在此基础之上,提出双向选择算法获取输入变量,将得到的输入变量应用于最小二乘支持向量机中,建立水泥生料细度软测量模型,最后应用水泥厂的实际数据对基于互信息和最小二乘支持向量机的水泥生料细度软测量模型进行仿真。结果表明该方法预测精度高、泛化能力强。     

Modeling and simulation of soft sensor design for real-time speed and position estimation of PMSM

This paper deals with the design of a speed soft sensor for permanent magnet synchronous motor. At high speed, model-based soft sensor is used and it gives excellent results. However, it fails to deliver satisfactory performance at zero or very low speed. High-frequency soft sensor is used at low speed. We suggest to use a model-based soft sensor together with the high-frequency soft sensor to overcome the limitations of the first one at low speed range.     

Soft sensor for an orifice flowmeter in presence of disturbances

Measurements using an orifice flowmeter are widely used in industry. In certain instances, the output of the flowmeter may be corrupted due to plate contamination, changes in fluid density, or incorrect insertion of the plate. This paper describes a method for estimating the correct output in the presence of such disturbances. First, a linear parameter-varying model of the orifice flowmeter is developed using data extracted from computational fluid dynamics simulations. The simulation and experimental output are found to have an average deviation of 6.5% and 3.49% in terms of the differential pressure and discharge coefficient, respectively. Observer-based estimators for the linear parameter-varying models are developed for different combinations of the settling time and maximum overshoot. These estimators enable the disturbance-induced output to be corrected close to the true value. The error in the disturbed output due to plate contamination is reduced from 45% to 0.82%. Similarly, the error due to an accidental change of plate decreases from 76% to 2.03%. Thus, the proposed estimator can be used to nullify the disturbances induced in the measurements from orifice flowmeters.     

A natural observer for optimal state estimation in second order linear structural systems

A state observer for mechanical and structural systems is derived in the context of the second order differential equation of motion of linear structural systems. The proposed observer possesses similar characteristics to the Kalman filter in the sense that it minimizes the trace of the state error covariance matrix within the predefined structure of the feedback gain. The main contribution of the paper consists of the fact that the proposed observer can be implemented directly as a modified linear finite element model of the system, subject to collocated corrective forces proportional to the measured response. The proposed algorithm is effectively illustrated in two different types of second order systems; a close-coupled spring–mass–damper multi-degree of freedom system and a plate subject to transverse vibrations.     

Added advantages in using a fiber Bragg grating sensor in the determination of early age setting time for cement pastes

A fiber Bragg grating (FBG) sensor was used to monitor the early age curing temperatures of cement paste. Additional advantages in using the sensor were highlighted. The FBG was inscribed by a Continuous Wave 244 nm argon ion laser in the photosensitivity fiber. The fabricated FBG was calibrated from room temperature to 105 °C. In this temperature range, the FBG was found to be good in terms of both the sensitivity and linearity which were around 9 pm/°C and 99.9%, respectively. A host specimen with ratio of Portland cement, sand and water of 800, 500, and 275 ml by volume was used in the experiment. Results showed that the FBG could determine the initial and the final early age setting times. The initial early age setting time for the cement paste was about 5 h and the final early age setting time was about 14 h after casting.     

Soft sensor based composition estimation and controller design for an ideal reactive distillation column

In this research work, the authors have presented the design and implementation of a recurrent neural network (RNN) based inferential state estimation scheme for an ideal reactive distillation column. Decentralized PI controllers are designed and implemented. The reactive distillation process is controlled by controlling the composition which has been estimated from the available temperature measurements using a type of RNN called Time Delayed Neural Network (TDNN). The performance of the RNN based state estimation scheme under both open loop and closed loop have been compared with a standard Extended Kalman filter (EKF) and a Feed forward Neural Network (FNN). The online training/correction has been done for both RNN and FNN schemes for every ten minutes whenever new un-trained measurements are available from a conventional composition analyzer. The performance of RNN shows better state estimation capability as compared to other state estimation schemes in terms of qualitative and quantitative performance indices.     

An intelligent estimation method for product design time

The planning and control of product development is based on the pre-estimation of product design time (PDT). In order to optimize the product development process (PDP), it is necessary for managers and designers to evaluate design time/effort at the early stage of product development. However, in systemic analytical methods for PDT this is somewhat lacking. This paper explores an intelligent method to evaluate the PDT regarding this problem. At the early development stage, designers lack sufficient product information and have difficulty in determining PDT via subjective evaluation. Thus, a fuzzy measurable house of quality (FM-HOQ) model is proposed to provide measurable engineering information. Quality function deployment (QFD) is combined with a mapping pattern of “function→principle→structure” to extract product characteristics from customer demands. Then, a fuzzy neural network (FNN) model is built to fuse data and realize the estimation of PDT, which makes use of fuzzy comprehensive evaluation to simplify structure. In a word, the whole estimation method consists of four steps: time factors identification, product characteristics extraction by QFD and function mapping pattern, FNN learning, and PDT estimation. Finally, to illustrate the procedure of the estimation method, the case of injection mold design is studied. The results of experiments show that the intelligent estimation method is feasible and effective. This paper is developed to provide designers with PDT information to help them in optimizing PDP.     

Recurrent Neural Network based Soft Sensor for flow estimation in Liquid Rocket Engine Injector calibration

Injector is the critical element in the Liquid Rocket Engine (LRE), to ensure proper mixing of propellants (fuel and oxidizer) in the thrust chamber for achieving the optimum thrust. LRE injector is calibrated in order to deliver required flow rates of propellants by sizing the orifices through simple injector water calibration (IWC) techniques. In LRE-IWC process, a huge 6” turbine flow meter (TFM) is employed for the flow-rate measurement. In order to achieve and maintain the required accuracy and precision in the LRE-IWC process, periodical calibration of TFM is mandatory. It involves tremendous time, cost and human effort. Soft sensors can provide an economical and effective alternative solution for TFM flow-rate measurement. The objective of the proposed work is to develop and implement a recurrent neural network based soft sensor (RNN-SS) for TFM flow-rate measurement. In the LRE-IWC process, experimental flow trials were carried out for different flow patterns, and the necessary measurement data were generated for the soft sensor design. The designed RNN-SS was trained by tuning various hyper parameters to replace the TFM, using three related measurement parameters acquired during the experimental trials. The precise TFM flow-rate estimation was achieved by the designed RNN-SS, with a worst-case mean absolute percentage error (MAPE) of 1.91% for the experimental flow patterns considered, with good repeat-ability. The proposed RNN-SS model for TFM flow-rate estimation gives a MAPE of 0.58%, for the required flow-pattern which is well suited for practical use.     

Software sensor for on-line estimation of the microbial activity in activated sludge systems

This paper considers the design of a software sensor (or soft-sensor) for the on-line estimation of the biological activities of a colony of aerobic micro-organisms acting on activated sludge processes, where the carbonaceous waste degradation and nitrification processes are taken into account. These bioactivities are intimately related to the dissolved oxygen concentration. Two factors that affect the dynamics of the dissolved oxygen are the respiration rate or the oxygen uptake rate (OUR) and the oxygen transfer function (K(l)a). These items are challenging topics for the application of recursive identification due the nonlinear characteristic of the oxygen transfer function, and to the time-varying feature of the respiration rate. In this work, OUR and the oxygen transfer function are estimated through a software sensor, which is based on a modified version of the discrete extended Kalman filter. Numerical simulations are carried out in a predenitrifying activated sludge process benchmark and the obtained results demonstrate the applicability and efficiency of the proposed methodology, which should provide a valuable tool to supervise and control activated sludge processes.