A Novel Soft Sensor for Chinese Tea Characterization

In cases of measurement,like the beverage discrimination or waste water monitoring,since sensors with direct measuring for specific variables cannot be practically applied,the interest in soft sensor technology,which is based on the acquisition and analysis for a lot of information variables,has rapidly increased.This article presents a novel soft sensor which activates a voltage signal between two specially-designed electrodes,inside the Chinese tea solution,and measures the produced current.The proposed sensor discriminated four different kinds of tea,and the dissolution processes of two of them are described as curves on a bidimensional plane.The results indicate that the proposed approach can also be adapted in characterization or discrimination for other solutions.

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.

Rapid Evaluation for Feed-axis Lubrication Condition Based on Soft Sensor

Stribeck effect is regarded as the most important feed-axis friction characteristics. According to the relationship between friction and lubrication,a rapid technology for feed-axis lubrication condition evaluation of computer numerical control( CNC) machine tools based on soft sensor is proposed. To obtain its state information,the static friction force,Coulomb friction force,and viscous coefficient are used as the key parameters of the soft sensor for tread analysis. Then the various amplitude and velocity triangular wave test curve, and a precise nonlinear model identification method are presented. The results of the experiments analysis show that this method is feasible and reliable for evaluating feed-axis lubrication condition,which lays the foundation for on-line condition monitoring and reliability evaluation for feed-axis lubrication of machine tools.

Outdoor Temperature Estimation Using ANFIS for Soft Sensors

In recent years,several studies using smart methods and soft computing in the field of HVAC systems have been provided.In this paper,we propose a framework which will strengthen the benefits of the Fuzzy Logic(FL)and Neural Fuzzy(NF)systems to estimate outdoor temperature.In this regard,Adaptive Neuro Fuzzy Inference System(ANFIS)is used in effective combination of strategic information for estimating the outdoor temperature of the building.A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory.Due to ANFIS accuracy in specialized predictions,it is an effective device to manage vulnerabilities of each experiential framework.The NF system can concentrate on measurable properties of the samples throughout the preparation sessions.Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data.This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored.The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

介绍是一个多重模型基于亲密关系，繁殖(AP ) ， Gaussian 过程(GP ) 和贝叶斯的委员会用机器制造的软察觉到方法(BCM ) 。聚类算术的 AP 被用来根据他们的操作的点聚类训练样品。然后，亚模型被 Gaussian 过程回归(GPR ) 估计。最后，以便得到全球概率的预言，贝叶斯的委员会机器被用来联合亚评估者的产量。建议方法被使用了在氢化裂解器分馏器预言轻石油结束点。实际应用显示它为在化学过程监视的质量的联机预言是有用的。

Calibration of soft sensor by using Just-in-time modeling and Ada Boost learning method

Soft sensor is an efficacious solution to predict the hard-to-measure target variable by using the process variables.In practical application scenarios, however, the feedback cycle of target variable is usually larger than that of the process variables, which causes the deficiency of prediction errors. Consequently soft sensor cannot be calibrated timely and deteriorates. We proposed a soft sensor calibration method by using Just-in-time modeling and Ada Boost learning method. A moving window consisting of a primary part and a secondary part is constructed.The primary part is made of history data from certain number of constant feedback cycles of target variable and the secondary part includes some coarse target values estimated initially by Just-in-time modeling during the latest feedback cycle of target variable. The data set of the whole moving window is processed by Ada Boost learning method to build an auxiliary estimation model and then target variable values of the latest corresponding feedback cycle are reestimated. Finally the soft sensor model is calibrated by using the reestimated target variable values when the target feedback is unavailable; otherwise using the feedback value. The feasibility and effectiveness of the proposed calibration method is tested and verified through a series of comparative experiments on a pH neutralization facility in our laboratory.

Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization(HDS) process was proposed. Therefore, an integrated approach using support vector regression(SVR) based on wavelet transform(WT) and principal component analysis(PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance(EAARE=0.058 and R2=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression(MLR), SVR and PCA-SVR.

Soft sensor modeling based on Gaussian processes

In order to meet the demand of online optimal running, a novel soft sensor modeling approach based on Gaussian processes was proposed. The approach is moderately simple to implement and use without loss of performance. It is trained by optimizing the hyperparameters using the scaled conjugate gradient algorithm with the squared exponential covariance function employed. Experimental simulations show that the soft sensor modeling approach has the advantage via a real-world example in a refinery. Meanwhile, the method opens new possibilities for application of kernel methods to potential fields.

Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

A novel data-driven, soft sensor based on support vector regression(SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization(HDS) process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model.Therefore, a hybrid approach using a combination of genetic algorithm(GA) and sequential quadratic programming(SQP)methods(GA–SQP) was developed. Performance of different optimization algorithms including GA–SQP, GA, pattern search(PS), and grid search(GS) indicated that the best average absolute relative error(AARE), squared correlation coefficient(R2), and computation time(CT)(AARE = 0.0745, R2= 0.997 and CT = 56 s) was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization(VQ) technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.

Soft Sensor for Inputs and Parameters Using Nonlinear Singular State Observer in Chemical Processes

Chemical processes are usually nonlinear singular systems.In this study,a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes,which are augmented as state variables.Based on the observability of the singular system,this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters.When the observability is satisfied,the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer.The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation.With the catalyst circulation rate as the only unknown input without model error,one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst circulation rate.However,when uncertain model parameters also exist,additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

4-CBA Soft Sensor Based on Fuzzy CMAC Neural Networks

Soft sensor is attractive in dealing with online product quality measurement by virtue of other easily measured variables. In AMOCO PTA (purified terephthalic acid) production process, the unavailability of real-time measurement of 4-CBA makes it impossible for timely adjustment and thereby influences the product quality and the plant economy benefit. In this paper, a kind of FCMAC (fuzzy cerebellar model articulation controller) method is presented to solve the online measurement problem. Different from the conventional CMAC (cerebellar model articulation controller) networks, which has inferior smoothing ability because of its table look-up based technology. Integrating fuzzy model into CMAC networks, it becomes more accurate in functional mapping without weakening its generalization ability. Numerical example and industrial application results show the method proposed here is satisfactory and feasible.

Soft sensor for ratio of soda to aluminate based on PCA-RBF multiple network

Based on principal component analysis, a multiple neural network was proposed. The principal component analysis was firstly used to reorganize the input variables and eliminate the correlativity. Then the reorganized variables were divided into 2 groups according to the original information and 2 corresponding neural networks were established. A radial basis function network was used to depict the relationship between the output variables and the first group input variables which contain main original information. An other single-layer neural network model was used to compensate the error between the output of radial basis function network and the actual output variables. At last, The multiple network was used as soft sensor for the ratio of soda to aluminate in the process of high-pressure digestion of alumina. Simulation of industry application data shows that the prediction error of the model is less than 3%, and the model has good generalization ability.

Data-driven soft sensors in blast furnace ironmaking:a survey

The blast furnace is a highly energy-intensive,highly polluting,and extremely complex reactor in the ironmaking process.Soft sensors are a key technology for predicting molten iron quality indices reflecting blast furnace energy consumption and operation stability,and play an important role in saving energy,reducing emissions,improving product quality,and producing economic benefits.With the advancement of the Internet of Things,big data,and artificial intelligence,data-driven soft sensors in blast furnace ironmaking processes have attracted increasing attention from researchers,but there has been no systematic review of the data-driven soft sensors in the blast furnace ironmaking process.This review covers the state-of-the-art studies of data-driven soft sensors technologies in the blast furnace ironmaking process.Specifically,wefirst conduct a comprehensive overview of various data-driven soft sensor modeling methods(multiscale methods,adaptive methods,deep learning,etc.)used in blast furnace ironmaking.Second,the important applications of data-driven soft sensors in blast furnace ironmaking(silicon content,molten iron temperature,gas utilization rate,etc.)are classified.Finally,the potential challenges and future development trends of data-driven soft sensors in blast furnace ironmaking applications are discussed,including digital twin,multi-source data fusion,and carbon peaking and carbon neutrality.

Soft Sensors for Property‑Controlled Multi‑Stage Press Hardening of 22MnB5

In multi-stage press hardening,the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps.To measure these properties and finally to control them by feedback,two soft sensors are developed in this work.The press hardening of 22MnB5 sheet material in a progressive die,where the material is first rapidly austenitized,then pre-cooled,stretch-formed,and finally die bent,serves as the framework for the development of these sensors.To provide feedback on the temporal and spatial temperature distribution,a soft sensor based on a model derived from the Dynamic mode decomposition(DMD)is presented.The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature(-distribution)as a function of all process-relevant control vari-ables.The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10°C during the process-relevant time steps.For the online prediction of the final microstructure,an artificial neural network(ANN)-based microstructure soft sensor is developed.As part of this,a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented,along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248(PHS_BMW).The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8%to a reference simulation.

Local multi-model integrated soft sensor based on just-in-time learning for mechanical properties of hot strip mill process

The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately,a soft sensor based on ensemble local modeling was proposed.Firstly,outliers of process data are removed by local outlier factor.After standardization and transformation,normal data that can be used in the model are obtained.Next,in order to avoid redundant variables participating in modeling and reducing performance of models,feature selection was applied combing the mechanism of hot rolling process and mutual information among variables.Then,features of samples were extracted by supervised local preserving projection,and a prediction model was constructed by Gaussian process regression based on just-in-time learning(JITL).Other JITL-based models,such as support vector regression and gradient boosting regression tree models,keep all variables and make up for the lost information during dimension reduction.Finally,the soft sensor was developed by integrating individual models through stacking method.Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.

Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks

This paper presents developing soft sensors for polymer melt index in an industrial polymerization process by using deep belief network(DBN).The important quality variable melt index of polypropylene is hard to measure in industrial processes.Lack of online measurement instruments becomes a problem in polymer quality control.One effective solution is to use soft sensors to estimate the quality variables from process data.In recent years,deep learning has achieved many successful applications in image classification and speech recognition.DBN as one novel technique has strong generalization capability to model complex dynamic processes due to its deep architecture.It can meet the demand of modelling accuracy when applied to actual processes.Compared to the conventional neural networks,the training of DBN contains a supervised training phase and an unsupervised training phase.To mine the valuable information from process data,DBN can be trained by the process data without existing labels in an unsupervised training phase to improve the performance of estimation.Selection of DBN structure is investigated in the paper.The modelling results achieved by DBN and feedforward neural networks are compared in this paper.It is shown that the DBN models give very accurate estimations of the polymer melt index.

Phenomenological Based Soft Sensor for Online Estimation of Slurry Rheological Properties

This work proposes a soft sensor based on a phenomenological model for online estimation of the density and viscosity of a slurry flowing through a pipe-and-fittings assembly(PFA). The model is developed considering the conservation principle applied to mass and momentum transfer and considering frictional energy losses to include the variables directly affecting slurry properties. A reported proposal for state observers with unknown inputs is used to develop the first block of the observer structure. The second block is constructed with two options for evaluating slurry viscosity, generating two possible estimator structures, which are tested using real data. A comparison between them indicates different uses and capabilities according to available process information.

Melt Index Prediction by Neural Soft-Sensor Based on Multi-Scale Analysis and Principal Component Analysis

熔体流动指数( MI )的预言，在决定产品的最重要的参数“ s 等级和在实际工业进程生产的聚丙烯的质量管理， isstudied.A 小说有主要组分分析( PCA )的软传感器的模型，光线的基础函数( RBF )网络，并且多尺度的分析( MSA )被建议从真实进程变量推断生产产品的 MI ，在 PCA 被执行选择最相关的进程特征并且消除输入变量的关联的地方， MSA 被介绍获得更多信息并且减少系统的不确定性，并且 RBF 网络被用来描绘这进程的非线性。研究结果证明建议方法提供有希望的预言可靠性和精确性，并且想了在丙烯聚合过程有广泛的应用程序前景。

Component Content Soft-sensor Based on Neural Networks in Rare-earth Countercurrent Extraction Process

Throught fusion of the mechanism modeling and the neural networks modeling,a compo- nent content soft-sensor,which is composed of the equilibrium calculation model for multi-component rare earth extraction and the error compensation model of fuzzy system,is proposed to solve the prob- lem that the component content in countercurrent rare-earth extraction process is hardly measured on-line.An industry experiment in the extraction Y process by HAB using this hybrid soft-sensor proves its effectiveness.