Soft-sensing Design Based on Semiclosed-loop Framework

Soft-sensing is widely used in industrial applications. The traditional soft-sensing structure is open-loop without correction mechanism. If the working condition is changed or there is unknown disturbance, the forecast result of soft-sensing model may be incorrect. In order to obtain accurate values, it is necessary to carry out online correction. In this paper, a semiclosed-loop framework (SLF) is proposed to establish a soft-sensing approach, which estimates the input variables in the next moment by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and robustness than other open-loop models.

Soft-Sensing Method of Water Temperature Measurement for Controlled Cooling System

Aiming at the water temperature measuring problem for controlled cooling system of rolling plant,a new water temperature measuring method based on soft-sensing method with a water temperature model of on-line self correction parameter was built.A water temperature compensation factor model was also built to improve coiling temperature control precision.It was proved that the model meets production requirements.The soft-sensing technique has extensive applications in the field of metal forming.

A soft-sensing model on hydraulic excavator's backhoe vibratory excavating resistance based on fuzzy support vector machine

In order to measure the backhoe vibratory excavating resistance of a hydraulic excavator fast and precisely,the influences of vibratory excavating depth,angle,vibratory frequency,amplitude,bucket inserting velocity and soil type on the vibratory excavating resistance were analyzed.Simulation analysis was carded out to establish the bucket inserting velocity,amplitude and vibratory frequency considered as secondary variables and excavating resistance as primary variable.A fttzzy membership function was introduced to improve the anti-noise capacity of support vector machine,which is a soft-sensing model on the hydraulic excavator＇s backhoe vibratory excavating resistance based on fuzzy support vector machine.The simulation result reveals that its maximum relative training and testing error are nearly 0.68% and-0.47%,respectively.It is concluded that the model has quite high modeling precision and generalization capacity,and it can measure the vibratory excavating resistance accurately,reliably and fast in an indirect way.

Hybrid intelligent control of combustion process for ore-roasting furnace

Because of its synthetic and complex characteristics, the combustion process of the shaft ore-roasting furnace is very difficult to control stably. A hybrid intelligent control approach is developed which consists of two systems： one is a cascade fuzzy control system with a temperature soft-sensor, and the other is a ratio control system for air flow with a compensation model for heating gas flow and air-fuel ratio. This approach combined intelligent control, soft-sensing and fault diagnosis with conventional control. It can adjust both the heating gas flow and the air-fuel ratio in real time. By this way, the difficulty of online measurement of the furnace temperature is solved, the fault ratios during combustion process is decreased, the steady control of the furnace temperature is achieved, and the gas consumption is reduced. The successful application in shaft furnaces of a mineral processing plant in China indicates its effectiveness.

Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high cost. One inherent difficulty is insufficiency of the training samples because the labeled data are limited. Besides, the traditional soft-sensing structure has no online correction mechanism. The forecasting result may be incorrect if the working condition is changed. In this work, a semi-supervised learning(SSL) method is proposed to build the soft-sensing model by use of the unlabeled data. Meanwhile, an online correction mechanism is proposed to establish a soft-sensing approach. The mechanism estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation model. The experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

Soft-sensing modeling of chemical oxygen demand in photo-electro-catalytic oxidation treatment of papermaking wastewater

Photo-electro-catalytic(PEC)oxidation has been widely recognized as an effective technology for advanced treatment of papermaking wastewater.To optimize the oxidation process,it is important of monitor continuously the chemical oxygen demand(COD)of inflow and outflow wastewater.However,online COD sensors are expensive difficult to maintain,and therefore COD is usually analyzed off-line in laboratories in most cases.The objective of this study is to develop an inexpensive method for on-line COD measurement.The oxidation-reduction potential(ORP),pH,and dissolved oxygen(DO)of wastewater were selected as the key parameters,which consists of four different types of artificial neural network(ANNs)methods:multi-layer perceptron neural network(MLP),back propagation neural network(BPNN),radial basis neural network(RBNN)and generalized regression neural network(GRNN).These parameters were applied in the development of COD soft-sensing models.Six batches of papermaking wastewater with different pollution loads were treated with PEC technology over a period of 90 minutes,and a total of 546 data points was collected,including the on-line measurements of ORP,pH and DO,as well as off-line COD data.The 546 data points were divided into training set(410 data,75%of total)and validation set(136 data,25%of total).Four statistical criteria,namely,root mean square error(RMSE),mean absolute error(MAE),mean absolute relative error(MARE),and determination coefficient(R2)were used to assess the performance of the models developed with the training set of data.The comparison of results for the four ANN models for COD soft-sensing indicated that the RBNN model behaved most favorably,which possessed precise and predictable results with R2=0.913 for the validation set.Lastly,the proposed RBNN model was applied to a new batch of PEC oxidation of papermaking wastewater,and the results indicated that the model could be applied successfully for COD soft-sensing for the wastewater.