Development and application of cold strip rolling flatness control system in Ansteel

In this paper,the flatness control technology AnShaperTM for cold-rolling mill and industry application are introduced.AnShaperTM includes;partitioning piezomagnetic shape meter for flatness measurement for cold-rolling strip;flatness measured signals processing system based on digital signal processing（DSP）;flatness feedback control model system based on the control efficiency of flatness control actuators and model adaptive function.The application verifies that strip flatness can meet the need of high quality product by using AnShaperTM.The average flatness quality is about 5Ⅰ-unit and the 0.18 mm ultrathin thickness strip flatness is 10Ⅰ-unit.

Improvement in shape control performance of finishing mills in endless rolling production line

Donghua steel continuous casting-rolling(DSCCR)production line is an endless rolling production line independently integrated and developed by a Chinese enterprise.To solve the problem of out-of-control shape caused by insufficient adjustment ability of work roll bending force of DSCCR finishing mills,the backup roll contours of the finishing mill were optimized considering multi-objectives,and varying contact length backup roll(VCR)contours matching the concave work rolls were designed based on the rapid rolls-strip deformation calculation model,which integrated the elastic deformation of the rolls and the fast plastic deformation of the rolled strip.The simulation results showed that VCR configuration can significantly enhance the control efficiency of the work roll bending force and increase the transverse stiffness of rolling mill compared with the conventional roll configuration.In addition,VCR backup roll can improve the contact state between backup roll and work roll.The industrial production showed that the bending force setup value was more reasonable and the strip crown control accuracy was improved.

Data-based flatness prediction and optimization in tandem cold rolling

In cold rolling process,the flatness actuator efficiency is the basis of the flatness control system.The precision of flatness is determined by the setpoints of flatness actuators.In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process,it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately.Based on the production data,a method to obtain the flatness actuator efficiency by using partial least square(PLS)combined with orthogonal signal correction(OSC)was adopted.Compared with the experiential method and principal component analysis method,the OSC-PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand.Furthermore,kernel partial least square combined with artificial neural network(KPLS-ANN)was proposed to predict the flatness values and optimize the setpoints of flatness actuators.Compared with KPLS or ANN,KPLS-ANN shows the best predictive ability.The root mean square error,mean absolute error and mean absolute percentage error are 0.51 IU,0.34 IU and 0.09,respectively.After the setpoints of flatness actuators are optimized,KPLS-ANN shows better optimization ability.The result in an average flatness standard deviation is 2.22 IU,while the unoptimized value is 4.10 IU.

An efficient energy-management strategy for a DC microgrid powered by a photovoltaic/fuel cell/battery/supercapacitor

The outcome of this paper is to suggest an efficient energy-management strategy(EMS)for a direct-current(DC)microgrid(MG).The typical MG is composed of two renewable energy sources[photovoltaic(PV)systems and fuel cells(FCs)]and two energy-storage elements(lithium-ion battery and supercapacitor).An EMS was proposed to ensure optimal bus voltage with a power-sharing arrangement between the load and the sources.As a result,in the suggested DC MG,non-linear flatness control theory was used instead of the traditional proportional-integral control approach.The suggested EMS is intended to supply high power quality to the load under varying load conditions with fluctuating solar irradiation while considering the FC status.To validate and prove the effectiveness of the proposed EMS,a MATLAB®environment was used.In addition,the output power of the PV system was maximized using the particle swarm optimization algorithm as a maximum power point tracking(MPPT)technique to track the MPP of the 3000-W PV system under different irradiance conditions.The results show that the suggested EMS delivers a stable and smooth DC bus voltage with minimum overshoot value(0.1%)and improved ripple content(0.1%).As a result,the performance of the DC MG was enhanced by employing the flatness control theory,which provides higher power quality by stabilizing the bus voltage.