Modeling and Optimization of Caliber Rolling Process

2006 ◽  
Vol 129 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Su-Hai Hsiang ◽  
Sheng-Li Lin
Keyword(s):  
Simulation Model ◽  
Simulated Annealing Algorithm ◽  
Back Propagation ◽  
Rolling Process ◽  
Processing Parameters ◽  
Back Propagation Neural Network ◽  
Optimal Processing ◽  
Annealing Algorithm ◽  
Searching Method ◽  
Short Time

In practice of caliber rolling, it is highly desirable to obtain optimal processing parameters to achieve the best quality in a short time. A sophisticated and faster simulation model of caliber rolling is proposed, and then a global optimal searching method—simulated annealing algorithm (SA)—is applied to find the optimal processing parameters of caliber rolling. The simulation model of caliber rolling in this paper was established by the back-propagation neural network (BPNN) model, which replaces the complex numerical analytical model, proposed in an earlier paper. This replacement improved the speed of simulation significantly.

Operational Parameters and Back Propagation Neural Network (BPNN) Simulation Model of Integration Membrane Bioreactor (IMBR) Treating Sewage from Ship

2011 ◽  
Vol 356-360 ◽  
pp. 1042-1045
Author(s):  
Yue Shi ◽  
Liang Guo ◽  
Jun Zhou ◽  
Run Bai ◽  
Xin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Membrane Bioreactor ◽  
Ph Value ◽  
Back Propagation ◽  
Influence Factors ◽  
Back Propagation Neural Network ◽  
Living Environment ◽  
Operational Parameters ◽  
Scale Experiment

Nowadays, in order to meet the new standard of IMO for sewage discharged from ship treatment, membrane bioreactor (MBR) was widely used in this field. In this study, a novel bioreactor named integration membrane bioreactor (IMBR) was used to treat sewage from ship. A lab scale experiment was conducted to find the best controlling strategy of operation. The results were as follows: The IMBR had strong adaptability and effluent stability under wide change in VLR which was from 1.2kg/m3.d to 4.3kg/m3.d; The HRT of the IMBR was suggested to be controlled around 6h; The IMBR operator was better in alkali-resistant and weaker in acid-proof, which implied the pH of suitable living environment for aerobic microbe should be higher than 6.5. At the same time, a simulation model of operational parameters was established based on theory of back propagation neural network (BPNN). The simulation model realizes prediction of which were the key impact factor and optimum operational parameters of the IMBR system. Each parameter influencing the performance of the reactor was compared using the method of partitioning connection weights (PCW). The weight of the influence factors was pH value> DO>influent COD in the experimental range.

scholarly journals Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 425 ◽  
Author(s):  
Xu ◽  
Feng ◽  
Xing
Keyword(s):  
Simulated Annealing Algorithm ◽  
Least Squares Method ◽  
Back Propagation ◽  
Humidity Sensors ◽  
Modeling And Analysis ◽  
Genetic Simulated Annealing Algorithm ◽  
Different Temperatures ◽  
Temperature Ranges ◽  
Annealing Algorithm ◽  
Adaptive Ability

In addition to being sensitive to humidity, humidity sensors with moisture sensitive elements are also sensitive to ambient temperature. The fusion of temperature and humidity data is an effective way to improve the accuracy of humidity sensors. In view of the problem of insufficient adaptive ability and poor universality in the current compensation algorithm, a piecewise processing of measured error at different temperatures by using multiple linear regression is proposed in this paper. The least squares method and back propagation (BP) neural network improved by a genetic simulated annealing algorithm (GSA-BP) were used to compensate the measured humidity data of different temperature ranges. The efficiency of the GSA-BP algorithm was tested, and the compensation function model was established. The compensation accuracy was also compared with the accuracies obtained by other methods. The experimental results show that the adaptive segmentation compensation method can significantly improve the measured error of the humidity sensor over a wide temperature range.

The Application of Simulated Annealing in Chiller Energy Consumption Optimization

2012 ◽  
Vol 462 ◽  
pp. 392-397 ◽  
Author(s):  
Bin Fei Li ◽  
Yan Min Song ◽  
Jun Pei ◽  
Jing Min Yang
Keyword(s):  
Simulated Annealing ◽  
Energy Consumption ◽  
Simulation Model ◽  
Simulated Annealing Algorithm ◽  
Energy Consumption Optimization ◽  
Reduce Energy Consumption ◽  
Annealing Algorithm ◽  
Energy Consumption Simulation

This paper based on the energy consumption simulation model of chiller system, by analyzing the composition of chiller energy consumption, proposes to compute the best optimal controlling parameters by using simulated annealing algorithm in order to reduce energy consumption. The optimizing process of the best settings of each controllable variable is introduced which use the simulated annealing algorithm in the running chiller system, and the effects of simulated annealing running has been simulated.

Modeling of the Hot Flow Behaviors for Ti-6Al-4V-0.1Ru Alloy by GA-BPNN Model and Its Application

2018 ◽  
Vol 37 (6) ◽  
pp. 551-562 ◽  
Author(s):  
Yu-ting Zhou ◽  
Yu-feng Xia ◽  
Lai Jiang ◽  
Shuai Long ◽  
Dong Yang
Keyword(s):  
Strain Rate ◽  
Back Propagation ◽  
Interaction Space ◽  
Processing Parameters ◽  
Back Propagation Neural Network ◽  
Strain Rate Range ◽  
Compression Tests ◽  
Strain And Strain Rate ◽  
Deformation Behaviors ◽  
Bpnn Model

AbstractA series of compression tests were performed on Ti-6Al-4V-0.1Ru titanium alloy in nine temperatures between 750 and 1150 °C and a strain rate range of 0.01 to 10s−1. The hot deformation behaviors of Ti-6Al-4V-0.1Ru showed highly non-linear intrinsic relationships with temperature, strain and strain rate. The flow curves exhibited different softening mechanisms, dynamic recrystallization (DRX) and dynamic recovery (DRV). In this study, the rheological behaviors of Ti-6Al-4V-0.1Ru were modeled using a special hybrid prediction model, where genetic algorithm (GA) was implemented to do a back-propagation neural network (BPNN) weights optimization, namely GA-BPNN. Subsequently, the predicted results were compared with experimental values and GA-BPNN model showed the ability to predict the flow behaviors of Ti-6Al-4V-0.1Ru with superior accuracy. Then a 3-D continuous interaction space was constructed to visually reveal the successive relationships among processing parameters. Finally, the predicted data were applied to process simulation and accuracy results were achieved.

Back-Propagation Model for Nanofiltration Process Simulation in Pesticide Wastewater Treatment

2010 ◽  
Vol 168-170 ◽  
pp. 404-407 ◽  
Author(s):  
Qing Yang ◽  
Yong Ju Hu ◽  
Liang Xue
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Process Simulation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Propagation Model ◽  
The Stability ◽  
Back Propagation Model ◽  
Good Agreement ◽  
Separation Model

This study simulated the nanofiltration (NF) process of contamination removing by back-propagation neural network (BPNN), according to the test values of DK membrane pre-treating Imidacloprid pesticide wastewater. The real time nanofiltration (NF) separation model was presented for effective controlling of DK NF separation. The research showed the simulation precision met the application demands, with the correlation coefficient between the simulation and test rejection of COD and salt over 0.99, and absoluteness error below ±4%. In order to test the prediction of this BPNN simulation model, further NF experiments were carried out. Under the same multifactor condition, the predictions for the NF process performances were found to be in good agreement with the experimental results. This BP simulation model for NF process could be used to test the stability and effectively of NF system, and support the membrane technology well.

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