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.

A comparative study of different prediction models in predicting human resource demands

2021 ◽  
pp. 1-11
Author(s):  
Bo Huang
Keyword(s):  
Neural Network ◽  
Human Resources ◽  
Comparative Study ◽  
Human Resource ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Methods ◽  
Bpnn Model ◽  
Good Agreement

This study analyzed three prediction models: ID model, GM (1,1) model and back-propagation neural network (BPNN) model. Firstly, the principles of the three models were introduced, and the prediction methods of the three models were analyzed. Then, taking enterprise A as an example, the demand for human resources was predicted, and the prediction results of the three models were compared. The results showed that the maximum and minimum errors were 240 people and 12 people respectively in the prediction results of the ID3 model and 64 people and 37 people respectively in the prediction results of the GM (1, 1) model; the errors of the BPNN model were smaller than ten people, and the minimum value of the BPNN model was three people, which was in good agreement with the actual value. The prediction of the human resource demand of enterprise A in the future five years with the BPNN model suggested that the demand for employees would growing rapidly. The results show that the BPNN model has better reliability and can be popularized and applied in practice.

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 Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network

2020 ◽  
Vol 38 (6) ◽  
pp. 2485-2506
Author(s):  
Yapeng Tian ◽  
Binshan Ju ◽  
Yong Yang ◽  
Hongya Wang ◽  
Yintao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithms ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Propagation Model ◽  
Coefficient Of Determination ◽  
Co2 Flooding ◽  
Dragonfly Algorithm ◽  
Minimum Miscibility Pressure

CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

Prediction of Spring Back of the Two-Axle Rotary Shaping Based on Neural Network

2006 ◽  
Vol 532-533 ◽  
pp. 1044-1047
Author(s):  
Shi Hong Lu ◽  
Jing Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Fem Simulation ◽  
Back Propagation Neural Network ◽  
Small Error ◽  
Forming Process ◽  
The Neural Network ◽  
Metal Forming Process ◽  
Set Up ◽  
Good Agreement

Two-axle rotary shaping is one of advanced sheet metal forming process that combined stamping ascendant used elastic medium with traditional rotary shaping principle. The prediction model of two-axle rotary shaping is set up to predict the springback for two-axle rotary shaping. It used the back propagation neural network because of the better nonlinear mapping ability. Some of data from the experiment and FEM simulation is applied to train the network; the other data is used to test the prediction result. The result showed that the value of prediction and experiment is in good agreement, and just small error is existed. It demonstrated that the neural network model might predict the springback of two-axle rotary shaping and reduce the number of simulation calculation and experiment operation. It can offer a powerful guidance for rapid choice of process parameters in production.

Influence of Gene Effect on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

2011 ◽  
Vol 460-461 ◽  
pp. 335-340 ◽  
Author(s):  
Xue Bin Li ◽  
Xiao Ling Yu ◽  
Yun Rui Guo ◽  
Zhi Feng Xiang ◽  
Kun Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantitative Traits ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Simple Relation ◽  
Gene Effects ◽  
Gene Effect ◽  
Artificial Neural ◽  
The Stability ◽  
Hidden Neurons

Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.

scholarly journals Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: A case study of Junagadh, Gujarat, India

2019 ◽  
Vol 11 (1) ◽  
pp. 35-41 ◽  
Author(s):  
D. K. Dwivedi ◽  
J.H. Kelaiya ◽  
G. R. Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Back Propagation ◽  
Propagation Model ◽  
Monthly Rainfall ◽  
Hydrological Processes ◽  
Ann Model ◽  
Testing Data ◽  
Back Propagation Model

The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Time series modelling has been extensively used in stochastic hydrology for predicting various hydrological processes. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time lagged models extract maximum possible information from the available record for forecasting. Artificial neural network has been found to be effective in modelling hydrological processes which are stochastic in nature. The ARIMA model was used to simulate and forecast rainfall using its linear approach and the performance of the model was compared with ANN. The computational approach of ANN is inspired from nervous system of living beings and the neurons possess the parallel distribution processing nature. ANN has proven to be a reliable tool for modelling compared to conventional methods like ARIMA and therefore ANN has been used in this study to estimate rainfall. In this study, rainfall estimation of Junagadh has been attempted using monthly rainfall training data of 32 years (1980-2011) and testing data of 5 years (2012-2016). A number of ANN model structures were tested, and the appropriate ANN model was selected based on its performance measures like root mean square error and correlation coefficient. The correlation coefficient Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) on the testing data was found to be 0.75 and 0.79 respectively. Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) were used for forecasting rainfall of 5 years (2017-2021).

Suggestion for a Neural Network Model for Simulating Child Language Acquisition

1998 ◽  
Vol 21 (1) ◽  
pp. 47-64 ◽  
Author(s):  
Anneli Tikkala
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Language Acquisition ◽  
Neural Network Model ◽  
Child Language ◽  
Back Propagation ◽  
Propagation Model ◽  
Child Language Acquisition ◽  
Early Phases ◽  
Back Propagation Model

This paper explores the possibilities of modelling and simulating the early phases in child language acquisition using neural networks. A back-propagation model is proposed for language acquisition in a highly inflecting language, Finnish. Some preliminary tests for simulating the U-shaped behaviour of a child's language acquisition process have been performed.

scholarly journals Unbalance Rotor Parameters Detection Based on Artificial Neural Network

2019 ◽  
Vol 24 (No 1) ◽  
pp. 113-118
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Back Propagation ◽  
Propagation Model ◽  
Ann Model ◽  
Acceleration Signal ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Back Propagation Model

Unbalance is an important fault that can damage or shut down vital rotary systems such as the gas turbine, compressors, and others, so to avoid this trouble, the balancing process is very crucial, even though it is time-consuming and costly. Thus, having a technique which can predict the unbalance location and its parameters will be valuable and practical. The current study represents a model that can identify the unbalance’s mass, radius, and location of the eccentric mass based on the artificial neural network (ANN) model. The inputs of the proposed ANN, which is based on a feed forward with back propagation model, is the bearing acceleration signal in the frequency domain. It has 10 hidden layers with 10 neurons through each layer. The accuracy in prediction was acquired at 96%, 96%, and 94% for the disc number (plane), the eccentric radius, and eccentric mass values, respectively.

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