The Research of the Water Quality Prediction Model for the Circulating Cooling Water System

2013 ◽  
Vol 385-386 ◽  
pp. 408-411
Author(s):  
Qiang Gao ◽  
Tian Lu Ma ◽  
Jun Fang Li ◽  
Chen Guang Li
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Water System ◽  
Cooling Water ◽  
Water Quality Prediction ◽  
Network Training ◽  
Prediction Function ◽  
Circulating Cooling Water

Aiming at the common quality faults of scaling and corrosion in circulating cooling water, water quality index were often used to determine the scaling and corrosion of circulating cooling water quality trends. Prediction model of corrosion and scaling rate was built based on BP Neural Network in this paper. The optimal initial individuals were written into the network operating system to optimize the disadvantages of weights and thresholds in BP neural network based on genetic algorithm. The prediction function would output after the network training after comparison of predicted and actual values of the model. The performance of the actual situation was verified to match the model prediction.

scholarly journals Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

2022 ◽  
Vol 12 (2) ◽  
pp. 757
Author(s):  
Xiaofeng Wang ◽  
Baochang Liu ◽  
Jiaqi Yun ◽  
Xueqi Wang ◽  
Haoliang Bai
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Thermal Deformation ◽  
Heating Temperature ◽  
Cooling Water ◽  
Interference Fit ◽  
Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

2014 ◽  
Vol 912-914 ◽  
pp. 1407-1411 ◽  
Author(s):  
Jing Xin Yan ◽  
Li Juan Yu ◽  
Wen Wu Mao ◽  
Shou Qi Cao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Water Quality Monitoring ◽  
High Water ◽  
Eriocheir Sinensis ◽  
Quality Monitoring

Eriocheir sinensis should cultivate in high water quality ponds, which is affected by many combined factors such as physics, chemistry, biology etc. Using the real-time water quality monitoring historical data to test one of the water quality indexes and predict this index in the next time has great significance. The dissolved oxygen is one of the most important indexes in aquaculture, such as in the Eriocheir sinensis pond. This paper established a dissolved oxygen prediction model of water quality monitoring system based on BP neural network. The forecast data which is predicted by the established model could fit the actual monitoring data very well.

scholarly journals Research on Danjiang Water Quality Prediction Based on Improved Artificial Bee Colony Algorithm and Optimized BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jian’qiang He ◽  
Naian Liu ◽  
Mei’lin Han ◽  
Yao Chen
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Abc Algorithm ◽  
Bee Colony

In order to ensure “a river of clear water is supplied to Beijing and Tianjin” and improve the water quality prediction accuracy of the Danjiang water source, while avoiding the local optimum and premature maturity of the artificial bee colony algorithm, an improved artificial bee colony algorithm (ABC algorithm) is proposed to optimize the Danjiang water quality prediction model of BP neural network is proposed. This method improves the local and global search capabilities of the ABC algorithm by adding adaptive local search factors and mutation factors, improves the performance of local search, and avoids local optimal conditions. The improved ABC algorithm is used to optimize the weights and thresholds of the BP neural network to establish a water quality grade prediction model. Taking the water quality monitoring data of Danjiang source (Shangzhou section) from 2015 to 2019 as the research object, it is compared with GA-BP, PSO-BP, ABC-BP, and BP models. The research results show that the improved ABC-BP algorithm has the highest prediction accuracy, faster convergence speed, stronger stability, and robustness.

scholarly journals A prediction model of aquaculture water quality based on multiscale decomposition

2021 ◽  
Vol 18 (6) ◽  
pp. 7561-7579
Author(s):  
Huanhai Yang ◽  
◽  
Shue Liu ◽  
◽  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Short Term Memory ◽  
Normal Growth ◽  
Original Data ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Prediction ◽  
Aquaculture Water

<abstract><p>In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.</p></abstract>

scholarly journals Prediction of Surface Water Quality by Artificial Neural Network Model Using Probabilistic Weather Forecasting

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2392
Author(s):  
Woo Suk Jung ◽  
Sung Eun Kim ◽  
Young Do Kim
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Oxygen Demand ◽  
Meteorological Station ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Artificial Neural

We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model. The ANN-based water quality prediction model was constructed by reflecting the actual meteorological observation data and the water quality factors classified using an exploratory factor analysis (EFA) for each unit watershed in Nam River. To apply spatial refinement of meteorological factors for each unit watershed, we used the data of the Sancheong meteorological station for Namgang A and B, and the data of the Jinju meteorological station for Namgang C, D, and E. The predicted water quality variables were dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), total phosphorus (T-P), and suspended solids (SS). The ANN evaluation results reveal that the Namgang E unit watershed has a higher model accuracy than the other unit watersheds. Furthermore, compared with Namgang C and D, Namgang E has a high correlation with water quality due to meteorological effects. The results of this study will help establish a water quality forecasting system based on probabilistic weather forecasting in the long term.

Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 2598-2602 ◽  
Author(s):  
Yan Cong Li ◽  
Lian Hong Zhang ◽  
Chun Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Hydraulic Press ◽  
Important Indicator ◽  
Network Training ◽  
The Neural Network ◽  
Forecast Result

Workpiece’s precision is an important indicator of hydraulic press. In order to accurately predict the accuracy of the part, a method that combined the genetic algorithm and neural network is put out. Design of orthogonal experiment (DOE) is used to determine the input samples of neural network training and testing samples. The output samples are obtained by finite element analysed method (FEA). Through optimizing weights and thresholds of BP neural network using genetic algorithms, prediction model of workpiece’s precision is established. The established predict model overcomes the shortcomings of slow to convergence and easy to fall into the local minimum point of BP neural network model . By comparing the neural network forecast result with FEA ‘s results, it can be seen that the established prediction model has good fitting and generalization ability. So the model can be used to predict the workpiece’s precision.

Experimental Study of Corrosion-Proof Scale-Preventative Agent on 150MW Circulating Cooling Water System

2011 ◽  
Vol 71-78 ◽  
pp. 2665-2670
Author(s):  
Bing Yun Shen ◽  
Hui Pan ◽  
Jing Hou ◽  
Xiao Yu Guo ◽  
Xiang Dong Fan
Keyword(s):  
Water Quality ◽  
Simulation Experiment ◽  
Water System ◽  
Cooling Water ◽  
Quality Analysis ◽  
Scale Inhibitor ◽  
Water Quality Analysis ◽  
Circulating Cooling Water ◽  
Mixed Water ◽  
Water Ratio

This article aims at exploring the influence of different water quality system to the scaling corrosion, which based on the mixed of circulating cooling water and reserved water in different proportion. Then the mixed water as new circulating cooling water conducts dynamic simulation experiment and rotating piece experiments and then adds with a certain volume of scale inhibitor. This article’s purpose still lies in obtaining the best water ratio through the scaling and corrosion inhibition’s performance of three kinds of water quality analysis.

Sign in / Sign up

Export Citation Format

Share Document