Prediction of Calcium Yield During Calcium Treatment Process Performed in Steelmaking Using Neural Network

Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors—C, Si, Mn, Cr, quenching temperature, and tempering temperature—are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1–Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.

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A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽

10.1155/2019/2468189 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Zehua Huang ◽

Renren Wu ◽

XiaoHui Yi ◽

Hongbin Liu ◽

Jiannan Cai ◽

...

Keyword(s):

Neural Network ◽

Wastewater Treatment ◽

Treatment Process ◽

Biogas Production ◽

Anaerobic Treatment ◽

Production Rates ◽

Wastewater Treatment Process ◽

Effluent Quality ◽

Anaerobic Wastewater Treatment ◽

Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

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MPC of Sewage Treatment Process

Computer System Networking and Telecommunications ◽

10.18063/csnt.v3i1.1185 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Jiahui Meng ◽

Qingyuan Zhao ◽

Yu Zhang ◽

Guanglei Wen ◽

Huimin Ge ◽

...

Keyword(s):

Neural Network ◽

Treatment Process ◽

Sewage Treatment ◽

Sewage Treatment Plant ◽

Treatment Plant ◽

Weather Conditions ◽

Learning Ability ◽

Control Effect ◽

Control Goal ◽

Control Layer

Sewage treatment is one of the main methods to promote the recycling of water resources. The control goal of sewage treatment process is to reduce energy consumption under the premise that the effluent quality reaches the standard. In recent years, model predictive control (MPC) has attracted some attention in sewage treatment. Neural network is widely used in control field because of its strong online learning ability. BP neural network is selected as the prediction layer and control layer of MPC and applied to sewage treatment plant to realize on-line control of dissolved oxygen and nitrate. The training index of traditional neural network usually only selects the error between measured value and set value as the variable, and now the change of control quantity is also taken as the training index variable of control layer to adjust the weight relation between them to get the best control effect. Considering that different weather conditions will have a greater impact on the water inflow, different coefficients of the two can be selected to achieve better results in different weather.

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