scholarly journals Study on optimal conditions of flocculation in deinking wastewater treatment

2021 ◽  
Vol 68 (1) ◽  
Author(s):  
Ming Li ◽  
Kaitang Hu ◽  
Jin Wang
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Support Vector Regression ◽  
Fitness Function ◽  
Real Life ◽  
Support Vector ◽  
Optimal Conditions ◽  
Support Vector Regression Model ◽  
Hydraulic Conditions

AbstractFlocculation is an important method to treat paper manufacturing wastewater. Coagulants and flocculants added to wastewater facilitate the aggregation and sedimentation of various particles in the wastewater and lead to the formation of floc networks which can be easily removed using physical methods. The goal of this paper is to determine the optimal hydraulic conditions using machine learning in order to enable efficient flocculation and improve performance during the treatment of deinking wastewater. Experiments using polymerized aluminum chloride as flocculant to treat deinking wastewater were carried out. Based on the orthogonal array test, 16 different combinations of hydraulic conditions were chosen to investigate the performance of flocculation, which was indicated by the turbidity of the solution after treatment. To develop a model representing the relationship between the hydraulic conditions and the performance of wastewater treatment, the machine learning methods, support vector regression and Gaussian process regression, were compared, whereby the support vector regression method was chosen. According to the fitness function derived from the support vector regression model, a genetic algorithm was applied to evaluate the optimal hydraulic conditions. Based on the optimal conditions determined by the genetic algorithm and real-life experience, a set of hydraulic conditions were implemented experimentally. After treatment under higher stirring speed at 120 rpm for 1 min and lower stirring speed at 20 rpm for 5 min at a temperature of 20 °C, the turbidity of deinking wastewater was measured as 1 NTU. The turbidity reduction was as high as 99.6%, which indicated good performance of the deinking wastewater treatment.

scholarly journals A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

2019 ◽  
Vol 12 (1) ◽  
pp. 142 ◽  
Author(s):  
Charalampos Bratsas ◽  
Kleanthis Koupidis ◽  
Josep-Maria Salanova ◽  
Konstantinos Giannakopoulos ◽  
Aristeidis Kaloudis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Traffic Lights ◽  
Stable Conditions

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

scholarly journals A Zoning Earthquake Casualty Prediction Model Based on Machine Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Boyi Li ◽  
Adu Gong ◽  
Tingting Zeng ◽  
Wenxuan Bao ◽  
Can Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Historical Earthquakes ◽  
Support Vector ◽  
Learning Methods ◽  
Support Vector Regression Model ◽  
Machine Learning Methods ◽  
Earthquake Casualty

The evaluation of mortality in earthquake-stricken areas is vital for the emergency response during rescue operations. Hence, an effective and universal approach for accurately predicting the number of casualties due to an earthquake is needed. To obtain a precise casualty prediction method that can be applied to regions with different geographical environments, a spatial division method based on regional differences and a zoning casualty prediction method based on support vector regression (SVR) are proposed in this study. This study comprises three parts: (1) evaluating the importance of influential features on seismic fatality based on random forest to select indicators for the prediction model; (2) dividing the study area into different grades of risk zones with a strata fault line dataset and WorldPop population dataset; and (3) developing a zoning support vector regression model (Z-SVR) with optimal parameters that is suitable for different risk areas. We selected 30 historical earthquakes that occurred in China’s mainland from 1950 to 2017 to examine the prediction performance of Z-SVR and compared its performance with those of other widely used machine learning methods. The results show that Z-SVR outperformed the other machine learning methods and can further enhance the accuracy of casualty prediction.

GA-SVR Based Bearing Condition Degradation Prediction

2009 ◽  
Vol 413-414 ◽  
pp. 431-437 ◽  
Author(s):  
Fu Zhou Feng ◽  
Dong Dong Zhu ◽  
Peng Cheng Jiang ◽  
Hao Jiang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Main Idea ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Bench Test ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Vibration Data ◽  
Condition Vector

A genetic algorithm-support vector regression model (GA-SVR) is proposed for machine performance degradation prediction. The main idea of the method is firstly to select the condition-sensitive features extracted from rolling bearing vibration signals using Genetic Algorithm to form a condition vector. Then prediction model is established for each feature time series. And the third step is to establish support vector regression models to obtain prediction result in each series. Finally, the condition prognosis can be obtained through combing all components to form a condition vector. Vibration data from a rolling bearing bench test process are used to verify accuracy of the proposed method. The results show that the model is an effective prediction method with a higher speed and a better accuracy.

scholarly journals The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6654
Author(s):  
Stefano Villa ◽  
Claudio Sassanelli
Keyword(s):  
Machine Learning ◽  
Regression Model ◽  
Support Vector Regression ◽  
Regression Models ◽  
Absolute Error ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Prediction Horizon ◽  
Dynamic Estimation

Buildings are among the main protagonists of the world’s growing energy consumption, employing up to 45%. Wide efforts have been directed to improve energy saving and reduce environmental impacts to attempt to address the objectives fixed by policymakers in the past years. Meanwhile, new approaches using Machine Learning regression models surged in the modeling and simulation research context. This research develops and proposes an innovative data-driven black box predictive model for estimating in a dynamic way the interior temperature of a building. Therefore, the rationale behind the approach has been chosen based on two steps. First, an investigation of the extant literature on the methods to be considered for tests has been conducted, shrinking the field of investigation to non-recursive multi-step approaches. Second, the results obtained on a pilot case using various Machine Learning regression models in the multi-step approach have been assessed, leading to the choice of the Support Vector Regression model. The prediction mean absolute error on the pilot case is 0.1 ± 0.2 °C when the offset from the prediction instant is 15 min and grows slowly for further future instants, up to 0.3 ± 0.8 °C for a prediction horizon of 8 h. In the end, the advantages and limitations of the new data-driven multi-step approach based on the Support Vector Regression model are provided. Relying only on data related to external weather, interior temperature and calendar, the proposed approach is promising to be applicable to any type of building without needing as input specific geometrical/physical characteristics.

scholarly journals Application of genetic algorithm-support vector regression model to predict damping of cantilever beam with particle damper

2017 ◽  
Vol 36 (2) ◽  
pp. 138-147 ◽  
Author(s):  
Zhaowang Xia ◽  
Kaijie Mao ◽  
Shoubei Wei ◽  
Xuetao Wang ◽  
Yuanyuan Fang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Regression Model ◽  
Support Vector Regression ◽  
Cantilever Beam ◽  
Cross Validation ◽  
Damping Ratio ◽  
Predictive Ability ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Strongly Nonlinear

The performance of particle damper is strongly nonlinear, and the energy dissipation is derived from a combination of mechanisms including plastic collisions and friction between the particles and the walls and between the particles themselves. An optimized support vector regression model is built to predict the damping ratio of cantilever beam with particle damper. Then, the optimal parameters are adopted to construct the support vector regression models. In addition, genetic algorithm is used to select the optimal variables so as to improve the predictive ability of the models. Cross validation combined with support vector regression is used in this research and is compared with the genetic algorithm-support vector regression method. Genetic algorithm-support vector regression as research object to compare with the combination of cross validation and support vector regression. The experimental results demonstrate that the proposed genetic algorithm-support vector regression model provides better prediction capability. Therefore, the genetic algorithm-support vector regression model is proven to be an effective approach to predict the damping ratio of cantilever beam with particle damper.

scholarly journals Estimating the refractive index of oxygenated and deoxygenated hemoglobin using genetic algorithm – support vector regression model

2018 ◽  
Vol 163 ◽  
pp. 135-142 ◽  
Author(s):  
Ibrahim Olanrewaju Alade ◽  
Aliyu Bagudu ◽  
Tajudeen A. Oyehan ◽  
Mohd Amiruddin Abd Rahman ◽  
Tawfik A. Saleh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Refractive Index ◽  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Deoxygenated Hemoglobin

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

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