A novel data driven and feature based forecasting framework for wastewater optimization of network pressure management system
Publish place: International Journal of Industrial Engineering & Production Research، Vol: 31، Issue: 3
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
View: 230
This Paper With 11 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJIEPR-31-3_009
تاریخ نمایه سازی: 10 اسفند 1399
Abstract:
In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.
Keywords:
Authors
Pegah Rahimian
PhD Student, Budapest University of technology & economics, Telecommunication & Media Informatics, HSNLab
Sahand Behnam
CEO & Founder, Teleminer GmbH